modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-11 | 2023-10-12T04:40:17.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-11 | 0 | 2 | transformers | 2023-10-12T04:33:34 | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-11
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-11
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5150
- F1: 0.6875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 2.7550 | 0.6471 |
| No log | 2.0 | 68 | 1.8306 | 0.4800 |
| No log | 3.0 | 102 | 2.7923 | 0.5517 |
| No log | 4.0 | 136 | 2.6429 | 0.5714 |
| No log | 5.0 | 170 | 2.0721 | 0.6897 |
| No log | 6.0 | 204 | 2.5092 | 0.6207 |
| No log | 7.0 | 238 | 2.5177 | 0.6207 |
| No log | 8.0 | 272 | 2.2647 | 0.6207 |
| No log | 9.0 | 306 | 2.5039 | 0.6875 |
| No log | 10.0 | 340 | 2.5150 | 0.6875 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-12 | 2023-10-12T04:51:20.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-12 | 0 | 2 | transformers | 2023-10-12T04:44:34 | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-roberta-wwm-ext-wallstreetcn-morning-news-market-overview-SSE50-f1-12
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2086
- F1: 0.6452
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 3.6720 | 0.6667 |
| No log | 2.0 | 68 | 2.0234 | 0.7273 |
| No log | 3.0 | 102 | 2.0655 | 0.5185 |
| No log | 4.0 | 136 | 2.5897 | 0.625 |
| No log | 5.0 | 170 | 2.9827 | 0.625 |
| No log | 6.0 | 204 | 3.0976 | 0.5714 |
| No log | 7.0 | 238 | 3.4262 | 0.5714 |
| No log | 8.0 | 272 | 3.1772 | 0.6667 |
| No log | 9.0 | 306 | 3.2014 | 0.6452 |
| No log | 10.0 | 340 | 3.2086 | 0.6452 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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maybe1991/my_awesome_wnut_model | 2023-10-13T08:22:35.000Z | [
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | maybe1991 | null | null | maybe1991/my_awesome_wnut_model | 0 | 2 | transformers | 2023-10-12T06:15:10 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: maybe1991/my_awesome_wnut_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# maybe1991/my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1183
- Validation Loss: 0.2623
- Train Precision: 0.5900
- Train Recall: 0.4354
- Train F1: 0.5010
- Train Accuracy: 0.9472
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.3558 | 0.3094 | 0.4248 | 0.1722 | 0.2451 | 0.9324 | 0 |
| 0.1596 | 0.2725 | 0.5469 | 0.3768 | 0.4462 | 0.9435 | 1 |
| 0.1183 | 0.2623 | 0.5900 | 0.4354 | 0.5010 | 0.9472 | 2 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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Ketak-ZoomRx/drugs_model_v1_pythia | 2023-10-12T07:34:12.000Z | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"text-generation-inference",
"region:us"
] | text-generation | Ketak-ZoomRx | null | null | Ketak-ZoomRx/drugs_model_v1_pythia | 0 | 2 | transformers | 2023-10-12T07:33:32 | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Ketak-ZoomRx/drugs_model_v1_pythia",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Ketak-ZoomRx/drugs_model_v1_pythia",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Ketak-ZoomRx/drugs_model_v1_pythia",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Ketak-ZoomRx/drugs_model_v1_pythia" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | 7,769 | [
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RogerB/afro-xlmr-large-kin-tweets-senti-finetuned | 2023-10-12T12:00:59.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | RogerB | null | null | RogerB/afro-xlmr-large-kin-tweets-senti-finetuned | 0 | 2 | transformers | 2023-10-12T08:39:47 | ---
license: mit
base_model: RogerB/afro-xlmr-large-kinte-domain-kinte-task
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: afro-xlmr-large-kinte-domain-kinte-task-unkin-sent4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# afro-xlmr-large-kinte-domain-kinte-task-unkin-sent4
This model is a fine-tuned version of [RogerB/afro-xlmr-large-kinte-domain-kinte-task](https://huggingface.co/RogerB/afro-xlmr-large-kinte-domain-kinte-task) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7807
- F1: 0.7086
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 49751346
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8815 | 1.0 | 1013 | 0.6436 | 0.7355 |
| 0.728 | 2.0 | 2026 | 0.5010 | 0.8138 |
| 0.6103 | 3.0 | 3039 | 0.5141 | 0.8301 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Tiabet/Tiabet-TFkoGPT-complete_story-epoch-3 | 2023-10-15T15:04:42.000Z | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Tiabet | null | null | Tiabet/Tiabet-TFkoGPT-complete_story-epoch-3 | 0 | 2 | transformers | 2023-10-12T09:13:45 | ---
license: cc-by-nc-sa-4.0
base_model: skt/kogpt2-base-v2
tags:
- generated_from_keras_callback
model-index:
- name: Tiabet-TFkoGPT-complete_story-epoch-3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Tiabet-TFkoGPT-complete_story-epoch-3
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Tokenizers 0.14.1
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Tommert25/multibert_1210seed25 | 2023-10-12T10:45:09.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/multibert_1210seed25 | 0 | 2 | transformers | 2023-10-12T10:37:04 | ---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: multibert_1210seed25
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multibert_1210seed25
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4453
- Precisions: 0.8647
- Recall: 0.8314
- F-measure: 0.8459
- Accuracy: 0.9141
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 25
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.6013 | 1.0 | 236 | 0.4080 | 0.8974 | 0.6857 | 0.7273 | 0.8736 |
| 0.319 | 2.0 | 472 | 0.3621 | 0.8338 | 0.7306 | 0.7317 | 0.8875 |
| 0.1929 | 3.0 | 708 | 0.3823 | 0.8020 | 0.7680 | 0.7761 | 0.9022 |
| 0.1389 | 4.0 | 944 | 0.4353 | 0.8400 | 0.7742 | 0.7990 | 0.9003 |
| 0.0958 | 5.0 | 1180 | 0.4348 | 0.8726 | 0.7547 | 0.7971 | 0.9011 |
| 0.0676 | 6.0 | 1416 | 0.4453 | 0.8647 | 0.8314 | 0.8459 | 0.9141 |
| 0.0506 | 7.0 | 1652 | 0.5222 | 0.8555 | 0.8013 | 0.8253 | 0.9100 |
| 0.0315 | 8.0 | 1888 | 0.5192 | 0.8700 | 0.7873 | 0.8187 | 0.9108 |
| 0.0229 | 9.0 | 2124 | 0.5977 | 0.8402 | 0.7839 | 0.8079 | 0.9062 |
| 0.0149 | 10.0 | 2360 | 0.6061 | 0.8622 | 0.8069 | 0.8305 | 0.9131 |
| 0.0122 | 11.0 | 2596 | 0.5894 | 0.8419 | 0.7702 | 0.7983 | 0.9085 |
| 0.0065 | 12.0 | 2832 | 0.6120 | 0.8514 | 0.7700 | 0.8021 | 0.9089 |
| 0.0039 | 13.0 | 3068 | 0.6434 | 0.8437 | 0.7646 | 0.7965 | 0.9055 |
| 0.003 | 14.0 | 3304 | 0.6391 | 0.8403 | 0.7670 | 0.7973 | 0.9062 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Tommert25/multibert_1210seed24 | 2023-10-12T10:59:56.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/multibert_1210seed24 | 0 | 2 | transformers | 2023-10-12T10:48:38 | ---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: multibert_1210seed24
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multibert_1210seed24
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6397
- Precisions: 0.8875
- Recall: 0.7915
- F-measure: 0.8255
- Accuracy: 0.9112
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.5949 | 1.0 | 236 | 0.4396 | 0.8425 | 0.6484 | 0.6768 | 0.8569 |
| 0.3352 | 2.0 | 472 | 0.4132 | 0.7836 | 0.7344 | 0.7453 | 0.8862 |
| 0.2148 | 3.0 | 708 | 0.3528 | 0.8396 | 0.7759 | 0.8020 | 0.8985 |
| 0.1389 | 4.0 | 944 | 0.4093 | 0.8386 | 0.7431 | 0.7775 | 0.8931 |
| 0.099 | 5.0 | 1180 | 0.4169 | 0.8501 | 0.7998 | 0.8200 | 0.9022 |
| 0.078 | 6.0 | 1416 | 0.4629 | 0.7912 | 0.7756 | 0.7815 | 0.8900 |
| 0.0536 | 7.0 | 1652 | 0.4658 | 0.8394 | 0.8096 | 0.8235 | 0.9098 |
| 0.0316 | 8.0 | 1888 | 0.5609 | 0.8440 | 0.7790 | 0.8044 | 0.9019 |
| 0.0217 | 9.0 | 2124 | 0.5870 | 0.8686 | 0.7814 | 0.8128 | 0.9055 |
| 0.0126 | 10.0 | 2360 | 0.5636 | 0.8613 | 0.7997 | 0.8255 | 0.9059 |
| 0.0115 | 11.0 | 2596 | 0.5978 | 0.8721 | 0.7964 | 0.8232 | 0.9093 |
| 0.0082 | 12.0 | 2832 | 0.6072 | 0.8645 | 0.7904 | 0.8184 | 0.9098 |
| 0.0042 | 13.0 | 3068 | 0.6332 | 0.8801 | 0.7903 | 0.8230 | 0.9104 |
| 0.0033 | 14.0 | 3304 | 0.6397 | 0.8875 | 0.7915 | 0.8255 | 0.9112 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Tommert25/multibert_1210seed85 | 2023-10-12T11:58:37.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/multibert_1210seed85 | 0 | 2 | transformers | 2023-10-12T11:50:49 | ---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: multibert_1210seed85
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# multibert_1210seed85
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4043
- Precisions: 0.8689
- Recall: 0.8339
- F-measure: 0.8498
- Accuracy: 0.9067
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 85
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.5968 | 1.0 | 236 | 0.4307 | 0.8843 | 0.6749 | 0.7165 | 0.8690 |
| 0.3238 | 2.0 | 472 | 0.3849 | 0.8827 | 0.7215 | 0.7489 | 0.8916 |
| 0.2021 | 3.0 | 708 | 0.4067 | 0.8540 | 0.7640 | 0.7969 | 0.8983 |
| 0.1335 | 4.0 | 944 | 0.3857 | 0.8227 | 0.8002 | 0.8071 | 0.8983 |
| 0.0886 | 5.0 | 1180 | 0.4043 | 0.8689 | 0.8339 | 0.8498 | 0.9067 |
| 0.0654 | 6.0 | 1416 | 0.4734 | 0.8847 | 0.8016 | 0.8359 | 0.9089 |
| 0.0451 | 7.0 | 1652 | 0.5312 | 0.8215 | 0.7826 | 0.7996 | 0.8980 |
| 0.031 | 8.0 | 1888 | 0.5520 | 0.8730 | 0.7873 | 0.8222 | 0.9074 |
| 0.0248 | 9.0 | 2124 | 0.4954 | 0.8896 | 0.8145 | 0.8454 | 0.9149 |
| 0.0149 | 10.0 | 2360 | 0.5595 | 0.8717 | 0.8101 | 0.8354 | 0.9104 |
| 0.0086 | 11.0 | 2596 | 0.5703 | 0.8814 | 0.8051 | 0.8348 | 0.9112 |
| 0.0061 | 12.0 | 2832 | 0.5855 | 0.8655 | 0.8138 | 0.8356 | 0.9103 |
| 0.006 | 13.0 | 3068 | 0.6068 | 0.8578 | 0.8137 | 0.8329 | 0.9105 |
| 0.0039 | 14.0 | 3304 | 0.6147 | 0.8656 | 0.8129 | 0.8356 | 0.9112 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Tommert25/multibert_1210seed7 | 2023-10-12T12:09:16.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/multibert_1210seed7 | 0 | 2 | transformers | 2023-10-12T12:00:40 | ---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: multibert_1210seed7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multibert_1210seed7
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5019
- Precisions: 0.8874
- Recall: 0.7790
- F-measure: 0.8105
- Accuracy: 0.9107
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 7
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.6032 | 1.0 | 236 | 0.4733 | 0.8608 | 0.6507 | 0.6853 | 0.8645 |
| 0.3527 | 2.0 | 472 | 0.3790 | 0.8098 | 0.7259 | 0.7383 | 0.8826 |
| 0.2198 | 3.0 | 708 | 0.4191 | 0.8209 | 0.7632 | 0.7816 | 0.8936 |
| 0.1359 | 4.0 | 944 | 0.4433 | 0.8430 | 0.7344 | 0.7590 | 0.8924 |
| 0.0862 | 5.0 | 1180 | 0.5207 | 0.8067 | 0.7697 | 0.7838 | 0.8947 |
| 0.0637 | 6.0 | 1416 | 0.5019 | 0.8874 | 0.7790 | 0.8105 | 0.9107 |
| 0.0454 | 7.0 | 1652 | 0.5048 | 0.8049 | 0.8135 | 0.8070 | 0.9058 |
| 0.0318 | 8.0 | 1888 | 0.5969 | 0.8135 | 0.7710 | 0.7845 | 0.9003 |
| 0.024 | 9.0 | 2124 | 0.6388 | 0.8295 | 0.7999 | 0.8057 | 0.9048 |
| 0.0138 | 10.0 | 2360 | 0.6448 | 0.8304 | 0.7727 | 0.7949 | 0.9033 |
| 0.0084 | 11.0 | 2596 | 0.6589 | 0.8216 | 0.7756 | 0.7936 | 0.9017 |
| 0.0091 | 12.0 | 2832 | 0.6471 | 0.8340 | 0.7683 | 0.7952 | 0.9045 |
| 0.005 | 13.0 | 3068 | 0.6817 | 0.8600 | 0.7662 | 0.8034 | 0.9073 |
| 0.0045 | 14.0 | 3304 | 0.6774 | 0.8397 | 0.7680 | 0.7976 | 0.9077 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Reangsy/my_awesome_billsum_model | 2023-10-12T13:21:19.000Z | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Reangsy | null | null | Reangsy/my_awesome_billsum_model | 0 | 2 | transformers | 2023-10-12T12:42:18 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: Reangsy/my_awesome_billsum_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Reangsy/my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.6650
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 4.6650 | 0 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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mie-zhz/distilbert-base-uncased-finetuned-imdb | 2023-10-12T13:06:22.000Z | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | mie-zhz | null | null | mie-zhz/distilbert-base-uncased-finetuned-imdb | 0 | 2 | transformers | 2023-10-12T12:53:54 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3824
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5243 | 1.0 | 157 | 2.4131 |
| 2.4809 | 2.0 | 314 | 2.3958 |
| 2.4597 | 3.0 | 471 | 2.3732 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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anyuanay/my_finetuned_wnut_model_1012 | 2023-10-12T14:13:46.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | anyuanay | null | null | anyuanay/my_finetuned_wnut_model_1012 | 0 | 2 | transformers | 2023-10-12T14:06:02 | ---
license: mit
base_model: dslim/bert-base-NER
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_finetuned_wnut_model_1012
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5479274611398963
- name: Recall
type: recall
value: 0.39202965708989806
- name: F1
type: f1
value: 0.45705024311183146
- name: Accuracy
type: accuracy
value: 0.9487047961015646
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_finetuned_wnut_model_1012
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2940
- Precision: 0.5479
- Recall: 0.3920
- F1: 0.4571
- Accuracy: 0.9487
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2657 | 0.5157 | 0.3967 | 0.4484 | 0.9468 |
| No log | 2.0 | 426 | 0.2940 | 0.5479 | 0.3920 | 0.4571 | 0.9487 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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TheBloke/FashionGPT-70B-v1.2-GGUF | 2023-10-12T18:02:05.000Z | [
"transformers",
"llama",
"license:llama2",
"text-generation-inference",
"region:us"
] | null | TheBloke | null | null | TheBloke/FashionGPT-70B-v1.2-GGUF | 5 | 2 | transformers | 2023-10-12T15:31:35 | ---
base_model: ICBU-NPU/FashionGPT-70B-V1.2
inference: false
license: llama2
model_creator: ICBU-NPU
model_name: Fashiongpt 70B v1.2
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- header start -->
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Fashiongpt 70B v1.2 - GGUF
- Model creator: [ICBU-NPU](https://huggingface.co/ICBU-NPU)
- Original model: [Fashiongpt 70B v1.2](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.2)
<!-- description start -->
## Description
This repo contains GGUF format model files for [ICBU-NPU's Fashiongpt 70B v1.2](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.2).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF)
* [ICBU-NPU's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [fashiongpt-70b-v1.2.Q2_K.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [fashiongpt-70b-v1.2.Q3_K_S.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [fashiongpt-70b-v1.2.Q3_K_M.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [fashiongpt-70b-v1.2.Q3_K_L.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [fashiongpt-70b-v1.2.Q4_0.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [fashiongpt-70b-v1.2.Q4_K_S.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [fashiongpt-70b-v1.2.Q4_K_M.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [fashiongpt-70b-v1.2.Q5_0.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [fashiongpt-70b-v1.2.Q5_K_S.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [fashiongpt-70b-v1.2.Q5_K_M.gguf](https://huggingface.co/TheBloke/FashionGPT-70B-v1.2-GGUF/blob/main/fashiongpt-70b-v1.2.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| fashiongpt-70b-v1.2.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
| fashiongpt-70b-v1.2.Q8_0.gguf | Q8_0 | 8 | 73.29 GB| 75.79 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `fashiongpt-70b-v1.2.Q6_K.gguf-split-a`
* `fashiongpt-70b-v1.2.Q6_K.gguf-split-b`
### q8_0
Please download:
* `fashiongpt-70b-v1.2.Q8_0.gguf-split-a`
* `fashiongpt-70b-v1.2.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat fashiongpt-70b-v1.2.Q6_K.gguf-split-* > fashiongpt-70b-v1.2.Q6_K.gguf && rm fashiongpt-70b-v1.2.Q6_K.gguf-split-*
cat fashiongpt-70b-v1.2.Q8_0.gguf-split-* > fashiongpt-70b-v1.2.Q8_0.gguf && rm fashiongpt-70b-v1.2.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B fashiongpt-70b-v1.2.Q6_K.gguf-split-a + fashiongpt-70b-v1.2.Q6_K.gguf-split-b fashiongpt-70b-v1.2.Q6_K.gguf
del fashiongpt-70b-v1.2.Q6_K.gguf-split-a fashiongpt-70b-v1.2.Q6_K.gguf-split-b
COPY /B fashiongpt-70b-v1.2.Q8_0.gguf-split-a + fashiongpt-70b-v1.2.Q8_0.gguf-split-b fashiongpt-70b-v1.2.Q8_0.gguf
del fashiongpt-70b-v1.2.Q8_0.gguf-split-a fashiongpt-70b-v1.2.Q8_0.gguf-split-b
```
</details>
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/FashionGPT-70B-v1.2-GGUF and below it, a specific filename to download, such as: fashiongpt-70b-v1.2.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/FashionGPT-70B-v1.2-GGUF fashiongpt-70b-v1.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/FashionGPT-70B-v1.2-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/FashionGPT-70B-v1.2-GGUF fashiongpt-70b-v1.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m fashiongpt-70b-v1.2.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/FashionGPT-70B-v1.2-GGUF", model_file="fashiongpt-70b-v1.2.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: ICBU-NPU's Fashiongpt 70B v1.2
<!-- original-model-card end -->
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raghavsharma06/results | 2023-10-22T20:45:57.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | raghavsharma06 | null | null | raghavsharma06/results | 0 | 2 | transformers | 2023-10-12T16:18:32 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
astrid01052/test-lora-lima-220 | 2023-10-12T18:02:28.000Z | [
"peft",
"region:us"
] | null | astrid01052 | null | null | astrid01052/test-lora-lima-220 | 0 | 2 | peft | 2023-10-12T17:59:21 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
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golightly/whisper-in-Hindi | 2023-10-12T19:31:32.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | golightly | null | null | golightly/whisper-in-Hindi | 0 | 2 | transformers | 2023-10-12T18:05:08 | ---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.2884
- eval_wer: 93.3886
- eval_runtime: 2745.3694
- eval_samples_per_second: 1.054
- eval_steps_per_second: 0.132
- epoch: 0.0
- step: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
ahuang11/mistral-ft-lumen | 2023-10-12T20:40:17.000Z | [
"peft",
"region:us"
] | null | ahuang11 | null | null | ahuang11/mistral-ft-lumen | 0 | 2 | peft | 2023-10-12T20:39:58 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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vladjr/t5-base-teste2 | 2023-10-12T21:58:45.000Z | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | vladjr | null | null | vladjr/t5-base-teste2 | 0 | 2 | transformers | 2023-10-12T21:18:24 | ---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_keras_callback
model-index:
- name: vladjr/t5-base-teste2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vladjr/t5-base-teste2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0136
- Validation Loss: 0.0140
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 6720, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.4667 | 0.0272 | 0 |
| 0.0536 | 0.0178 | 1 |
| 0.0351 | 0.0191 | 2 |
| 0.0260 | 0.0163 | 3 |
| 0.0205 | 0.0146 | 4 |
| 0.0165 | 0.0145 | 5 |
| 0.0152 | 0.0145 | 6 |
| 0.0136 | 0.0140 | 7 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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asmallgreenpotato/falcon-7b-ft-general1-adapters | 2023-10-13T00:03:59.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | asmallgreenpotato | null | null | asmallgreenpotato/falcon-7b-ft-general1-adapters | 0 | 2 | peft | 2023-10-13T00:03:45 | ---
library_name: peft
base_model: tiiuae/falcon-7b
---
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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## Glossary [optional]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
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giantkylin/my_eli5_clm-model | 2023-10-26T14:01:52.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | giantkylin | null | null | giantkylin/my_eli5_clm-model | 0 | 2 | transformers | 2023-10-13T01:03:55 | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: my_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8641 | 1.0 | 1103 | 3.7760 |
| 3.7704 | 2.0 | 2206 | 3.7635 |
| 3.7314 | 3.0 | 3309 | 3.7619 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 2023-10-13T01:55:03.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v1 | 0 | 2 | transformers | 2023-10-13T01:38:52 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v1
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3800
- F1: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 0.6889 | 0.6154 |
| No log | 2.0 | 136 | 0.6617 | 0.0 |
| No log | 3.0 | 204 | 0.5459 | 0.5263 |
| No log | 4.0 | 272 | 0.5617 | 0.5 |
| No log | 5.0 | 340 | 0.5251 | 0.6667 |
| No log | 6.0 | 408 | 0.4182 | 0.7586 |
| No log | 7.0 | 476 | 0.9859 | 0.6667 |
| 0.5907 | 8.0 | 544 | 1.3002 | 0.6957 |
| 0.5907 | 9.0 | 612 | 1.2383 | 0.6667 |
| 0.5907 | 10.0 | 680 | 1.3800 | 0.6667 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
Vezora/Mistral-Narwhal-7b-SafeTensor | 2023-10-13T03:14:08.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Vezora | null | null | Vezora/Mistral-Narwhal-7b-SafeTensor | 0 | 2 | transformers | 2023-10-13T01:41:57 | ---
license: apache-2.0
---
# Narwhal-Mistral-7b
## Model Description
Mistral-Narwhal-7b is a Hugging Face model built on top of Mistral 7b. It is a result of merging two models: Eric Hartford's Dolphin2.1 and HuggingFace's Zephyr-7b-alpha. All credit goes to them.
## Source Models
- Dolphin2.1-mistral-7b by Eric Hartford (https://huggingface.co/ehartford/dolphin-2.1-mistral-7b)
- Zephyr-7b-alpha by HuggingFace (https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)
## Usage
This model uses 3 different models in combination, so you must adhere to their Lisencing, as well as the lisencing available here.
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 2023-10-13T02:19:15.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v2 | 0 | 2 | transformers | 2023-10-13T02:02:29 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v2
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9002
- F1: 0.7407
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 1.0453 | 0.6087 |
| No log | 2.0 | 136 | 1.4334 | 0.6667 |
| No log | 3.0 | 204 | 2.0460 | 0.6154 |
| No log | 4.0 | 272 | 1.5248 | 0.6957 |
| No log | 5.0 | 340 | 2.4518 | 0.625 |
| No log | 6.0 | 408 | 1.8801 | 0.7143 |
| No log | 7.0 | 476 | 2.0821 | 0.7333 |
| 0.2154 | 8.0 | 544 | 1.7219 | 0.6667 |
| 0.2154 | 9.0 | 612 | 1.9040 | 0.7407 |
| 0.2154 | 10.0 | 680 | 1.9002 | 0.7407 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v3 | 2023-10-13T02:44:05.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v3 | 0 | 2 | transformers | 2023-10-13T02:26:52 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v3
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1970
- F1: 0.5926
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 2.6956 | 0.6207 |
| No log | 2.0 | 136 | 2.3070 | 0.6364 |
| No log | 3.0 | 204 | 2.9120 | 0.6429 |
| No log | 4.0 | 272 | 2.3987 | 0.6897 |
| No log | 5.0 | 340 | 3.0729 | 0.5600 |
| No log | 6.0 | 408 | 2.7170 | 0.6154 |
| No log | 7.0 | 476 | 2.7256 | 0.5833 |
| 0.0631 | 8.0 | 544 | 3.1654 | 0.5926 |
| 0.0631 | 9.0 | 612 | 3.1896 | 0.5926 |
| 0.0631 | 10.0 | 680 | 3.1970 | 0.5926 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 2023-10-13T03:08:58.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v4 | 0 | 2 | transformers | 2023-10-13T02:51:42 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v4
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5191
- F1: 0.7143
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 2.4376 | 0.5926 |
| No log | 2.0 | 136 | 2.5659 | 0.64 |
| No log | 3.0 | 204 | 2.8116 | 0.5833 |
| No log | 4.0 | 272 | 3.4625 | 0.6154 |
| No log | 5.0 | 340 | 2.6318 | 0.64 |
| No log | 6.0 | 408 | 2.3575 | 0.64 |
| No log | 7.0 | 476 | 2.5060 | 0.7200 |
| 0.0646 | 8.0 | 544 | 2.7379 | 0.5455 |
| 0.0646 | 9.0 | 612 | 2.5176 | 0.7143 |
| 0.0646 | 10.0 | 680 | 2.5191 | 0.7143 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
AA051610/H1L | 2023-10-13T08:29:29.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:gpl-3.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | AA051610 | null | null | AA051610/H1L | 0 | 2 | transformers | 2023-10-13T03:00:48 | ---
license: gpl-3.0
---
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 2023-10-13T03:33:29.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v5 | 0 | 2 | transformers | 2023-10-13T03:16:33 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v5
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8836
- F1: 0.5455
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 3.6036 | 0.6875 |
| No log | 2.0 | 136 | 3.7574 | 0.5714 |
| No log | 3.0 | 204 | 3.7837 | 0.5217 |
| No log | 4.0 | 272 | 3.6457 | 0.4615 |
| No log | 5.0 | 340 | 2.6183 | 0.5000 |
| No log | 6.0 | 408 | 3.2188 | 0.4545 |
| No log | 7.0 | 476 | 2.6074 | 0.64 |
| 0.0726 | 8.0 | 544 | 3.2742 | 0.4762 |
| 0.0726 | 9.0 | 612 | 2.9602 | 0.5455 |
| 0.0726 | 10.0 | 680 | 2.8836 | 0.5455 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
Rewcifer/teamyellow-llama1-7B-lora-5pct-1ktoken | 2023-10-13T03:20:12.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | Rewcifer | null | null | Rewcifer/teamyellow-llama1-7B-lora-5pct-1ktoken | 0 | 2 | peft | 2023-10-13T03:20:08 | ---
library_name: peft
base_model: decapoda-research/llama-7b-hf
---
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
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LikelySurf/FreeLabSeminar_MammoLLM_Kyungmin | 2023-10-13T03:45:05.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LikelySurf | null | null | LikelySurf/FreeLabSeminar_MammoLLM_Kyungmin | 0 | 2 | transformers | 2023-10-13T03:33:49 | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: FreeLabSeminar_MammoLLM_Kyungmin
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# FreeLabSeminar_MammoLLM_Kyungmin
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9386
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 192
- eval_batch_size: 192
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 768
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.8558 | 0.74 | 5 | 4.9386 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.13.1
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 2023-10-13T03:57:57.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v6 | 0 | 2 | transformers | 2023-10-13T03:41:06 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v6
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1257
- F1: 0.5217
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 4.7671 | 0.5185 |
| No log | 2.0 | 136 | 4.2600 | 0.5333 |
| No log | 3.0 | 204 | 3.5360 | 0.5926 |
| No log | 4.0 | 272 | 2.9121 | 0.6364 |
| No log | 5.0 | 340 | 3.0757 | 0.5833 |
| No log | 6.0 | 408 | 3.9310 | 0.4444 |
| No log | 7.0 | 476 | 4.1382 | 0.4762 |
| 0.0463 | 8.0 | 544 | 4.0637 | 0.5 |
| 0.0463 | 9.0 | 612 | 4.2119 | 0.5217 |
| 0.0463 | 10.0 | 680 | 4.1257 | 0.5217 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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sgeorge31/setfit-ethos-multilabel-example | 2023-10-13T03:57:06.000Z | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | sgeorge31 | null | null | sgeorge31/setfit-ethos-multilabel-example | 0 | 2 | sentence-transformers | 2023-10-13T03:56:48 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# sgeorge31/setfit-ethos-multilabel-example
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("sgeorge31/setfit-ethos-multilabel-example")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 2023-10-13T04:23:03.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v7 | 0 | 2 | transformers | 2023-10-13T04:05:34 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v7
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8497
- F1: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 4.2201 | 0.5600 |
| No log | 2.0 | 136 | 3.2610 | 0.3 |
| No log | 3.0 | 204 | 3.2933 | 0.5185 |
| No log | 4.0 | 272 | 2.7472 | 0.5000 |
| No log | 5.0 | 340 | 2.4131 | 0.6923 |
| No log | 6.0 | 408 | 2.4668 | 0.6667 |
| No log | 7.0 | 476 | 2.3010 | 0.64 |
| 0.0665 | 8.0 | 544 | 2.7031 | 0.5833 |
| 0.0665 | 9.0 | 612 | 2.8595 | 0.6667 |
| 0.0665 | 10.0 | 680 | 2.8497 | 0.6667 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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dainis-boumber/df-adapters-twitter-rumours | 2023-10-13T06:26:06.000Z | [
"adapter-transformers",
"bert",
"dataset:redasers/difraud",
"region:us"
] | null | dainis-boumber | null | null | dainis-boumber/df-adapters-twitter-rumours | 0 | 2 | adapter-transformers | 2023-10-13T04:14:22 | ---
tags:
- bert
- adapter-transformers
datasets:
- redasers/difraud
---
# Adapter `dainis-boumber/df-adapters-twitter-rumours` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [redasers/difraud](https://huggingface.co/datasets/redasers/difraud/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("bert-base-uncased")
adapter_name = model.load_adapter("dainis-boumber/df-adapters-twitter-rumours", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | 1,227 | [
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 2023-10-13T04:47:56.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v8 | 0 | 2 | transformers | 2023-10-13T04:30:43 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v8
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0139
- F1: 0.5926
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 3.3484 | 0.5556 |
| No log | 2.0 | 136 | 3.1730 | 0.5600 |
| No log | 3.0 | 204 | 4.6814 | 0.6471 |
| No log | 4.0 | 272 | 2.2215 | 0.6667 |
| No log | 5.0 | 340 | 3.0883 | 0.6207 |
| No log | 6.0 | 408 | 3.2254 | 0.6207 |
| No log | 7.0 | 476 | 3.0645 | 0.6429 |
| 0.0463 | 8.0 | 544 | 2.5887 | 0.6957 |
| 0.0463 | 9.0 | 612 | 2.8651 | 0.6154 |
| 0.0463 | 10.0 | 680 | 3.0139 | 0.5926 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 2023-10-13T05:12:06.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v9 | 0 | 2 | transformers | 2023-10-13T04:55:17 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-v9
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4354
- F1: 0.5600
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 4.7259 | 0.6875 |
| No log | 2.0 | 136 | 3.9377 | 0.6207 |
| No log | 3.0 | 204 | 3.0016 | 0.7143 |
| No log | 4.0 | 272 | 3.4055 | 0.6667 |
| No log | 5.0 | 340 | 2.0258 | 0.7200 |
| No log | 6.0 | 408 | 2.7715 | 0.6429 |
| No log | 7.0 | 476 | 3.5727 | 0.6667 |
| 0.0461 | 8.0 | 544 | 2.6897 | 0.6364 |
| 0.0461 | 9.0 | 612 | 3.0299 | 0.5833 |
| 0.0461 | 10.0 | 680 | 3.4354 | 0.5600 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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IHaBiS/Mistral-11B-OmniMix-bf16-4.125bpw-h8-exl2 | 2023-10-13T06:17:56.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | IHaBiS | null | null | IHaBiS/Mistral-11B-OmniMix-bf16-4.125bpw-h8-exl2 | 0 | 2 | transformers | 2023-10-13T06:02:27 | ---
license: cc-by-nc-4.0
---
exl2 version of [NeverSleep/Mistral-11B-OmniMix-bf16](https://huggingface.co/NeverSleep/Mistral-11B-OmniMix-bf16)
used dataset : [wikitext](https://huggingface.co/datasets/wikitext)
quantized by IHaBiS
command : python convert.py -i models/NeverSleep_Mistral-11B-OmniMix-bf16 -o NeverSleep_Mistral-11B-OmniMix-bf16-temp -cf NeverSleep_Mistral-11B-OmniMix-bf16-4.125bpw-h8-exl2 -c 0000.parquet -l 4096 -b 4.125 -hb 8 -ss 4096
Below this sentence is original model card
This model should be fixed, it was MEANT to be BF16.
Don't mind this one at the moment, I need to finetune it for RP, it's just a test.
## Description
This repo contains fp16 files of Mistral-11B-OmniMix-bf16.
My goal for this model was only to make it score the highest possible with merge and layer toying, proving that:
- Benchmark are objective
- You should try a model yourself and don't go blindly to the highest rated one
- Merge/Layer toying CAN be usable to do better model (maybe?)
## Model used
- [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
- [Mistral-7B-v0.1-Open-Platypus](akjindal53244/Mistral-7B-v0.1-Open-Platypus)
- [CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B)
- [zephyr-7b-alpha](HuggingFaceH4/zephyr-7b-alpha)
## Prompt template: Alpaca or default
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
```
USER: <prompt>
ASSISTANT:
```
Or use any prompting system from one of the 4 source model, should work.
## The secret sauce
Mistral-11B-OpenOrcaPlatypus :
```
slices:
- sources:
- model: Open-Orca/Mistral-7B-OpenOrca
layer_range: [0, 24]
- sources:
- model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
Mistral-11B-CC-Zephyr :
```
slices:
- sources:
- model: "/content/drive/MyDrive/CC-v1.1-7B-bf16"
layer_range: [0, 24]
- sources:
- model: "/content/drive/MyDrive/Zephyr-7B"
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
Mistral-11B-OmniMix :
```
slices:
- sources:
- model: Mistral-11B-OpenOrcaPlatypus
layer_range: [0, 48]
- model: Mistral-11B-CC-Zephyr
layer_range: [0, 48]
merge_method: slerp
base_model: Mistral-11B-OpenOrcaPlatypus
parameters:
t:
- filter: lm_head
value: [0.75]
- filter: embed_tokens
value: [0.75]
- filter: self_attn
value: [0.75, 0.25]
- filter: mlp
value: [0.25, 0.75]
- filter: layernorm
value: [0.5, 0.5]
- filter: modelnorm
value: [0.75]
- value: 0.5 # fallback for rest of tensors
dtype: bfloat16
```
I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here.
## Some scoring I done myself
hf-causal-experimental (pretrained=/content/drive/MyDrive/Mistral-11B-OmniMix-bf16), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5580|± |0.0145|
| | |acc_norm|0.5819|± |0.0144|
|arc_easy | 0|acc |0.8300|± |0.0077|
| | |acc_norm|0.8211|± |0.0079|
|hellaswag | 0|acc |0.6372|± |0.0048|
| | |acc_norm|0.8209|± |0.0038|
|piqa | 0|acc |0.8145|± |0.0091|
| | |acc_norm|0.8286|± |0.0088|
|truthfulqa_mc| 1|mc1 |0.3978|± |0.0171|
| | |mc2 |0.5680|± |0.0155|
|winogrande | 0|acc |0.7427|± |0.0123|
## Others
Special thanks to Sushi, [Henky](https://github.com/KoboldAI/KoboldAI-Client) for the machine he give me for big task, and [Charles Goddard](https://github.com/cg123) for his amazing tool.
If you want to support me, you can [here](https://ko-fi.com/undiai).
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] |
TheBloke/speechless-code-mistral-7B-v1.0-GGUF | 2023-10-13T06:17:08.000Z | [
"transformers",
"mistral",
"llama-2",
"code",
"text-generation",
"en",
"dataset:jondurbin/airoboros-2.2",
"dataset:Open-Orca/OpenOrca",
"dataset:garage-bAInd/Open-Platypus",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"dataset:TokenBender/python_eval_instruct_51k",
"license:llama2",
"model-index",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/speechless-code-mistral-7B-v1.0-GGUF | 4 | 2 | transformers | 2023-10-13T06:07:41 | ---
base_model: uukuguy/speechless-code-mistral-7b-v1.0
datasets:
- jondurbin/airoboros-2.2
- Open-Orca/OpenOrca
- garage-bAInd/Open-Platypus
- WizardLM/WizardLM_evol_instruct_V2_196k
- TokenBender/python_eval_instruct_51k
inference: false
language:
- en
library_name: transformers
license: llama2
model-index:
- name: SpeechlessCoder
results:
- dataset:
name: HumanEval
type: openai_humaneval
metrics:
- name: pass@1
type: pass@1
value: 0.0
verified: false
task:
type: text-generation
model_creator: Jiangwen Su
model_name: Speechless Code Mistral 7B v1.0
model_type: mistral
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
tags:
- llama-2
- code
---
<!-- header start -->
<!-- 200823 -->
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Speechless Code Mistral 7B v1.0 - GGUF
- Model creator: [Jiangwen Su](https://huggingface.co/uukuguy)
- Original model: [Speechless Code Mistral 7B v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jiangwen Su's Speechless Code Mistral 7B v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF)
* [Jiangwen Su's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [speechless-code-mistral-7b-v1.0.Q2_K.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [speechless-code-mistral-7b-v1.0.Q3_K_S.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [speechless-code-mistral-7b-v1.0.Q3_K_M.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [speechless-code-mistral-7b-v1.0.Q3_K_L.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [speechless-code-mistral-7b-v1.0.Q4_0.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [speechless-code-mistral-7b-v1.0.Q4_K_S.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [speechless-code-mistral-7b-v1.0.Q4_K_M.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [speechless-code-mistral-7b-v1.0.Q5_0.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [speechless-code-mistral-7b-v1.0.Q5_K_S.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [speechless-code-mistral-7b-v1.0.Q5_K_M.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [speechless-code-mistral-7b-v1.0.Q6_K.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [speechless-code-mistral-7b-v1.0.Q8_0.gguf](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF/blob/main/speechless-code-mistral-7b-v1.0.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/speechless-code-mistral-7B-v1.0-GGUF and below it, a specific filename to download, such as: speechless-code-mistral-7b-v1.0.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/speechless-code-mistral-7B-v1.0-GGUF speechless-code-mistral-7b-v1.0.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/speechless-code-mistral-7B-v1.0-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/speechless-code-mistral-7B-v1.0-GGUF speechless-code-mistral-7b-v1.0.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m speechless-code-mistral-7b-v1.0.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/speechless-code-mistral-7B-v1.0-GGUF", model_file="speechless-code-mistral-7b-v1.0.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
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**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
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<!-- original-model-card start -->
# Original model card: Jiangwen Su's Speechless Code Mistral 7B v1.0
<p><h1> speechless-code-mistral-7b-v1.0 </h1></p>
Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities.
Total 201,981 samples.
- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
- garage-bAInd/Open-Platypus: 100%, 24,926 samples.
- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
- TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
- Spider: 8,659 samples
| | |
|------ | ------ |
| lr | 2e-4 |
| lr_scheduler_type | cosine |
| weight_decay | 0.0 |
| optim | paged_adamw_8bit |
| flash_attention | True |
| rerope | False |
| max_new_tokens | 4096 |
| num_train_epochs | 2 |
| bits | 4 |
| lora_r | 64 |
| lora_alpha | 16 |
| lora_dropout | 0.05 |
| double_quant | True |
| quant_type | nf4 |
| dataset_format | airoboros |
| mini_batch_size | 2 |
| grandient_accumulation_steps | 32 |
| bf16 | True |
A40-48G x 2
| | |
|------ | ------ |
| epoch | 2.0 |
| etrain_loss | 0.5 |
| etrain_runtime | 1 day, 10:25:26.77 |
| etrain_samples_per_second | 3.194 |
| etrain_steps_per_second | 0.025 |
| eeval_loss | 0.5146 |
| eeval_runtime | 0:00:25.04 |
| eeval_samples_per_second | 7.985 |
| eeval_steps_per_second | |
| Metric | Value |
| --- | --- |
| humaneval-python ||
[Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard)
CodeLlama-34B-Python: 53.29
CodeLlama-34B-Instruct: 50.79
CodeLlama-13B-Instruct: 50.6
CodeLlama-34B: 45.11
CodeLlama-13B-Python: 42.89
CodeLlama-13B: 35.07
[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| Metric | Value |
| --- | --- |
| ARC | |
| HellaSwag | |
| MMLU | |
| TruthfulQA | |
| Average | |
<!-- original-model-card end -->
| 19,671 | [
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] |
yesj1234/mbart-mmt_7.4p_en-ko | 2023-10-13T06:27:08.000Z | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"generated_from_trainer",
"en",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | yesj1234 | null | null | yesj1234/mbart-mmt_7.4p_en-ko | 0 | 2 | transformers | 2023-10-13T06:17:10 | ---
language:
- en
- ko
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: tst-translation-output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tst-translation-output
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5319
- Bleu: 11.2991
- Gen Len: 16.268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.4354 | 1.12 | 1500 | 1.5319 | 11.2991 | 16.268 |
| 0.9158 | 2.24 | 3000 | 1.5989 | 11.9085 | 16.3331 |
| 0.4823 | 3.37 | 4500 | 1.7407 | 11.2452 | 15.771 |
| 0.3005 | 4.49 | 6000 | 1.8923 | 11.0872 | 16.3668 |
| 0.1969 | 5.61 | 7500 | 2.0164 | 11.449 | 15.8694 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
AA051610/H2L | 2023-10-13T17:59:04.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:gpl",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | AA051610 | null | null | AA051610/H2L | 0 | 2 | transformers | 2023-10-13T09:06:07 | ---
license: gpl
---
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[More Information Needed] | 5,242 | [
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Tommert25/multibert_1310seed7 | 2023-10-13T09:51:39.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Tommert25 | null | null | Tommert25/multibert_1310seed7 | 0 | 2 | transformers | 2023-10-13T09:28:03 | ---
license: apache-2.0
base_model: bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: multibert_1310seed7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multibert_1310seed7
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4338
- Precisions: 0.8841
- Recall: 0.8144
- F-measure: 0.8437
- Accuracy: 0.9402
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 7
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.4441 | 1.0 | 236 | 0.2809 | 0.8700 | 0.7020 | 0.7222 | 0.9118 |
| 0.2161 | 2.0 | 472 | 0.2575 | 0.8741 | 0.7653 | 0.7818 | 0.9250 |
| 0.1277 | 3.0 | 708 | 0.2644 | 0.8331 | 0.8115 | 0.8175 | 0.9299 |
| 0.0891 | 4.0 | 944 | 0.2614 | 0.8671 | 0.8120 | 0.8341 | 0.9390 |
| 0.0559 | 5.0 | 1180 | 0.3259 | 0.8806 | 0.7923 | 0.8279 | 0.9332 |
| 0.0322 | 6.0 | 1416 | 0.3770 | 0.8807 | 0.8064 | 0.8333 | 0.9373 |
| 0.0241 | 7.0 | 1652 | 0.4548 | 0.8430 | 0.8213 | 0.8223 | 0.9323 |
| 0.0162 | 8.0 | 1888 | 0.3705 | 0.8493 | 0.8239 | 0.8343 | 0.9405 |
| 0.0099 | 9.0 | 2124 | 0.4498 | 0.8463 | 0.8094 | 0.8245 | 0.9369 |
| 0.0069 | 10.0 | 2360 | 0.4445 | 0.8606 | 0.8141 | 0.8328 | 0.9381 |
| 0.0062 | 11.0 | 2596 | 0.4429 | 0.8880 | 0.8075 | 0.8405 | 0.9383 |
| 0.0045 | 12.0 | 2832 | 0.4496 | 0.8794 | 0.8017 | 0.8322 | 0.9393 |
| 0.0041 | 13.0 | 3068 | 0.4338 | 0.8841 | 0.8144 | 0.8437 | 0.9402 |
| 0.0029 | 14.0 | 3304 | 0.4401 | 0.8850 | 0.8135 | 0.8437 | 0.9400 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hmbert/flair-hipe-2022-ajmc-fr | 2023-10-17T23:15:01.000Z | [
"flair",
"token-classification",
"sequence-tagger-model",
"fr",
"license:mit",
"region:us"
] | token-classification | hmbert | null | null | hmbert/flair-hipe-2022-ajmc-fr | 0 | 2 | flair | 2023-10-13T11:09:55 | ---
language: fr
license: mit
tags:
- flair
- token-classification
- sequence-tagger-model
base_model: dbmdz/bert-base-historic-multilingual-cased
widget:
- text: — 469 . Πεδία . Les tribraques formés par un seul mot sont rares chez les
tragiques , partont ailleurs qu ’ au premier pied . CÉ . cependant QEd , Roi ,
719 , 826 , 4496 .
---
# Fine-tuned Flair Model on AjMC French NER Dataset (HIPE-2022)
This Flair model was fine-tuned on the
[AjMC French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)
NER Dataset using hmBERT as backbone LM.
The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics,
and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/)
project.
The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`.
# Results
We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:
* Batch Sizes: `[8, 4]`
* Learning Rates: `[3e-05, 5e-05]`
And report micro F1-score on development set:
| Configuration | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg. |
|-----------------|--------------|--------------|--------------|--------------|--------------|--------------|
| bs4-e10-lr5e-05 | [0.8436][1] | [0.8287][2] | [0.8475][3] | [0.8455][4] | [0.8553][5] | 84.41 ± 0.87 |
| bs8-e10-lr3e-05 | [0.8228][6] | [0.8407][7] | [0.8557][8] | [0.8532][9] | [0.8385][10] | 84.22 ± 1.18 |
| bs4-e10-lr3e-05 | [0.8202][11] | [0.8519][12] | [0.8434][13] | [0.8418][14] | [0.8436][15] | 84.02 ± 1.06 |
| bs8-e10-lr5e-05 | [0.8333][16] | [0.8338][17] | [0.8394][18] | [0.8409][19] | [0.8504][20] | 83.96 ± 0.62 |
[1]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
[2]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
[3]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
[4]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
[5]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
[6]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
[7]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
[8]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
[9]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
[10]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
[11]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
[12]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
[13]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
[14]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
[15]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
[16]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
[17]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
[18]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
[19]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
[20]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub.
More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench).
# Acknowledgements
We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and
[Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models.
Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
Many Thanks for providing access to the TPUs ❤️
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IHaBiS/PetrolLM-CollectiveCognition-4.125bpw-h8-exl2 | 2023-10-13T15:31:06.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"dataset:Squish42/bluemoon-fandom-1-1-rp-cleaned",
"dataset:OpenLeecher/Teatime",
"dataset:PygmalionAI/PIPPA",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | IHaBiS | null | null | IHaBiS/PetrolLM-CollectiveCognition-4.125bpw-h8-exl2 | 0 | 2 | transformers | 2023-10-13T12:58:28 | ---
datasets:
- Squish42/bluemoon-fandom-1-1-rp-cleaned
- OpenLeecher/Teatime
- PygmalionAI/PIPPA
tags:
- not-for-all-audiences
- nsfw
license: cc-by-nc-4.0
---
exl2 version of [Norquinal/PetrolLM-CollectiveCognition](https://huggingface.co/Norquinal/PetrolLM-CollectiveCognition)
used dataset : [wikitext](https://huggingface.co/datasets/wikitext)
quantized by IHaBiS
command : python convert.py -i models/Norquinal_PetrolLM-CollectiveCognition -o Norquinal_PetrolLM-CollectiveCognition-temp -cf Norquinal_PetrolLM-CollectiveCognition-4.125bpw-h8-exl2 -c 0000.parquet -l 4096 -b 4.125 -hb 8 -ss 4096 -m Norquinal_PetrolLM-CollectiveCognition_measurement.json
Below this sentence is original model card
## What is PetrolLM-Claude-Chat?
PetrolLM-Claude-Chat is the [CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B) model with the [PetrolLoRA](https://huggingface.co/Norquinal/PetrolLoRA) applied.
The dataset (for the LoRA) consists of 2800 samples, with the composition as follows:
* AICG Logs (~34%)
* PygmalionAI/PIPPA (~33%)
* Squish42/bluemoon-fandom-1-1-rp-cleaned (~29%)
* OpenLeecher/Teatime (~4%)
These samples were then back-filled using gpt-4/gpt-3.5-turbo-16k or otherwise converted to fit the prompt format.
## Prompt Format
The model uses the following prompt format:
```
---
style: roleplay
characters:
[char]: [description]
summary: [scenario]
---
<chat_history>
Format:
[char]: [message]
Human: [message]
```
## Use in Text Generation Web UI
Install the bleeding-edge version of `transformers` from source:
```
pip install git+https://github.com/huggingface/transformers
```
Or, alternatively, change `model_type` in `config.json` from `mistral` to `llama`.
## Use in SillyTavern UI
As an addendum, you can include one of the following as the `Last Output Sequence`:
```
Human: In your next reply, write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.
{{char}}:
```
```
{{char}} (2 paragraphs, engaging, natural, authentic, descriptive, creative):
```
```
[System note: Write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.]
{{char}}:
```
The third one seems to work the best. I would recommend experimenting with creating your own to best suit your needs. | 2,467 | [
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Mathoctopus/Parallel_7B | 2023-11-02T07:29:00.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"es",
"zh",
"de",
"ru",
"th",
"sw",
"ja",
"fr",
"bn",
"dataset:Mathoctopus/GSM8KInstruct_Parallel",
"arxiv:2310.20246",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Mathoctopus | null | null | Mathoctopus/Parallel_7B | 1 | 2 | transformers | 2023-10-13T13:27:21 | ---
license: apache-2.0
datasets:
- Mathoctopus/GSM8KInstruct_Parallel
language:
- en
- es
- zh
- de
- ru
- th
- sw
- ja
- fr
- bn
---
# 🐙 Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
Project Page: [https://mathoctopus.github.io/](https://mathoctopus.github.io/)
Paper: [https://arxiv.org/abs/2310.20246.pdf](https://arxiv.org/abs/2310.20246.pdf)
Code: [https://github.com/microsoft/MathOctopus](https://github.com/microsoft/MathOctopus)
### Introduction
We introduce 🐙 MathOctopus, a series of open-source large language models (LLMs) specifically tailored for multilingual math problem-solving. The MathOctopus models are trained on 🤗 MGSM8KInstruct Dataset, encompassing ten distinct languages.
MathOctopus notably outperforms conventional open-source LLMs and exhibits superiority over ChatGPT in few-shot scenarios.
### Datasets
#### **MGSM8KInstruct**
| Training Dataset | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:----------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MGSM8KInstruct | 7473 | 7472 | 7466 | 6539 | 7466 | 7470 | 7469 | 7471 | 7361 | 7473 | **73.6K** |
#### **MSVAMP**
| Test Dataset | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:----------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MSVAMP | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | **10K** |
#### Usage
Our dataset and models are all available at Huggingface.
🤗 [MGSM8KInstruct_Parallel Dataset](https://huggingface.co/datasets/Mathoctopus/GSM8KInstruct_Parallel)
🤗 [MGSM8KInstruct_Cross Dataset](https://huggingface.co/datasets/Mathoctopus/MGSM8KInstruct_Cross)
🤗 [MSVAMP Dataset](https://huggingface.co/datasets/Mathoctopus/MSVAMP)
## Models
| Base Model: LLama | Parallel-Training | Cross-Training |
|----|---------------------------------------------------------------|---------------------------------------------------------------------------|
| 7B-LLaMA 2 | 🐙 [MathOctopus-Parallel-7B](https://huggingface.co/Mathoctopus/Parallel_7B) | 🐙 [MathOctopus-Cross-7B](https://huggingface.co/Mathoctopus/Cross_7B) |
|| 🐙[MathOctopus-Parallel-xRFT-7B](https://huggingface.co/Mathoctopus/Parallel_xRFT_7B)|🐙[MathOctopus-Cross-xRFT-7B](https://huggingface.co/Mathoctopus/Cross_xRFT_7B)|
| 13B-LLaMA 2 | 🐙 [MathOctopus-Parallel-13B](https://huggingface.co/Mathoctopus/Parallel_13B) | 🐙 [MathOctopus-Cross-13B](https://huggingface.co/Mathoctopus/Cross_13B) |
|| 🐙[MathOctopus-Parallel-xRFT-13B](https://huggingface.co/Mathoctopus/Parallel_xRFT_13B)|🐙[MathOctopus-Cross-xRFT-13B]|
| 33B-LLaMA 1 | 🐙 [MathOctopus-Parallel-33B](https://huggingface.co/Mathoctopus/Parallel_33B) | 🐙 [MathOctopus-Cross-33B] |
| 70B-LLaMA 2 | Coming soon! | Coming Soon! |
*-Parallel refers to our model trained with the parallel-training strategy.
*-Cross refers to our model trained with cross-training strategy.
*-xRFT means we train the model with multilingual rejection sampling.
### **Overall Results on MGSM**
| 7B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 52.0 | 23.6 | 31.6 | 18.8 | 38.0 | 39.2 | 36.4 | 27.2 | 33.6 | 21.6 | 32.2 |
| **xRFT**-MathOctopus<sup>C</sup>| 51.2 | 24.0 | 33.2 | 18.8 | 36.0 | 41.2 | 37.6 | 29.6 | 36.4 | 25.2 | 33.3 |
| MathOctopus<sup>P</sup>-LoRA | 30.4 | 15.2 | 23.6 | 10.4 | 22.8 | 24.8 | 26.4 | 18.0 | 22.0 | 14.8 | 20.8 |
| MathOctopus<sup>P</sup> | 52.4 | 39.2 | 38.4 | 28.8 | 44.8 | 42.4 | 43.6 | 36.0 | 39.6 | 34.4 | 40.0 |
| **xRFT**-MathOctopus<sup>P</sup>| 54.8 | 38.4 | 45.2 | 33.2 | 43.6 | 45.2 | 38.0 | 35.6 | 48.4 | 36.4 | 41.9 |
<p></p >
| 13B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 56.4 | 27.2 | 39.2 | 24.0 | 47.6 | 49.6 | 47.6 | 40.4 | 42.0 | 24.8 | 39.9 |
| **xRFT**-MathOctopus<sup>C</sup>| 53.6 | 28.0 | 45.2 | 21.2 | 48.0 | 46.4 | 46.0 | 35.2 | 45.6 | 28.8 | 39.8 |
| MathOctopus<sup>P</sup> | 53.2 | 42.8 | 48.8 | 35.2 | 44.4 | 48.0 | 48.4 | 43.2 | 47.6 | 46.8 | 45.8 |
| **xRFT**-MathOctopus<sup>P</sup>| 51.6 | 46.0 | 51.2 | 42.0 | 49.2 | 53.2 | 49.6 | 39.6 | 47.6 | 46.0 | 47.6 |
<p></p >
| 30-34B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 55.6 | 24.4 | 36.0 | 19.2 | 40.4 | 51.2 | 44.4 | 27.2 | 37.2 | 21.6 | 35.7 |
| **xRFT**-MathOctopus<sup>C</sup>| 53.6 | 27.6 | 34.4 | 19.2 | 47.2 | 47.6 | 44.8 | 30.8 | 38.8 | 22.8 | 36.7 |
| MathOctopus<sup>P</sup> | 56.4 | 46.8 | 52.0 | 35.2 | 47.2 | 53.2 | 48.0 | 39.2 | 45.6 | 41.2 | 46.5 |
| **xRFT**-MathOctopus<sup>P</sup>| 51.6 | 47.2 | 52.4 | 37.6 | 51.2 | 52.8 | 44.4 | 41.6 | 50.0 | 47.6 | 47.6 |
### **Overall Results on MSVAMP**
| 7B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 49.2 | 36.6 | 43.6 | 30.2 | 48.6 | 46.8 | 46.4 | 42.5 | 46.7 | 34.0 | 42.5 |
| **xRFT**-MathOctopus<sup>C</sup>| 49.9 | 37.7 | 43.3 | 32.9 | 46.5 | 47.6 | 47.3 | 42.7 | 46.6 | 36.2 | 43.1 |
| MathOctopus<sup>P</sup>-LoRA | 30.4 | 15.2 | 23.6 | 10.4 | 22.8 | 24.8 | 26.4 | 18.0 | 22.0 | 14.8 | 20.8 |
| MathOctopus<sup>P</sup> | 46.5 | 40.1 | 42.5 | 29.1 | 43.5 | 45.4 | 46.0 | 42.5 | 45.4 | 35.7 | 41.7 |
| **xRFT**-MathOctopus<sup>P</sup>| 46.8 | 42.3 | 43.2 | 32.8 | 43.1 | 44.5 | 45.3 | 43.2 | 42.1 | 40.5 | 42.4 |
<p></p >
| 13B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 56.6 | 40.4 | 49.0 | 30.3 | 50.9 | 54.2 | 54.7 | 46.3 | 52.4 | 35.7 | 47.1 |
| **xRFT**-MathOctopus<sup>C</sup>| 52.9 | 41.9 | 49.2 | 34.1 | 50.5 | 52.8 | 51.5 | 45.8 | 50.2 | 35.7 | 46.5 |
| MathOctopus<sup>P</sup> | 50.7 | 43.4 | 42.6 | 31.8 | 48.4 | 49.4 | 50.6 | 41.1 | 46.9 | 39.3 | 44.4 |
| **xRFT**-MathOctopus<sup>P</sup>| 44.6 | 43.4 | 46.4 | 34.2 | 47.7 | 48.2 | 49.9 | 43.1 | 48.2 | 39.5 | 44.5 |
<p></p >
| 30-34B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 51.5 | 42.1 | 46.2 | 23.2 | 50.5 | 52.1 | 52.9 | 42.2 | 50.5 | 33.4 | 44.5 |
| **xRFT**-MathOctopus<sup>C</sup>| 48.1 | 42.8 | 43.6 | 23.3 | 48.7 | 50.0 | 48.9 | 43.4 | 44.6 | 35.5 | 42.9 |
| MathOctopus<sup>P</sup> | 56.4 | 46.8 | 52.0 | 35.2 | 47.2 | 53.2 | 48.0 | 39.2 | 45.6 | 41.2 | 46.5 |
| **xRFT**-MathOctopus<sup>P</sup>| 48.0 | 42.3 | 46.1 | 36.2 | 47.5 | 48.5 | 48.3 | 45.8 | 47.2 | 41.2 | 45.1 |
### **MathOctopus in English**
| Models | GSM8K | SVAMP |
|:--------------------------------|:--------|:--------|
| LLaMA 2-7B | 42.4 | 38.3 |
| MathOctopus<sup>P</sup>-7B | 49.3 | 46.8 |
| MathOctopus<sup>C</sup>-7B | 50.8 | 49.3 |
| LLaMA 2-13B | 51.0 | 50.9 |
| MathOctopus<sup>P</sup>-13B | 55.5 | 52.1 |
| MathOctopus<sup>C</sup>-13B | 56.6 | 56.6 |
| LLaMA 1-33B | 50.0 | 49.0 |
| MathOctopus<sup>P</sup>-33B | 56.0 | 52.5 |
| MathOctopus<sup>C</sup>-33B | 53.7 | 51.5 |
## Intended Uses
These models are trained for research purposes. They are designed to solve multilingual math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed.
## Citation
Please cite our paper if you use our data, model or code. Please also kindly cite the original dataset papers.
```
@misc{chen2023breaking,
title={Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations},
author={Nuo Chen and Zinan Zheng and Ning Wu and Linjun Shou and Ming Gong and Yangqiu Song and Dongmei Zhang and Jia Li},
year={2023},
eprint={2310.20246},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 10,504 | [
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] |
IHaBiS/PetrolLM-CollectiveCognition-6bpw-h8-exl2 | 2023-10-13T15:31:45.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"dataset:Squish42/bluemoon-fandom-1-1-rp-cleaned",
"dataset:OpenLeecher/Teatime",
"dataset:PygmalionAI/PIPPA",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | IHaBiS | null | null | IHaBiS/PetrolLM-CollectiveCognition-6bpw-h8-exl2 | 0 | 2 | transformers | 2023-10-13T13:47:59 | ---
datasets:
- Squish42/bluemoon-fandom-1-1-rp-cleaned
- OpenLeecher/Teatime
- PygmalionAI/PIPPA
tags:
- not-for-all-audiences
- nsfw
license: cc-by-nc-4.0
---
exl2 version of [Norquinal/PetrolLM-CollectiveCognition](https://huggingface.co/Norquinal/PetrolLM-CollectiveCognition)
used dataset : [wikitext](https://huggingface.co/datasets/wikitext)
quantized by IHaBiS
command : python convert.py -i models/Norquinal_PetrolLM-CollectiveCognition -o Norquinal_PetrolLM-CollectiveCognition-temp -cf Norquinal_PetrolLM-CollectiveCognition-6bpw-h8-exl2 -c 0000.parquet -l 4096 -b 6 -hb 8 -ss 4096 -m Norquinal_PetrolLM-CollectiveCognition_measurement.json
Below this sentence is original model card
## What is PetrolLM-Claude-Chat?
PetrolLM-Claude-Chat is the [CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B) model with the [PetrolLoRA](https://huggingface.co/Norquinal/PetrolLoRA) applied.
The dataset (for the LoRA) consists of 2800 samples, with the composition as follows:
* AICG Logs (~34%)
* PygmalionAI/PIPPA (~33%)
* Squish42/bluemoon-fandom-1-1-rp-cleaned (~29%)
* OpenLeecher/Teatime (~4%)
These samples were then back-filled using gpt-4/gpt-3.5-turbo-16k or otherwise converted to fit the prompt format.
## Prompt Format
The model uses the following prompt format:
```
---
style: roleplay
characters:
[char]: [description]
summary: [scenario]
---
<chat_history>
Format:
[char]: [message]
Human: [message]
```
## Use in Text Generation Web UI
Install the bleeding-edge version of `transformers` from source:
```
pip install git+https://github.com/huggingface/transformers
```
Or, alternatively, change `model_type` in `config.json` from `mistral` to `llama`.
## Use in SillyTavern UI
As an addendum, you can include one of the following as the `Last Output Sequence`:
```
Human: In your next reply, write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.
{{char}}:
```
```
{{char}} (2 paragraphs, engaging, natural, authentic, descriptive, creative):
```
```
[System note: Write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.]
{{char}}:
```
The third one seems to work the best. I would recommend experimenting with creating your own to best suit your needs. | 2,459 | [
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dima806/full_flat_tyre_image_detection | 2023-10-13T14:16:46.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | dima806 | null | null | dima806/full_flat_tyre_image_detection | 0 | 2 | transformers | 2023-10-13T14:14:04 | ---
license: apache-2.0
metrics:
- accuracy
- f1
---
See https://www.kaggle.com/code/dima806/full-flat-tyre-image-detection-vit for more details. | 145 | [
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intanm/bert-base-multilingual-cased-idkmrc | 2023-10-13T15:16:38.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | intanm | null | null | intanm/bert-base-multilingual-cased-idkmrc | 0 | 2 | transformers | 2023-10-13T15:04:51 | ---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-multilingual-cased-idkmrc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-idkmrc
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9374
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4806 | 1.0 | 584 | 0.9536 |
| 0.7576 | 2.0 | 1168 | 0.8814 |
| 0.5407 | 3.0 | 1752 | 0.9374 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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intanm/bert-base-multilingual-cased-clickbaitspoiling | 2023-10-13T15:39:28.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | intanm | null | null | intanm/bert-base-multilingual-cased-clickbaitspoiling | 0 | 2 | transformers | 2023-10-13T15:21:01 | ---
license: apache-2.0
base_model: intanm/bert-base-multilingual-cased-idkmrc
tags:
- generated_from_trainer
model-index:
- name: bert-base-multilingual-cased-clickbaitspoiling
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-clickbaitspoiling
This model is a fine-tuned version of [intanm/bert-base-multilingual-cased-idkmrc](https://huggingface.co/intanm/bert-base-multilingual-cased-idkmrc) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9379
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 200 | 3.0339 |
| No log | 2.0 | 400 | 2.9536 |
| 2.7795 | 3.0 | 600 | 3.2096 |
| 2.7795 | 4.0 | 800 | 3.3790 |
| 1.3376 | 5.0 | 1000 | 3.7804 |
| 1.3376 | 6.0 | 1200 | 4.2252 |
| 1.3376 | 7.0 | 1400 | 4.4965 |
| 0.5455 | 8.0 | 1600 | 4.7341 |
| 0.5455 | 9.0 | 1800 | 4.9777 |
| 0.285 | 10.0 | 2000 | 4.9379 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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LoneStriker/speechless-code-mistral-7b-v1.0-3.0bpw-h6-exl2 | 2023-10-13T15:48:22.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"llama-2",
"code",
"en",
"dataset:jondurbin/airoboros-2.2",
"dataset:Open-Orca/OpenOrca",
"dataset:garage-bAInd/Open-Platypus",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"dataset:TokenBender/python_eval_instruct_51k",
"license:llama2",
"model-index",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/speechless-code-mistral-7b-v1.0-3.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-13T15:46:05 | ---
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- jondurbin/airoboros-2.2
- Open-Orca/OpenOrca
- garage-bAInd/Open-Platypus
- WizardLM/WizardLM_evol_instruct_V2_196k
- TokenBender/python_eval_instruct_51k
tags:
- llama-2
- code
license: llama2
model-index:
- name: SpeechlessCoder
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 50.0
verified: false
---
<p><h1> speechless-code-mistral-7b-v1.0 </h1></p>
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF)
Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities.
Total 201,981 samples.
- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
- garage-bAInd/Open-Platypus: 100%, 24,926 samples.
- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
- TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
- Spider: 8,659 samples
## HumanEval
| Metric | Value |
| --- | --- |
| humaneval-python | 50.0|
[Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard)
CodeLlama-34B-Python: 53.29
CodeLlama-34B-Instruct: 50.79
CodeLlama-13B-Instruct: 50.6
CodeLlama-34B: 45.11
CodeLlama-13B-Python: 42.89
CodeLlama-13B: 35.07
## lm-evaluation-harness
[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| Metric | Value |
| --- | --- |
| ARC |59.64 |
| HellaSwag |82.25 |
| MMLU | 61.33 |
| TruthfulQA | 48.45 |
| Average | 62.92 |
## Parameters
| | |
|------ | ------ |
| lr | 2e-4 |
| lr_scheduler_type | cosine |
| weight_decay | 0.0 |
| optim | paged_adamw_8bit |
| flash_attention | True |
| rerope | False |
| max_new_tokens | 4096 |
| num_train_epochs | 2 |
| bits | 4 |
| lora_r | 64 |
| lora_alpha | 16 |
| lora_dropout | 0.05 |
| double_quant | True |
| quant_type | nf4 |
| dataset_format | airoboros |
| mini_batch_size | 2 |
| grandient_accumulation_steps | 32 |
| bf16 | True |
A40-48G x 2
| | |
|------ | ------ |
| epoch | 2.0 |
| etrain_loss | 0.5 |
| etrain_runtime | 1 day, 10:25:26.77 |
| etrain_samples_per_second | 3.194 |
| etrain_steps_per_second | 0.025 |
| eeval_loss | 0.5146 |
| eeval_runtime | 0:00:25.04 |
| eeval_samples_per_second | 7.985 |
| eeval_steps_per_second | |
| 3,043 | [
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LoneStriker/speechless-code-mistral-7b-v1.0-8.0bpw-h6-exl2 | 2023-10-13T15:49:00.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"llama-2",
"code",
"en",
"dataset:jondurbin/airoboros-2.2",
"dataset:Open-Orca/OpenOrca",
"dataset:garage-bAInd/Open-Platypus",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"dataset:TokenBender/python_eval_instruct_51k",
"license:llama2",
"model-index",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/speechless-code-mistral-7b-v1.0-8.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-13T15:46:22 | ---
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- jondurbin/airoboros-2.2
- Open-Orca/OpenOrca
- garage-bAInd/Open-Platypus
- WizardLM/WizardLM_evol_instruct_V2_196k
- TokenBender/python_eval_instruct_51k
tags:
- llama-2
- code
license: llama2
model-index:
- name: SpeechlessCoder
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 50.0
verified: false
---
<p><h1> speechless-code-mistral-7b-v1.0 </h1></p>
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/speechless-code-mistral-7B-v1.0-GGUF)
Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities.
Total 201,981 samples.
- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
- garage-bAInd/Open-Platypus: 100%, 24,926 samples.
- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
- TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
- Spider: 8,659 samples
## HumanEval
| Metric | Value |
| --- | --- |
| humaneval-python | 50.0|
[Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard)
CodeLlama-34B-Python: 53.29
CodeLlama-34B-Instruct: 50.79
CodeLlama-13B-Instruct: 50.6
CodeLlama-34B: 45.11
CodeLlama-13B-Python: 42.89
CodeLlama-13B: 35.07
## lm-evaluation-harness
[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| Metric | Value |
| --- | --- |
| ARC |59.64 |
| HellaSwag |82.25 |
| MMLU | 61.33 |
| TruthfulQA | 48.45 |
| Average | 62.92 |
## Parameters
| | |
|------ | ------ |
| lr | 2e-4 |
| lr_scheduler_type | cosine |
| weight_decay | 0.0 |
| optim | paged_adamw_8bit |
| flash_attention | True |
| rerope | False |
| max_new_tokens | 4096 |
| num_train_epochs | 2 |
| bits | 4 |
| lora_r | 64 |
| lora_alpha | 16 |
| lora_dropout | 0.05 |
| double_quant | True |
| quant_type | nf4 |
| dataset_format | airoboros |
| mini_batch_size | 2 |
| grandient_accumulation_steps | 32 |
| bf16 | True |
A40-48G x 2
| | |
|------ | ------ |
| epoch | 2.0 |
| etrain_loss | 0.5 |
| etrain_runtime | 1 day, 10:25:26.77 |
| etrain_samples_per_second | 3.194 |
| etrain_steps_per_second | 0.025 |
| eeval_loss | 0.5146 |
| eeval_runtime | 0:00:25.04 |
| eeval_samples_per_second | 7.985 |
| eeval_steps_per_second | |
| 3,043 | [
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] |
mtc/LeoLM-leo-hessianai-13b-all-labels-classification-english-one-epoch-qlora-4bit | 2023-10-13T17:13:07.000Z | [
"peft",
"region:us"
] | null | mtc | null | null | mtc/LeoLM-leo-hessianai-13b-all-labels-classification-english-one-epoch-qlora-4bit | 0 | 2 | peft | 2023-10-13T17:11:59 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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Carmesix/Sentiment_Analysis_20000sample | 2023-10-13T19:37:11.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Carmesix | null | null | Carmesix/Sentiment_Analysis_20000sample | 0 | 2 | transformers | 2023-10-13T18:36:00 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: Sentiment_Analysis_20000sample
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.92603550295858
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Sentiment_Analysis_20000sample
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2415
- Accuracy: 0.925
- F1: 0.9260
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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lum-ai/metal-graphcodebert-base-gpt4-v2 | 2023-10-14T15:15:41.000Z | [
"transformers",
"pytorch",
"metal",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | lum-ai | null | null | lum-ai/metal-graphcodebert-base-gpt4-v2 | 0 | 2 | transformers | 2023-10-13T19:43:50 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: metal-graphcodebert-base-gpt4-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# metal-graphcodebert-base-gpt4-v2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.8479
- Accuracy: 0.7289
- Text Start Acc: 0.7159
- Text End Acc: 0.6831
- Code Start Acc: 0.7602
- Code End Acc: 0.7565
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 28
- eval_batch_size: 28
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Text Start Acc | Text End Acc | Code Start Acc | Code End Acc |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------------:|:------------:|:--------------:|:------------:|
| 8.3971 | 0.02 | 100 | 6.7316 | 0.3450 | 0.3354 | 0.3555 | 0.3508 | 0.3385 |
| 6.9776 | 0.04 | 200 | 6.3918 | 0.3317 | 0.3607 | 0.3607 | 0.2892 | 0.3161 |
| 6.7246 | 0.05 | 300 | 6.4021 | 0.2950 | 0.3098 | 0.3588 | 0.2861 | 0.2253 |
| 6.2857 | 0.07 | 400 | 6.4514 | 0.2544 | 0.3112 | 0.3147 | 0.1952 | 0.1965 |
| 5.6849 | 0.09 | 500 | 6.8546 | 0.2111 | 0.1829 | 0.2111 | 0.2242 | 0.2263 |
| 5.2509 | 0.11 | 600 | 6.6644 | 0.2578 | 0.2362 | 0.2369 | 0.2918 | 0.2664 |
| 4.7871 | 0.13 | 700 | 6.2825 | 0.3287 | 0.3252 | 0.3182 | 0.3412 | 0.3302 |
| 4.5091 | 0.14 | 800 | 6.6473 | 0.3498 | 0.3174 | 0.3213 | 0.3717 | 0.3890 |
| 4.2741 | 0.16 | 900 | 6.9305 | 0.3409 | 0.3046 | 0.3140 | 0.3685 | 0.3765 |
| 4.0916 | 0.18 | 1000 | 6.9337 | 0.3729 | 0.3892 | 0.3541 | 0.3695 | 0.3789 |
| 3.9533 | 0.2 | 1100 | 6.4524 | 0.4331 | 0.4411 | 0.3891 | 0.4630 | 0.4392 |
| 3.7737 | 0.22 | 1200 | 6.9752 | 0.4375 | 0.4372 | 0.3867 | 0.4670 | 0.4592 |
| 3.6482 | 0.23 | 1300 | 6.5678 | 0.4796 | 0.4856 | 0.4361 | 0.5141 | 0.4826 |
| 3.5602 | 0.25 | 1400 | 6.1688 | 0.5299 | 0.5260 | 0.4741 | 0.5569 | 0.5627 |
| 3.4698 | 0.27 | 1500 | 6.8455 | 0.4851 | 0.4801 | 0.4559 | 0.5056 | 0.4987 |
| 3.3788 | 0.29 | 1600 | 6.4223 | 0.5420 | 0.5168 | 0.4904 | 0.5809 | 0.5799 |
| 3.3251 | 0.31 | 1700 | 6.8447 | 0.5318 | 0.5374 | 0.4958 | 0.5393 | 0.5550 |
| 3.2756 | 0.32 | 1800 | 6.6636 | 0.5328 | 0.5289 | 0.4806 | 0.5676 | 0.5540 |
| 3.2408 | 0.34 | 1900 | 6.9432 | 0.5282 | 0.5139 | 0.4744 | 0.5732 | 0.5513 |
| 3.1621 | 0.36 | 2000 | 6.5627 | 0.5485 | 0.5462 | 0.5275 | 0.5585 | 0.5618 |
| 3.1129 | 0.38 | 2100 | 6.4573 | 0.5837 | 0.5588 | 0.5337 | 0.6266 | 0.6158 |
| 3.0736 | 0.4 | 2200 | 6.5341 | 0.5745 | 0.5407 | 0.5285 | 0.6187 | 0.6101 |
| 3.1045 | 0.42 | 2300 | 6.5989 | 0.5637 | 0.5541 | 0.5294 | 0.5906 | 0.5806 |
| 2.9869 | 0.43 | 2400 | 5.9950 | 0.6288 | 0.6510 | 0.6143 | 0.6286 | 0.6213 |
| 3.031 | 0.45 | 2500 | 6.0789 | 0.6228 | 0.6292 | 0.5977 | 0.6376 | 0.6268 |
| 2.9777 | 0.47 | 2600 | 6.5276 | 0.5798 | 0.5589 | 0.5255 | 0.6207 | 0.6140 |
| 2.9349 | 0.49 | 2700 | 6.8991 | 0.5739 | 0.5494 | 0.5251 | 0.6085 | 0.6125 |
| 2.9124 | 0.51 | 2800 | 6.5091 | 0.6107 | 0.5949 | 0.5815 | 0.6333 | 0.6331 |
| 2.8915 | 0.52 | 2900 | 6.5923 | 0.5845 | 0.6001 | 0.5595 | 0.5938 | 0.5846 |
| 2.8751 | 0.54 | 3000 | 6.5511 | 0.6096 | 0.5879 | 0.5700 | 0.6367 | 0.6439 |
| 2.8601 | 0.56 | 3100 | 6.3659 | 0.6199 | 0.6034 | 0.5669 | 0.6493 | 0.6598 |
| 2.8456 | 0.58 | 3200 | 6.4313 | 0.6036 | 0.5800 | 0.5632 | 0.6358 | 0.6354 |
| 2.8159 | 0.6 | 3300 | 6.4739 | 0.6275 | 0.6061 | 0.6005 | 0.6579 | 0.6453 |
| 2.7855 | 0.61 | 3400 | 6.2978 | 0.6346 | 0.6622 | 0.5956 | 0.6549 | 0.6257 |
| 2.7855 | 0.63 | 3500 | 6.3657 | 0.6196 | 0.6127 | 0.5731 | 0.6633 | 0.6293 |
| 2.7661 | 0.65 | 3600 | 6.0560 | 0.6541 | 0.6664 | 0.6242 | 0.6779 | 0.6481 |
| 2.767 | 0.67 | 3700 | 6.4933 | 0.6305 | 0.5995 | 0.5815 | 0.6770 | 0.6640 |
| 2.729 | 0.69 | 3800 | 5.9295 | 0.6679 | 0.6615 | 0.6292 | 0.7063 | 0.6748 |
| 2.7618 | 0.7 | 3900 | 6.1942 | 0.6567 | 0.6397 | 0.6347 | 0.6870 | 0.6655 |
| 2.71 | 0.72 | 4000 | 6.3116 | 0.6487 | 0.6382 | 0.5967 | 0.6951 | 0.6649 |
| 2.6885 | 0.74 | 4100 | 6.4091 | 0.6324 | 0.6167 | 0.5838 | 0.6734 | 0.6555 |
| 2.6836 | 0.76 | 4200 | 6.1200 | 0.6698 | 0.6710 | 0.6424 | 0.6940 | 0.6717 |
| 2.6377 | 0.78 | 4300 | 6.2637 | 0.6623 | 0.6715 | 0.6492 | 0.6722 | 0.6563 |
| 2.637 | 0.79 | 4400 | 5.9594 | 0.6834 | 0.6762 | 0.6608 | 0.7179 | 0.6787 |
| 2.6328 | 0.81 | 4500 | 6.3686 | 0.6437 | 0.6240 | 0.6216 | 0.6741 | 0.6549 |
| 2.6071 | 0.83 | 4600 | 6.2973 | 0.6590 | 0.6201 | 0.6371 | 0.6951 | 0.6835 |
| 2.633 | 0.85 | 4700 | 6.4171 | 0.6366 | 0.6061 | 0.5995 | 0.6824 | 0.6583 |
| 2.61 | 0.87 | 4800 | 6.2193 | 0.6555 | 0.6441 | 0.6416 | 0.6891 | 0.6472 |
| 2.6009 | 0.88 | 4900 | 6.2767 | 0.6449 | 0.6457 | 0.6373 | 0.6643 | 0.6324 |
| 2.6197 | 0.9 | 5000 | 6.2614 | 0.6677 | 0.6624 | 0.6324 | 0.6863 | 0.6899 |
| 2.5735 | 0.92 | 5100 | 6.2180 | 0.6688 | 0.6586 | 0.6209 | 0.7093 | 0.6863 |
| 2.5512 | 0.94 | 5200 | 5.9750 | 0.6846 | 0.6702 | 0.6490 | 0.7297 | 0.6894 |
| 2.5685 | 0.96 | 5300 | 5.8572 | 0.7053 | 0.7120 | 0.6648 | 0.7302 | 0.7142 |
| 2.5614 | 0.97 | 5400 | 6.3916 | 0.6566 | 0.6565 | 0.6030 | 0.6959 | 0.6708 |
| 2.5365 | 0.99 | 5500 | 6.2022 | 0.6796 | 0.6803 | 0.6622 | 0.6886 | 0.6872 |
| 2.5137 | 1.01 | 5600 | 6.1598 | 0.6761 | 0.6635 | 0.6453 | 0.7047 | 0.6910 |
| 2.4921 | 1.03 | 5700 | 6.0222 | 0.6818 | 0.6528 | 0.6502 | 0.7180 | 0.7063 |
| 2.4955 | 1.05 | 5800 | 6.1344 | 0.6762 | 0.6616 | 0.6250 | 0.7163 | 0.7018 |
| 2.4384 | 1.06 | 5900 | 6.2503 | 0.6726 | 0.6751 | 0.6596 | 0.6799 | 0.6759 |
| 2.4571 | 1.08 | 6000 | 6.3051 | 0.6637 | 0.6435 | 0.6192 | 0.7025 | 0.6894 |
| 2.455 | 1.1 | 6100 | 6.0680 | 0.6799 | 0.6896 | 0.6430 | 0.7052 | 0.6816 |
| 2.4189 | 1.12 | 6200 | 6.2017 | 0.6742 | 0.6631 | 0.6318 | 0.7145 | 0.6875 |
| 2.4405 | 1.14 | 6300 | 6.1502 | 0.6773 | 0.6631 | 0.6395 | 0.7011 | 0.7054 |
| 2.4469 | 1.16 | 6400 | 6.1243 | 0.6800 | 0.6772 | 0.6390 | 0.7092 | 0.6948 |
| 2.4166 | 1.17 | 6500 | 6.0812 | 0.6819 | 0.6639 | 0.6391 | 0.7141 | 0.7106 |
| 2.4241 | 1.19 | 6600 | 5.6822 | 0.7309 | 0.7273 | 0.7163 | 0.7426 | 0.7376 |
| 2.412 | 1.21 | 6700 | 6.1815 | 0.6781 | 0.6598 | 0.6544 | 0.7128 | 0.6855 |
| 2.4207 | 1.23 | 6800 | 5.9485 | 0.6983 | 0.7056 | 0.6681 | 0.7193 | 0.7001 |
| 2.3856 | 1.25 | 6900 | 6.2165 | 0.6769 | 0.6538 | 0.6242 | 0.7227 | 0.7068 |
| 2.3837 | 1.26 | 7000 | 6.2255 | 0.6784 | 0.6531 | 0.6288 | 0.7208 | 0.7108 |
| 2.4406 | 1.28 | 7100 | 6.0703 | 0.6964 | 0.6972 | 0.6630 | 0.7098 | 0.7155 |
| 2.3832 | 1.3 | 7200 | 6.0778 | 0.6867 | 0.6864 | 0.6563 | 0.7102 | 0.6939 |
| 2.4194 | 1.32 | 7300 | 6.1522 | 0.6804 | 0.6654 | 0.6349 | 0.7144 | 0.7069 |
| 2.4214 | 1.34 | 7400 | 6.2848 | 0.6664 | 0.6660 | 0.6218 | 0.6998 | 0.6781 |
| 2.3598 | 1.35 | 7500 | 6.0278 | 0.7073 | 0.6869 | 0.6610 | 0.7400 | 0.7413 |
| 2.3669 | 1.37 | 7600 | 5.9456 | 0.7121 | 0.7083 | 0.6921 | 0.7264 | 0.7217 |
| 2.4028 | 1.39 | 7700 | 6.1055 | 0.6827 | 0.6846 | 0.6357 | 0.7085 | 0.7018 |
| 2.3922 | 1.41 | 7800 | 5.9995 | 0.6972 | 0.6770 | 0.6487 | 0.7351 | 0.7281 |
| 2.3867 | 1.43 | 7900 | 6.2460 | 0.6816 | 0.6672 | 0.6363 | 0.7096 | 0.7135 |
| 2.371 | 1.44 | 8000 | 5.9551 | 0.7072 | 0.6980 | 0.6734 | 0.7318 | 0.7256 |
| 2.3553 | 1.46 | 8100 | 5.9955 | 0.7052 | 0.7135 | 0.6651 | 0.7256 | 0.7168 |
| 2.4089 | 1.48 | 8200 | 6.1565 | 0.6869 | 0.6769 | 0.6335 | 0.7102 | 0.7270 |
| 2.3822 | 1.5 | 8300 | 6.2396 | 0.6817 | 0.6722 | 0.6434 | 0.7130 | 0.6982 |
| 2.3743 | 1.52 | 8400 | 5.9867 | 0.6964 | 0.6853 | 0.6573 | 0.7236 | 0.7195 |
| 2.3818 | 1.53 | 8500 | 6.1663 | 0.6839 | 0.6549 | 0.6340 | 0.7371 | 0.7094 |
| 2.3467 | 1.55 | 8600 | 6.3287 | 0.6657 | 0.6509 | 0.6116 | 0.7034 | 0.6968 |
| 2.3544 | 1.57 | 8700 | 5.9424 | 0.7101 | 0.7074 | 0.6769 | 0.7346 | 0.7213 |
| 2.3138 | 1.59 | 8800 | 6.1324 | 0.6859 | 0.6778 | 0.6387 | 0.7278 | 0.6994 |
| 2.3574 | 1.61 | 8900 | 6.0064 | 0.6995 | 0.6850 | 0.6600 | 0.7350 | 0.7179 |
| 2.3234 | 1.62 | 9000 | 5.9436 | 0.7048 | 0.6848 | 0.6644 | 0.7450 | 0.7251 |
| 2.3546 | 1.64 | 9100 | 6.0459 | 0.6933 | 0.6701 | 0.6415 | 0.7306 | 0.7311 |
| 2.3518 | 1.66 | 9200 | 6.0300 | 0.6976 | 0.6831 | 0.6657 | 0.7208 | 0.7209 |
| 2.3474 | 1.68 | 9300 | 6.2438 | 0.6880 | 0.6584 | 0.6192 | 0.7376 | 0.7366 |
| 2.3079 | 1.7 | 9400 | 6.1013 | 0.6922 | 0.6720 | 0.6398 | 0.7326 | 0.7242 |
| 2.3718 | 1.71 | 9500 | 5.9430 | 0.7004 | 0.6996 | 0.6597 | 0.7316 | 0.7109 |
| 2.3153 | 1.73 | 9600 | 6.0077 | 0.7016 | 0.6941 | 0.6597 | 0.7359 | 0.7166 |
| 2.2929 | 1.75 | 9700 | 6.0677 | 0.6997 | 0.6787 | 0.6448 | 0.7424 | 0.7327 |
| 2.3055 | 1.77 | 9800 | 6.1334 | 0.6887 | 0.6659 | 0.6466 | 0.7252 | 0.7170 |
| 2.327 | 1.79 | 9900 | 5.8188 | 0.7274 | 0.7217 | 0.6932 | 0.7538 | 0.7407 |
| 2.2936 | 1.81 | 10000 | 5.9292 | 0.7172 | 0.7042 | 0.6836 | 0.7497 | 0.7313 |
| 2.2941 | 1.82 | 10100 | 6.2885 | 0.6812 | 0.6610 | 0.6220 | 0.7321 | 0.7098 |
| 2.3006 | 1.84 | 10200 | 5.8766 | 0.7159 | 0.7171 | 0.6864 | 0.7352 | 0.7250 |
| 2.3093 | 1.86 | 10300 | 5.8775 | 0.7189 | 0.7182 | 0.6820 | 0.7436 | 0.7319 |
| 2.3366 | 1.88 | 10400 | 6.1641 | 0.6916 | 0.6679 | 0.6407 | 0.7321 | 0.7255 |
| 2.3077 | 1.9 | 10500 | 5.8684 | 0.7198 | 0.6958 | 0.6744 | 0.7584 | 0.7505 |
| 2.317 | 1.91 | 10600 | 5.9451 | 0.7119 | 0.6781 | 0.6581 | 0.7507 | 0.7608 |
| 2.2959 | 1.93 | 10700 | 6.0043 | 0.7100 | 0.6915 | 0.6526 | 0.7512 | 0.7448 |
| 2.3102 | 1.95 | 10800 | 5.9453 | 0.7171 | 0.7025 | 0.6803 | 0.7504 | 0.7351 |
| 2.2915 | 1.97 | 10900 | 6.0021 | 0.7087 | 0.6753 | 0.6582 | 0.7421 | 0.7591 |
| 2.3239 | 1.99 | 11000 | 6.0865 | 0.6927 | 0.6744 | 0.6382 | 0.7313 | 0.7270 |
| 2.2721 | 2.0 | 11100 | 5.9036 | 0.7169 | 0.7026 | 0.6824 | 0.7435 | 0.7393 |
| 2.2238 | 2.02 | 11200 | 6.0722 | 0.7050 | 0.6864 | 0.6488 | 0.7471 | 0.7376 |
| 2.2447 | 2.04 | 11300 | 6.1078 | 0.6991 | 0.6627 | 0.6440 | 0.7452 | 0.7445 |
| 2.2241 | 2.06 | 11400 | 5.9561 | 0.7144 | 0.7061 | 0.6706 | 0.7502 | 0.7307 |
| 2.2261 | 2.08 | 11500 | 5.7849 | 0.7248 | 0.7244 | 0.6917 | 0.7359 | 0.7474 |
| 2.231 | 2.09 | 11600 | 6.0342 | 0.7090 | 0.6958 | 0.6485 | 0.7486 | 0.7432 |
| 2.1898 | 2.11 | 11700 | 5.9014 | 0.7201 | 0.7069 | 0.6768 | 0.7484 | 0.7483 |
| 2.2201 | 2.13 | 11800 | 5.9044 | 0.7174 | 0.7009 | 0.6657 | 0.7542 | 0.7486 |
| 2.2307 | 2.15 | 11900 | 5.9123 | 0.7155 | 0.7054 | 0.6746 | 0.7407 | 0.7413 |
| 2.1999 | 2.17 | 12000 | 6.2262 | 0.6832 | 0.6482 | 0.6148 | 0.7354 | 0.7345 |
| 2.2209 | 2.18 | 12100 | 5.9494 | 0.7107 | 0.6891 | 0.6654 | 0.7408 | 0.7474 |
| 2.2259 | 2.2 | 12200 | 5.9214 | 0.7109 | 0.7052 | 0.6571 | 0.7392 | 0.7422 |
| 2.2245 | 2.22 | 12300 | 5.8257 | 0.7295 | 0.7212 | 0.6841 | 0.7600 | 0.7528 |
| 2.2082 | 2.24 | 12400 | 6.0086 | 0.7081 | 0.7012 | 0.6534 | 0.7507 | 0.7271 |
| 2.2245 | 2.26 | 12500 | 5.9757 | 0.7081 | 0.6949 | 0.6458 | 0.7426 | 0.7493 |
| 2.2237 | 2.27 | 12600 | 5.8529 | 0.7212 | 0.7150 | 0.6687 | 0.7589 | 0.7423 |
| 2.2003 | 2.29 | 12700 | 6.0264 | 0.7004 | 0.6798 | 0.6391 | 0.7405 | 0.7422 |
| 2.1977 | 2.31 | 12800 | 5.8916 | 0.7227 | 0.7137 | 0.6774 | 0.7574 | 0.7424 |
| 2.22 | 2.33 | 12900 | 5.9524 | 0.7153 | 0.6999 | 0.6703 | 0.7442 | 0.7467 |
| 2.1917 | 2.35 | 13000 | 5.9550 | 0.7142 | 0.6964 | 0.6694 | 0.7464 | 0.7446 |
| 2.215 | 2.36 | 13100 | 5.9686 | 0.7090 | 0.6853 | 0.6596 | 0.7478 | 0.7433 |
| 2.2258 | 2.38 | 13200 | 5.7851 | 0.7321 | 0.7214 | 0.6929 | 0.7594 | 0.7548 |
| 2.2281 | 2.4 | 13300 | 5.9139 | 0.7193 | 0.6966 | 0.6765 | 0.7526 | 0.7514 |
| 2.2055 | 2.42 | 13400 | 5.9116 | 0.7197 | 0.7077 | 0.6696 | 0.7534 | 0.7483 |
| 2.1781 | 2.44 | 13500 | 5.9780 | 0.7117 | 0.6918 | 0.6603 | 0.7450 | 0.7495 |
| 2.2239 | 2.45 | 13600 | 5.9471 | 0.7110 | 0.6875 | 0.6655 | 0.7435 | 0.7476 |
| 2.2284 | 2.47 | 13700 | 5.9708 | 0.7082 | 0.6934 | 0.6694 | 0.7384 | 0.7317 |
| 2.1786 | 2.49 | 13800 | 5.8479 | 0.7290 | 0.7189 | 0.6845 | 0.7604 | 0.7523 |
| 2.1944 | 2.51 | 13900 | 5.8999 | 0.7194 | 0.7051 | 0.6831 | 0.7456 | 0.7437 |
| 2.1895 | 2.53 | 14000 | 5.9709 | 0.7107 | 0.6936 | 0.6660 | 0.7466 | 0.7364 |
| 2.1995 | 2.55 | 14100 | 5.7951 | 0.7333 | 0.7223 | 0.6908 | 0.7612 | 0.7588 |
| 2.1989 | 2.56 | 14200 | 5.8615 | 0.7252 | 0.7102 | 0.6743 | 0.7595 | 0.7570 |
| 2.2218 | 2.58 | 14300 | 5.8684 | 0.7246 | 0.7161 | 0.6939 | 0.7445 | 0.7438 |
| 2.192 | 2.6 | 14400 | 5.9905 | 0.7132 | 0.6884 | 0.6538 | 0.7593 | 0.7512 |
| 2.1772 | 2.62 | 14500 | 5.9371 | 0.7145 | 0.7012 | 0.6554 | 0.7591 | 0.7422 |
| 2.1891 | 2.64 | 14600 | 5.9154 | 0.7187 | 0.7056 | 0.6669 | 0.7560 | 0.7462 |
| 2.1816 | 2.65 | 14700 | 5.9592 | 0.7126 | 0.6936 | 0.6633 | 0.7475 | 0.7460 |
| 2.2013 | 2.67 | 14800 | 5.9243 | 0.7151 | 0.6980 | 0.6649 | 0.7497 | 0.7480 |
| 2.208 | 2.69 | 14900 | 5.8249 | 0.7250 | 0.7113 | 0.6717 | 0.7603 | 0.7565 |
| 2.2053 | 2.71 | 15000 | 5.8508 | 0.7286 | 0.7130 | 0.6840 | 0.7596 | 0.7577 |
| 2.1609 | 2.73 | 15100 | 5.9201 | 0.7207 | 0.7042 | 0.6770 | 0.7542 | 0.7475 |
| 2.1801 | 2.74 | 15200 | 5.8693 | 0.7275 | 0.7084 | 0.6880 | 0.7593 | 0.7545 |
| 2.1972 | 2.76 | 15300 | 5.8483 | 0.7285 | 0.7166 | 0.6874 | 0.7596 | 0.7502 |
| 2.2134 | 2.78 | 15400 | 5.8746 | 0.7275 | 0.7164 | 0.6826 | 0.7603 | 0.7507 |
| 2.1707 | 2.8 | 15500 | 5.8845 | 0.7246 | 0.7121 | 0.6821 | 0.7550 | 0.7494 |
| 2.1909 | 2.82 | 15600 | 5.8627 | 0.7269 | 0.7073 | 0.6849 | 0.7598 | 0.7556 |
| 2.2049 | 2.83 | 15700 | 5.8539 | 0.7291 | 0.7080 | 0.6856 | 0.7652 | 0.7575 |
| 2.1588 | 2.85 | 15800 | 5.9037 | 0.7214 | 0.7028 | 0.6820 | 0.7519 | 0.7488 |
| 2.1796 | 2.87 | 15900 | 5.8557 | 0.7263 | 0.7118 | 0.6844 | 0.7565 | 0.7524 |
| 2.216 | 2.89 | 16000 | 5.8732 | 0.7261 | 0.7103 | 0.6777 | 0.7608 | 0.7556 |
| 2.1994 | 2.91 | 16100 | 5.8283 | 0.7304 | 0.7202 | 0.6854 | 0.7615 | 0.7543 |
| 2.168 | 2.92 | 16200 | 5.8564 | 0.7274 | 0.7156 | 0.6822 | 0.7570 | 0.7547 |
| 2.1989 | 2.94 | 16300 | 5.8409 | 0.7293 | 0.7179 | 0.6839 | 0.7595 | 0.7560 |
| 2.22 | 2.96 | 16400 | 5.8212 | 0.7317 | 0.7211 | 0.6851 | 0.7632 | 0.7575 |
| 2.1602 | 2.98 | 16500 | 5.8388 | 0.7303 | 0.7171 | 0.6840 | 0.7623 | 0.7577 |
| 2.1948 | 3.0 | 16600 | 5.8484 | 0.7289 | 0.7157 | 0.6831 | 0.7603 | 0.7566 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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rjarpa/ms-16maps_alpha-ds | 2023-10-28T16:39:49.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | rjarpa | null | null | rjarpa/ms-16maps_alpha-ds | 0 | 2 | transformers | 2023-10-14T01:21:03 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: ms-16maps_alpha-ds
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ms-16maps_alpha-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8259
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.0301 | 0.17 | 100 | 4.9775 |
| 4.9441 | 0.33 | 200 | 4.8830 |
| 4.8801 | 0.5 | 300 | 4.8528 |
| 4.8623 | 0.67 | 400 | 4.8378 |
| 4.8464 | 0.83 | 500 | 4.8285 |
| 4.8475 | 1.0 | 600 | 4.8259 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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rjarpa/ms-16maps_nonalpha-ds | 2023-10-28T16:48:43.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | rjarpa | null | null | rjarpa/ms-16maps_nonalpha-ds | 0 | 2 | transformers | 2023-10-14T01:24:17 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: ms-16maps_nonalpha-ds
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ms-16maps_nonalpha-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.3569
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.6897 | 0.06 | 100 | 6.5741 |
| 6.5747 | 0.11 | 200 | 6.5529 |
| 6.5287 | 0.17 | 300 | 6.5047 |
| 6.4802 | 0.22 | 400 | 6.4695 |
| 6.4668 | 0.28 | 500 | 6.4435 |
| 6.4468 | 0.33 | 600 | 6.4283 |
| 6.4378 | 0.39 | 700 | 6.4119 |
| 6.4183 | 0.44 | 800 | 6.4043 |
| 6.4172 | 0.5 | 900 | 6.3952 |
| 6.3946 | 0.55 | 1000 | 6.3870 |
| 6.3888 | 0.61 | 1100 | 6.3795 |
| 6.3756 | 0.66 | 1200 | 6.3749 |
| 6.3738 | 0.72 | 1300 | 6.3690 |
| 6.3771 | 0.77 | 1400 | 6.3644 |
| 6.3709 | 0.83 | 1500 | 6.3600 |
| 6.3706 | 0.88 | 1600 | 6.3578 |
| 6.3652 | 0.94 | 1700 | 6.3571 |
| 6.3767 | 1.0 | 1800 | 6.3569 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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taylorbollman/distilbert-base-uncased-finetuned-imdb | 2023-11-03T00:47:34.000Z | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | taylorbollman | null | null | taylorbollman/distilbert-base-uncased-finetuned-imdb | 0 | 2 | transformers | 2023-10-14T02:04:41 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4119
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7024 | 1.0 | 157 | 2.4966 |
| 2.5796 | 2.0 | 314 | 2.4282 |
| 2.5355 | 3.0 | 471 | 2.4510 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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t4ai/finetuning-classification-model-t4-roberta | 2023-10-14T08:30:07.000Z | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | t4ai | null | null | t4ai/finetuning-classification-model-t4-roberta | 0 | 2 | transformers | 2023-10-14T02:53:19 | ---
license: mit
base_model: roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: t4ai/finetuning-classification-model-t4-roberta
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t4ai/finetuning-classification-model-t4-roberta
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2423
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 24360, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.4136 | 0 |
| 0.3023 | 1 |
| 0.2423 | 2 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
| 1,806 | [
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] |
Mathoctopus/Cross_7B | 2023-11-02T07:36:42.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"es",
"zh",
"de",
"ru",
"th",
"sw",
"ja",
"fr",
"bn",
"dataset:Mathoctopus/GSM8KInstruct_Parallel",
"arxiv:2310.20246",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Mathoctopus | null | null | Mathoctopus/Cross_7B | 2 | 2 | transformers | 2023-10-14T04:47:34 | ---
license: apache-2.0
datasets:
- Mathoctopus/GSM8KInstruct_Parallel
language:
- en
- es
- zh
- de
- ru
- th
- sw
- ja
- fr
- bn
---
# 🐙 Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
Project Page: [https://mathoctopus.github.io/](https://mathoctopus.github.io/)
Paper: [https://arxiv.org/abs/2310.20246.pdf](https://arxiv.org/abs/2310.20246.pdf)
Code: [https://github.com/microsoft/MathOctopus](https://github.com/microsoft/MathOctopus)
### Introduction
We introduce 🐙 MathOctopus, a series of open-source large language models (LLMs) specifically tailored for multilingual math problem-solving. The MathOctopus models are trained on 🤗 MGSM8KInstruct Dataset, encompassing ten distinct languages.
MathOctopus notably outperforms conventional open-source LLMs and exhibits superiority over ChatGPT in few-shot scenarios.
### Datasets
#### **MGSM8KInstruct**
| Training Dataset | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:----------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MGSM8KInstruct | 7473 | 7472 | 7466 | 6539 | 7466 | 7470 | 7469 | 7471 | 7361 | 7473 | **73.6K** |
#### **MSVAMP**
| Test Dataset | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:----------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MSVAMP | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | **10K** |
#### Usage
Our dataset and models are all available at Huggingface.
🤗 [MGSM8KInstruct_Parallel Dataset](https://huggingface.co/datasets/Mathoctopus/GSM8KInstruct_Parallel)
🤗 [MGSM8KInstruct_Cross Dataset](https://huggingface.co/datasets/Mathoctopus/MGSM8KInstruct_Cross)
🤗 [MSVAMP Dataset](https://huggingface.co/datasets/Mathoctopus/MSVAMP)
## Models
| Base Model: LLama | Parallel-Training | Cross-Training |
|----|---------------------------------------------------------------|---------------------------------------------------------------------------|
| 7B-LLaMA 2 | 🐙 [MathOctopus-Parallel-7B](https://huggingface.co/Mathoctopus/Parallel_7B) | 🐙 [MathOctopus-Cross-7B](https://huggingface.co/Mathoctopus/Cross_7B) |
|| 🐙[MathOctopus-Parallel-xRFT-7B](https://huggingface.co/Mathoctopus/Parallel_xRFT_7B)|🐙[MathOctopus-Cross-xRFT-7B](https://huggingface.co/Mathoctopus/Cross_xRFT_7B)|
| 13B-LLaMA 2 | 🐙 [MathOctopus-Parallel-13B](https://huggingface.co/Mathoctopus/Parallel_13B) | 🐙 [MathOctopus-Cross-13B](https://huggingface.co/Mathoctopus/Cross_13B) |
|| 🐙[MathOctopus-Parallel-xRFT-13B](https://huggingface.co/Mathoctopus/Parallel_xRFT_13B)|🐙[MathOctopus-Cross-xRFT-13B]|
| 33B-LLaMA 1 | 🐙 [MathOctopus-Parallel-33B](https://huggingface.co/Mathoctopus/Parallel_33B) | 🐙 [MathOctopus-Cross-33B] |
| 70B-LLaMA 2 | Coming soon! | Coming Soon! |
*-Parallel refers to our model trained with the parallel-training strategy.
*-Cross refers to our model trained with cross-training strategy.
*-xRFT means we train the model with multilingual rejection sampling.
### **Overall Results on MGSM**
| 7B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 52.0 | 23.6 | 31.6 | 18.8 | 38.0 | 39.2 | 36.4 | 27.2 | 33.6 | 21.6 | 32.2 |
| **xRFT**-MathOctopus<sup>C</sup>| 51.2 | 24.0 | 33.2 | 18.8 | 36.0 | 41.2 | 37.6 | 29.6 | 36.4 | 25.2 | 33.3 |
| MathOctopus<sup>P</sup>-LoRA | 30.4 | 15.2 | 23.6 | 10.4 | 22.8 | 24.8 | 26.4 | 18.0 | 22.0 | 14.8 | 20.8 |
| MathOctopus<sup>P</sup> | 52.4 | 39.2 | 38.4 | 28.8 | 44.8 | 42.4 | 43.6 | 36.0 | 39.6 | 34.4 | 40.0 |
| **xRFT**-MathOctopus<sup>P</sup>| 54.8 | 38.4 | 45.2 | 33.2 | 43.6 | 45.2 | 38.0 | 35.6 | 48.4 | 36.4 | 41.9 |
<p></p >
| 13B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 56.4 | 27.2 | 39.2 | 24.0 | 47.6 | 49.6 | 47.6 | 40.4 | 42.0 | 24.8 | 39.9 |
| **xRFT**-MathOctopus<sup>C</sup>| 53.6 | 28.0 | 45.2 | 21.2 | 48.0 | 46.4 | 46.0 | 35.2 | 45.6 | 28.8 | 39.8 |
| MathOctopus<sup>P</sup> | 53.2 | 42.8 | 48.8 | 35.2 | 44.4 | 48.0 | 48.4 | 43.2 | 47.6 | 46.8 | 45.8 |
| **xRFT**-MathOctopus<sup>P</sup>| 51.6 | 46.0 | 51.2 | 42.0 | 49.2 | 53.2 | 49.6 | 39.6 | 47.6 | 46.0 | 47.6 |
<p></p >
| 30-34B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 55.6 | 24.4 | 36.0 | 19.2 | 40.4 | 51.2 | 44.4 | 27.2 | 37.2 | 21.6 | 35.7 |
| **xRFT**-MathOctopus<sup>C</sup>| 53.6 | 27.6 | 34.4 | 19.2 | 47.2 | 47.6 | 44.8 | 30.8 | 38.8 | 22.8 | 36.7 |
| MathOctopus<sup>P</sup> | 56.4 | 46.8 | 52.0 | 35.2 | 47.2 | 53.2 | 48.0 | 39.2 | 45.6 | 41.2 | 46.5 |
| **xRFT**-MathOctopus<sup>P</sup>| 51.6 | 47.2 | 52.4 | 37.6 | 51.2 | 52.8 | 44.4 | 41.6 | 50.0 | 47.6 | 47.6 |
### **Overall Results on MSVAMP**
| 7B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 49.2 | 36.6 | 43.6 | 30.2 | 48.6 | 46.8 | 46.4 | 42.5 | 46.7 | 34.0 | 42.5 |
| **xRFT**-MathOctopus<sup>C</sup>| 49.9 | 37.7 | 43.3 | 32.9 | 46.5 | 47.6 | 47.3 | 42.7 | 46.6 | 36.2 | 43.1 |
| MathOctopus<sup>P</sup>-LoRA | 30.4 | 15.2 | 23.6 | 10.4 | 22.8 | 24.8 | 26.4 | 18.0 | 22.0 | 14.8 | 20.8 |
| MathOctopus<sup>P</sup> | 46.5 | 40.1 | 42.5 | 29.1 | 43.5 | 45.4 | 46.0 | 42.5 | 45.4 | 35.7 | 41.7 |
| **xRFT**-MathOctopus<sup>P</sup>| 46.8 | 42.3 | 43.2 | 32.8 | 43.1 | 44.5 | 45.3 | 43.2 | 42.1 | 40.5 | 42.4 |
<p></p >
| 13B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 56.6 | 40.4 | 49.0 | 30.3 | 50.9 | 54.2 | 54.7 | 46.3 | 52.4 | 35.7 | 47.1 |
| **xRFT**-MathOctopus<sup>C</sup>| 52.9 | 41.9 | 49.2 | 34.1 | 50.5 | 52.8 | 51.5 | 45.8 | 50.2 | 35.7 | 46.5 |
| MathOctopus<sup>P</sup> | 50.7 | 43.4 | 42.6 | 31.8 | 48.4 | 49.4 | 50.6 | 41.1 | 46.9 | 39.3 | 44.4 |
| **xRFT**-MathOctopus<sup>P</sup>| 44.6 | 43.4 | 46.4 | 34.2 | 47.7 | 48.2 | 49.9 | 43.1 | 48.2 | 39.5 | 44.5 |
<p></p >
| 30-34B Model | En | Sw | Zh | Bn | De | Es | Fr | Ja | Ru | Th | Overall |
|:--------------------------------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|
| MathOctopus<sup>C</sup> | 51.5 | 42.1 | 46.2 | 23.2 | 50.5 | 52.1 | 52.9 | 42.2 | 50.5 | 33.4 | 44.5 |
| **xRFT**-MathOctopus<sup>C</sup>| 48.1 | 42.8 | 43.6 | 23.3 | 48.7 | 50.0 | 48.9 | 43.4 | 44.6 | 35.5 | 42.9 |
| MathOctopus<sup>P</sup> | 56.4 | 46.8 | 52.0 | 35.2 | 47.2 | 53.2 | 48.0 | 39.2 | 45.6 | 41.2 | 46.5 |
| **xRFT**-MathOctopus<sup>P</sup>| 48.0 | 42.3 | 46.1 | 36.2 | 47.5 | 48.5 | 48.3 | 45.8 | 47.2 | 41.2 | 45.1 |
### **MathOctopus in English**
| Models | GSM8K | SVAMP |
|:--------------------------------|:--------|:--------|
| LLaMA 2-7B | 42.4 | 38.3 |
| MathOctopus<sup>P</sup>-7B | 49.3 | 46.8 |
| MathOctopus<sup>C</sup>-7B | 50.8 | 49.3 |
| LLaMA 2-13B | 51.0 | 50.9 |
| MathOctopus<sup>P</sup>-13B | 55.5 | 52.1 |
| MathOctopus<sup>C</sup>-13B | 56.6 | 56.6 |
| LLaMA 1-33B | 50.0 | 49.0 |
| MathOctopus<sup>P</sup>-33B | 56.0 | 52.5 |
| MathOctopus<sup>C</sup>-33B | 53.7 | 51.5 |
## Intended Uses
These models are trained for research purposes. They are designed to solve multilingual math problems. They can be used in educational software, tutoring systems, or any application where a solution to a math problem is needed.
## Citation
Please cite our paper if you use our data, model or code. Please also kindly cite the original dataset papers.
```
@misc{chen2023breaking,
title={Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations},
author={Nuo Chen and Zinan Zheng and Ning Wu and Linjun Shou and Ming Gong and Yangqiu Song and Dongmei Zhang and Jia Li},
year={2023},
eprint={2310.20246},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 10,504 | [
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nlewins/whisper-tiny-translateV-fp16-1e5-noforce-es-nosuppress | 2023-10-14T17:59:39.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ceb",
"dataset:google/fleurs",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | nlewins | null | null | nlewins/whisper-tiny-translateV-fp16-1e5-noforce-es-nosuppress | 0 | 2 | transformers | 2023-10-14T07:21:16 | ---
language:
- ceb
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- google/fleurs
model-index:
- name: Whisper finetuned for ceb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper finetuned for ceb
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Fleurs Ceb subset dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.2462
- eval_wer: 122.3821
- eval_wer_un_norm: 119.9640
- eval_runtime: 406.1746
- eval_samples_per_second: 2.071
- eval_steps_per_second: 0.261
- epoch: 13.32
- step: 5100
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: reduce_lr_on_plateau
- lr_scheduler_warmup_steps: 2000
- training_steps: 20000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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EllieLee/distilbert-base-uncased-finetuned-emotion | 2023-10-14T14:10:18.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | EllieLee | null | null | EllieLee/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-10-14T13:53:26 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.923973954226437
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2220
- Accuracy: 0.924
- F1: 0.9240
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8453 | 1.0 | 250 | 0.3277 | 0.901 | 0.8983 |
| 0.2625 | 2.0 | 500 | 0.2220 | 0.924 | 0.9240 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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NeverSleep/Mistral-11B-SynthIAirOmniMix | 2023-10-14T15:37:15.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | NeverSleep | null | null | NeverSleep/Mistral-11B-SynthIAirOmniMix | 0 | 2 | transformers | 2023-10-14T14:34:58 | ---
license: cc-by-nc-4.0
---
Replaced Zephyr by Airoboros 2.2 and OpenOrca by SynthIA in the mix, the reason why is to see if using merged Mistral models using all the same prompt format would be a better step or not.
## Description
This repo contains fp16 files of Mistral-11B-SynthIAirOmniMix.
## Model used
- [SynthIA-7B-v1.5](https://huggingface.co/migtissera/SynthIA-7B-v1.5)
- [Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus)
- [CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B)
- [airoboros-mistral2.2-7b](https://huggingface.co/teknium/airoboros-mistral2.2-7b)
## Prompt template
3 out of 4 models use the same prompting format in this merge.
The best one should be this one, since Zephyr and OpenOrca is out of the merge:
```
(SYSTEM: {context}) - Not mandatory
USER: {prompt}
ASSISTANT:
```
But this one (maybe) work too:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## The secret sauce
Mistral-11B-SynthIAOpenPlatypus :
```
slices:
- sources:
- model: "/content/drive/MyDrive/SynthIA-7B-v1.5-bf16"
layer_range: [0, 24]
- sources:
- model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
Mistral-11B-CC-Airo :
```
slices:
- sources:
- model: "/content/drive/MyDrive/CC-v1.1-7B-bf16"
layer_range: [0, 24]
- sources:
- model: "/content/drive/MyDrive/Mistral-7B-Airoboros-2.2-bf16"
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```
Mistral-11B-SynthIAirOmniMix :
```
slices:
- sources:
- model: Mistral-11B-SynthIAOpenPlatypus
layer_range: [0, 48]
- model: Mistral-11B-CC-Airo
layer_range: [0, 48]
merge_method: slerp
base_model: Mistral-11B-OpenOrcaPlatypus
parameters:
t:
- filter: lm_head
value: [0.75]
- filter: embed_tokens
value: [0.75]
- filter: self_attn
value: [0.75, 0.25]
- filter: mlp
value: [0.25, 0.75]
- filter: layernorm
value: [0.5, 0.5]
- filter: modelnorm
value: [0.75]
- value: 0.5 # fallback for rest of tensors
dtype: bfloat16
```
I use [mergekit](https://github.com/cg123/mergekit) for all the manipulation told here.
## Some scoring I done myself
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5410|± |0.0146|
| | |acc_norm|0.5640|± |0.0145|
|arc_easy | 0|acc |0.8228|± |0.0078|
| | |acc_norm|0.8068|± |0.0081|
|hellaswag | 0|acc |0.6274|± |0.0048|
| | |acc_norm|0.8167|± |0.0039|
|piqa | 0|acc |0.8052|± |0.0092|
| | |acc_norm|0.8232|± |0.0089|
|truthfulqa_mc| 1|mc1 |0.3905|± |0.0171|
| | |mc2 |0.5592|± |0.0155|
|winogrande | 0|acc |0.7364|± |0.0124|
## Others
Special thanks to Sushi, [Henky](https://github.com/KoboldAI/KoboldAI-Client) for the machine he give me for big task, and [Charles Goddard](https://github.com/cg123) for his amazing tool.
If you want to support me, you can [here](https://ko-fi.com/undiai). | 3,493 | [
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] |
fkr1306/distilbert-base-uncased-finetuned-emotion | 2023-10-29T02:58:18.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | fkr1306 | null | null | fkr1306/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-10-14T14:39:29 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.927
- name: F1
type: f1
value: 0.9269870944171579
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2192
- Accuracy: 0.927
- F1: 0.9270
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8221 | 1.0 | 250 | 0.3228 | 0.9055 | 0.9042 |
| 0.2483 | 2.0 | 500 | 0.2192 | 0.927 | 0.9270 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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CobraMamba/mamba-gpt-7b-v2 | 2023-10-14T14:54:28.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"gpt",
"llm",
"large language model",
"en",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | text-generation | CobraMamba | null | null | CobraMamba/mamba-gpt-7b-v2 | 0 | 2 | transformers | 2023-10-14T14:42:12 | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
inference: false
thumbnail: >-
https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
license: apache-2.0
---
# Model Card
## Summary
We have fine-tuned the OpenLLaMA model and surpassed the original model in multiple evaluation subtasks, making it currently one of the best performing 3B model, with comparable performance to llama-7b.
- Base model: [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2)
## Usage
To use the model with the `transformers` library on a machine with GPU(s), first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
Then, run the following Python snippet:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("CobraMamba/mamba-gpt-7b-v2")
model = AutoModelForCausalLM.from_pretrained("CobraMamba/mamba-gpt-7b-v2", trust_remote_code=True, torch_dtype=torch.float16)
input_content = "Your text here"
input_ids = tokenizer.encode(input_content, return_tensors="pt")
output = model.generate(input_ids, max_length=128, temperature=0.7)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)
```
## Citation
If this work is helpful, please kindly cite as:
```bibtex
@Misc{mamba-gpt-7b-v2,
title = {Mamba-GPT-7b-v2},
author = {chiliu},
howpublished = {\url{https://huggingface.co/CobraMamba/mamba-gpt-7b-v2}},
year = {2023}
}
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
---
license: apache-2.0
---
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legacy107/multi-qa-mpnet-base-dot-v1-wikipedia-augmented-search-farmed | 2023-10-14T15:14:17.000Z | [
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | legacy107 | null | null | legacy107/multi-qa-mpnet-base-dot-v1-wikipedia-augmented-search-farmed | 0 | 2 | sentence-transformers | 2023-10-14T15:13:56 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 3744 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1123,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,498 | [
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imdatta0/qwen-books-gas32-cos | 2023-10-14T17:00:29.000Z | [
"peft",
"region:us"
] | null | imdatta0 | null | null | imdatta0/qwen-books-gas32-cos | 0 | 2 | peft | 2023-10-14T17:00:28 | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
| 101 | [
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t4ai/finetuning-classification-model-t4-roberta2 | 2023-10-14T20:41:23.000Z | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | t4ai | null | null | t4ai/finetuning-classification-model-t4-roberta2 | 0 | 2 | transformers | 2023-10-14T17:05:56 | ---
license: mit
base_model: roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: t4ai/finetuning-classification-model-t4-roberta2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t4ai/finetuning-classification-model-t4-roberta2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2905
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16240, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.4000 | 0 |
| 0.2905 | 1 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
| 1,785 | [
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] |
GGital/CAI_Test_New_01 | 2023-10-14T18:32:09.000Z | [
"peft",
"tensorboard",
"arxiv:1910.09700",
"region:us"
] | null | GGital | null | null | GGital/CAI_Test_New_01 | 0 | 2 | peft | 2023-10-14T18:31:49 | ---
library_name: peft
base_model: openthaigpt/openthaigpt-1.0.0-beta-7b-chat-ckpt-hf
---
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Summary
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
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Barani1-t/speecht5_finetuned_voxpopuli_nl | 2023-10-14T20:07:20.000Z | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"nl",
"dataset:facebook/voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | Barani1-t | null | null | Barani1-t/speecht5_finetuned_voxpopuli_nl | 0 | 2 | transformers | 2023-10-14T19:00:25 | ---
language:
- nl
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6104
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.2269 | 666.67 | 1000 | 0.5692 |
| 0.1972 | 1333.33 | 2000 | 0.5829 |
| 0.1884 | 2000.0 | 3000 | 0.6067 |
| 0.1801 | 2666.67 | 4000 | 0.6104 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
TheBloke/tora-13B-v1.0-GPTQ | 2023-10-14T20:07:06.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"math",
"en",
"dataset:gsm8k",
"dataset:competition_math",
"arxiv:2309.17452",
"license:llama2",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/tora-13B-v1.0-GPTQ | 0 | 2 | transformers | 2023-10-14T19:30:30 | ---
base_model: llm-agents/tora-13b-v1.0
datasets:
- gsm8k
- competition_math
inference: false
language:
- en
library_name: transformers
license: llama2
metrics:
- exact_match
model_creator: LLM-Agents
model_name: ToRA 13B v1.0
model_type: llama
pipeline_tag: text-generation
prompt_template: '<|user|>
{prompt}
<|assistant|>
'
quantized_by: TheBloke
tags:
- code
- math
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# ToRA 13B v1.0 - GPTQ
- Model creator: [LLM-Agents](https://huggingface.co/llm-agents)
- Original model: [ToRA 13B v1.0](https://huggingface.co/llm-agents/tora-13b-v1.0)
<!-- description start -->
## Description
This repo contains GPTQ model files for [LLM-Agents's ToRA 13B v1.0](https://huggingface.co/llm-agents/tora-13b-v1.0).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/tora-13B-v1.0-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/tora-13B-v1.0-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/tora-13B-v1.0-GGUF)
* [LLM-Agents's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/llm-agents/tora-13b-v1.0)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ToRA
```
<|user|>
{prompt}
<|assistant|>
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/tora-13B-v1.0-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [CamelAI Math](https://huggingface.co/datasets/andersonbcdefg/math) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/tora-13B-v1.0-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [CamelAI Math](https://huggingface.co/datasets/andersonbcdefg/math) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/tora-13B-v1.0-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [CamelAI Math](https://huggingface.co/datasets/andersonbcdefg/math) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/tora-13B-v1.0-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [CamelAI Math](https://huggingface.co/datasets/andersonbcdefg/math) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/tora-13B-v1.0-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [CamelAI Math](https://huggingface.co/datasets/andersonbcdefg/math) | 4096 | 14.55 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/tora-13B-v1.0-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [CamelAI Math](https://huggingface.co/datasets/andersonbcdefg/math) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/tora-13B-v1.0-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/tora-13B-v1.0-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `tora-13B-v1.0-GPTQ`:
```shell
mkdir tora-13B-v1.0-GPTQ
huggingface-cli download TheBloke/tora-13B-v1.0-GPTQ --local-dir tora-13B-v1.0-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir tora-13B-v1.0-GPTQ
huggingface-cli download TheBloke/tora-13B-v1.0-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir tora-13B-v1.0-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir tora-13B-v1.0-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/tora-13B-v1.0-GPTQ --local-dir tora-13B-v1.0-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/tora-13B-v1.0-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/tora-13B-v1.0-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/tora-13B-v1.0-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `tora-13B-v1.0-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/tora-13B-v1.0-GPTQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|user|>
{prompt}
<|assistant|>
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/tora-13B-v1.0-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<|user|>
{prompt}
<|assistant|>
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: LLM-Agents's ToRA 13B v1.0
<h1 align="center">
ToRA: A Tool-Integrated Reasoning Agent <br> for Mathematical Problem Solving
</h1>
<p align="center">
<a href="https://microsoft.github.io/ToRA/"><b>[🌐 Website]</b></a> •
<a href="https://arxiv.org/abs/2309.17452"><b>[📜 Paper]</b></a> •
<a href="https://huggingface.co/llm-agents"><b>[🤗 HF Models]</b></a> •
<a href="https://github.com/microsoft/ToRA"><b>[🐱 GitHub]</b></a>
<br>
<a href="https://twitter.com/zhs05232838/status/1708860992631763092"><b>[🐦 Twitter]</b></a> •
<a href="https://www.reddit.com/r/LocalLLaMA/comments/1703k6d/tora_a_toolintegrated_reasoning_agent_for/"><b>[💬 Reddit]</b></a> •
<a href="https://notes.aimodels.fyi/researchers-announce-tora-training-language-models-to-better-understand-math-using-external-tools/">[🍀 Unofficial Blog]</a>
<!-- <a href="#-quick-start">Quick Start</a> • -->
<!-- <a href="#%EF%B8%8F-citation">Citation</a> -->
</p>
<p align="center">
Repo for "<a href="https://arxiv.org/abs/2309.17452" target="_blank">ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving</a>"
</p>
## 🔥 News
- [2023/10/08] 🔥🔥🔥 All ToRA models released at [HuggingFace](https://huggingface.co/llm-agents)!!!
- [2023/09/29] ToRA paper, repo, and website released.
## 💡 Introduction
ToRA is a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical reasoning problems by interacting with tools, e.g., computation libraries and symbolic solvers. ToRA series seamlessly integrate natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of external tools.
| Model | Size | GSM8k | MATH | AVG@10 math tasks<sup>†</sup> |
|---|---|---|---|---|
| GPT-4 | - | 92.0 | 42.5 | 78.3 |
| GPT-4 (PAL) | - | 94.2 | 51.8 | 86.4 |
| [ToRA-7B](https://huggingface.co/llm-agents/tora-7b-v1.0) | 7B | 68.8 | 40.1 | 62.4|
| [ToRA-Code-7B](https://huggingface.co/llm-agents/tora-code-7b-v1.0) | 7B | 72.6 | 44.6 | 66.5|
| [ToRA-13B](https://huggingface.co/llm-agents/tora-13b-v1.0) | 13B | 72.7 | 43.0 | 65.9|
| [ToRA-Code-13B](https://huggingface.co/llm-agents/tora-code-13b-v1.0) | 13B | 75.8 | 48.1 | 71.3 |
| [ToRA-Code-34B<sup>*</sup>](https://huggingface.co/llm-agents/tora-code-34b-v1.0) | 34B | 80.7 | **51.0** | 74.8 |
| [ToRA-70B](https://huggingface.co/llm-agents/tora-70b-v1.0) | 70B | **84.3** | 49.7 | **76.9** |
- <sup>*</sup>ToRA-Code-34B is currently the first and only open-source model to achieve over 50% accuracy (pass@1) on the MATH dataset, which significantly outperforms GPT-4’s CoT result (51.0 vs. 42.5), and is competitive with GPT-4 solving problems with programs. By open-sourcing our codes and models, we hope more breakthroughs will come!
- <sup>†</sup>10 math tasks include GSM8k, MATH, GSM-Hard, SVAMP, TabMWP, ASDiv, SingleEQ, SingleOP, AddSub, and MultiArith.
## ⚡️ Training
The models are trained on ToRA-Corpus 16k, which contains tool-integrated reasoning trajectories of MATH and GSM8k from GPT-4.
We use imitation learning (i.e., SFT) to fine-tune the models, and then apply our proposed *output space shaping* to improve tool-integrated reasoning behaviors. Please refer to the [paper](https://arxiv.org/pdf/2309.17452.pdf) for more details.
## 🪁 Inference & Evaluation
Please refer to ToRA's [GitHub repo](https://github.com/microsoft/ToRA) for inference, evaluation, and training code.
## ☕️ Citation
If you find this repository helpful, please consider citing our paper:
```
@misc{gou2023tora,
title={ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving},
author={Zhibin Gou and Zhihong Shao and Yeyun Gong and yelong shen and Yujiu Yang and Minlie Huang and Nan Duan and Weizhu Chen},
year={2023},
eprint={2309.17452},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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spadini/meme-class | 2023-10-14T19:48:52.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | spadini | null | null | spadini/meme-class | 0 | 2 | transformers | 2023-10-14T19:44:58 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: meme-class
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6883116960525513
---
# meme-class
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Carreta Furacao
#### Eu sou rica
#### Meme Doge
#### Nazare Confusa
#### Risitas
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Akshay0706/Plant-Diseases-Classification-Training-Arguments | 2023-10-14T21:38:07.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | Akshay0706 | null | null | Akshay0706/Plant-Diseases-Classification-Training-Arguments | 0 | 2 | transformers | 2023-10-14T21:37:29 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: Plant-Diseases-Classification-Training-Arguments
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Plant-Diseases-Classification-Training-Arguments
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 26 | 0.4907 | 0.9524 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
galbitang/autotrain-ijeongmi_lamp_final-95169146297 | 2023-10-14T21:52:06.000Z | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"autotrain",
"vision",
"dataset:galbitang/autotrain-data-ijeongmi_lamp_final",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | galbitang | null | null | galbitang/autotrain-ijeongmi_lamp_final-95169146297 | 0 | 2 | transformers | 2023-10-14T21:45:43 | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- galbitang/autotrain-data-ijeongmi_lamp_final
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 2.397022420581575
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95169146297
- CO2 Emissions (in grams): 2.3970
## Validation Metrics
- Loss: 1.042
- Accuracy: 0.655
- Macro F1: 0.563
- Micro F1: 0.655
- Weighted F1: 0.646
- Macro Precision: 0.602
- Micro Precision: 0.655
- Weighted Precision: 0.652
- Macro Recall: 0.552
- Micro Recall: 0.655
- Weighted Recall: 0.655 | 889 | [
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Cesar42/TrainLlama2Dataset2 | 2023-10-14T22:39:44.000Z | [
"tensorboard",
"autotrain",
"text-generation",
"region:us"
] | text-generation | Cesar42 | null | null | Cesar42/TrainLlama2Dataset2 | 0 | 2 | null | 2023-10-14T22:15:17 | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
---
# Model Trained Using AutoTrain | 120 | [
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pkhanna2/my_awesome_qa_model | 2023-10-14T22:27:50.000Z | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | pkhanna2 | null | null | pkhanna2/my_awesome_qa_model | 0 | 2 | transformers | 2023-10-14T22:25:52 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7909
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 38 | 4.4095 |
| No log | 2.0 | 76 | 3.8866 |
| No log | 3.0 | 114 | 3.7909 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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adityarra07/whisper-medium-ft-GPT | 2023-10-14T23:31:41.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | adityarra07 | null | null | adityarra07/whisper-medium-ft-GPT | 0 | 2 | transformers | 2023-10-14T22:39:23 | ---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-ft-GPT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-ft-GPT
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4126
- Wer: 16.1319
- Gpt: 8.1573
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Gpt |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|
| 1.4571 | 1.0 | 63 | 0.4337 | 33.9572 | 8.0230 |
| 0.176 | 2.0 | 126 | 0.3830 | 31.2834 | 7.9877 |
| 0.0563 | 3.0 | 189 | 0.3942 | 16.5775 | 8.1149 |
| 0.0192 | 4.0 | 252 | 0.4223 | 15.6863 | 7.9620 |
| 0.0079 | 5.0 | 315 | 0.4049 | 15.5080 | 7.6897 |
| 0.0023 | 6.0 | 378 | 0.4126 | 16.1319 | 8.1573 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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1TuanPham/Instruction_tune_8k_e3_en-vi_EleutherAI_pythia-410m-deduped-v0_LORA_CAUSAL_LM | 2023-10-15T04:40:13.000Z | [
"peft",
"region:us"
] | null | 1TuanPham | null | null | 1TuanPham/Instruction_tune_8k_e3_en-vi_EleutherAI_pythia-410m-deduped-v0_LORA_CAUSAL_LM | 0 | 2 | peft | 2023-10-15T03:15:56 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: True
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
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LoneStriker/Mistral-11B-CC-Air-RP-3.0bpw-h6-exl2 | 2023-10-15T03:33:00.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"pretrained",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/Mistral-11B-CC-Air-RP-3.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-15T03:32:35 | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
- mistral
- pretrained
---
CollectiveCognition-v1.1-Mistral-7B and airoboros-mistral2.2-7b glued together and finetuned with qlora of Pippa and LimaRPv3 dataset.
<!-- description start -->
## Description
This repo contains fp16 files of Mistral-11B-CC-Air-RP.
<!-- description end -->
<!-- description start -->
## Model used
- [CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B)
- [airoboros-mistral2.2-7b](https://huggingface.co/teknium/airoboros-mistral2.2-7b/)
- PIPPA dataset 11B qlora
- LimaRPv3 dataset 11B qlora
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca or default
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
```
USER: <prompt>
ASSISTANT:
```
## The secret sauce
```
slices:
- sources:
- model: teknium/CollectiveCognition-v1.1-Mistral-7B
layer_range: [0, 24]
- sources:
- model: teknium/airoboros-mistral2.2-7b
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
```
Special thanks to Sushi.
If you want to support me, you can [here](https://ko-fi.com/undiai). | 1,287 | [
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FredNajjar/bigbird-QA-squad_v2.2 | 2023-10-15T04:59:20.000Z | [
"transformers",
"pytorch",
"big_bird",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | FredNajjar | null | null | FredNajjar/bigbird-QA-squad_v2.2 | 0 | 2 | transformers | 2023-10-15T03:45:06 | ---
license: apache-2.0
base_model: google/bigbird-roberta-base
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bigbird-QA-squad_v2.2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bigbird-QA-squad_v2.2
This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8585
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 121
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0955 | 1.0 | 814 | 0.9719 |
| 0.8505 | 2.0 | 1629 | 0.8657 |
| 0.6993 | 3.0 | 2442 | 0.8585 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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bnunticha/seq-classification-demo | 2023-10-15T15:19:34.000Z | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | bnunticha | null | null | bnunticha/seq-classification-demo | 0 | 2 | transformers | 2023-10-15T05:04:23 | ---
base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: seq-classification-demo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# seq-classification-demo
This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2038
- Accuracy: 0.9199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2244 | 1.0 | 13509 | 0.2187 | 0.9153 |
| 0.2004 | 2.0 | 27018 | 0.2038 | 0.9199 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Supersaiyan1729/mistrail_9_epichs_dolly | 2023-10-15T06:19:13.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | Supersaiyan1729 | null | null | Supersaiyan1729/mistrail_9_epichs_dolly | 0 | 2 | peft | 2023-10-15T06:19:07 | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
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## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
| 5,072 | [
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FredNajjar/bigbird-QA-squad_v2.4 | 2023-10-15T08:05:39.000Z | [
"transformers",
"pytorch",
"big_bird",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | FredNajjar | null | null | FredNajjar/bigbird-QA-squad_v2.4 | 0 | 2 | transformers | 2023-10-15T06:31:47 | ---
license: apache-2.0
base_model: google/bigbird-roberta-base
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: bigbird-QA-squad_v2.4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bigbird-QA-squad_v2.4
This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8951
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 244
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3245 | 1.0 | 407 | 1.1746 |
| 1.0089 | 2.0 | 814 | 0.9133 |
| 0.8408 | 3.0 | 1221 | 0.8951 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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allspace/distilbert-base-uncased-finetuned-emotion | 2023-10-16T03:23:30.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | allspace | null | null | allspace/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-10-15T12:42:03 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9264148990589147
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2191
- Accuracy: 0.9265
- F1: 0.9264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3143 | 0.907 | 0.9060 |
| No log | 2.0 | 500 | 0.2191 | 0.9265 | 0.9264 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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buianh0803/text-sum-3 | 2023-10-15T18:49:54.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | buianh0803 | null | null | buianh0803/text-sum-3 | 0 | 2 | transformers | 2023-10-15T14:03:35 | ---
license: apache-2.0
base_model: buianh0803/text-sum-2
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: text-sum-3
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
config: 3.0.0
split: test
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 0.2475
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# text-sum-3
This model is a fine-tuned version of [buianh0803/text-sum-2](https://huggingface.co/buianh0803/text-sum-2) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6546
- Rouge1: 0.2475
- Rouge2: 0.1177
- Rougel: 0.2051
- Rougelsum: 0.2051
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.8082 | 1.0 | 17945 | 1.6546 | 0.2475 | 0.1177 | 0.2051 | 0.2051 | 19.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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dima806/133_dog_breeds_image_detection | 2023-10-15T14:54:28.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | dima806 | null | null | dima806/133_dog_breeds_image_detection | 0 | 2 | transformers | 2023-10-15T14:52:05 | ---
license: apache-2.0
metrics:
- accuracy
- f1
---
See https://www.kaggle.com/code/dima806/133-dog-breed-image-detection-vit for more details. | 144 | [
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galbitang/autotrain-lamp_1015-95249146314 | 2023-10-15T15:15:15.000Z | [
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"autotrain",
"vision",
"dataset:galbitang/autotrain-data-lamp_1015",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | galbitang | null | null | galbitang/autotrain-lamp_1015-95249146314 | 0 | 2 | transformers | 2023-10-15T15:07:40 | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- galbitang/autotrain-data-lamp_1015
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.05129502913184454
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 95249146314
- CO2 Emissions (in grams): 0.0513
## Validation Metrics
- Loss: 1.035
- Accuracy: 0.660
- Macro F1: 0.478
- Micro F1: 0.660
- Weighted F1: 0.624
- Macro Precision: 0.525
- Micro Precision: 0.660
- Weighted Precision: 0.614
- Macro Recall: 0.490
- Micro Recall: 0.660
- Weighted Recall: 0.660 | 881 | [
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laksh688/my_first_repo | 2023-10-15T15:51:12.000Z | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | laksh688 | null | null | laksh688/my_first_repo | 0 | 2 | sentence-transformers | 2023-10-15T15:39:49 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# laksh688/my_first_repo
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("laksh688/my_first_repo")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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btoscano/eminem_asr_mind_model | 2023-10-15T22:40:03.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | btoscano | null | null | btoscano/eminem_asr_mind_model | 0 | 2 | transformers | 2023-10-15T20:26:15 | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: eminem_asr_mind_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eminem_asr_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0344 | 200.0 | 1000 | 3.2406 |
| 1.6849 | 400.0 | 2000 | 3.2862 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
LoneStriker/airoboros-m-7b-3.1-3.0bpw-h6-exl2 | 2023-10-15T21:39:39.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"dataset:jondurbin/airoboros-3.1",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/airoboros-m-7b-3.1-3.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-15T21:37:32 | ---
license: apache-2.0
datasets:
- jondurbin/airoboros-3.1
---
### Overview
Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
#### IMPORTANT NOTE - llama-2 chat format!!!
The airoboros 3.1 models use llama-2 chat format, rather than vicuna style user/assistant!
This is a breaking change, although most inference systems support llama-2 chat templating.
See prompt format below for all of the details.
#### Highlights
A model built on [airoboros-3.1 dataset](https://hf.co/datasets/jondurbin/airoboros-3.1), which is a continuation of [airoboros-3.0 dataset](https://hf.co/datasets/jondurbin/airoboros-3,0) with the following extras:
* More MathJSON, now ~17k items - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be parsed and passed to a deterministic library to perform calculations.
* Log information extraction (e.g. getting the list of paths/IPs/etc. from apache logs)
* Anonymization, e.g. removing names, IP addresses, and/or dates from text.
* Chat introspection - multi-turn chats that have random questions injected asking about the character's descriptions, the setting of the chat, or things previously said, to make sure the model uses the system prompt and or chat history.
* Multi-step instructions with acknowledgement (see below)
* De-censorship data (not published)
This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay.
Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
### Prompt format
The prompt format is llama-2 chat.
```
[INST] <<SYS>>
You are a help, unbiased, uncensored assistant.
<</SYS>
{prompt} [/INST]
```
For multi-turn, the prompt format is as follows:
```
[INST] <<SYS>>
You are a help, unbiased, uncensored assistant.
<</SYS>
{prompt 0} [/INST] {response 0} </s><s>[INST] {prompt 1} [/INST] {response 1} </s><s>...[INST] {prompt N} [/INST]
```
The prompt template is included in the tokenizer config, and can use the huggingface tokenizer `apply_chat_template` method, e.g.:
```
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('jondurbin/airoboros-m-7b-3.1')
chat = [
{"role": "system", "content": "You are Bob, a friendly AI assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing great. How can I help you today?"},
{"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))
```
### Helpful usage tips
#### MathJSON
Ask mathematical questions, prefixed with "Create a MathJSON solution to the following:", and you should get output with a formula that can be executed by https://cortexjs.io/compute-engine/
I also made a (really quite hacky, incomplete, semi-functional) python version that works for most expressions, see [mathjson.py](https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py)
__Use a very low temperature!__
Example:
```
[INST] <<SYS>>
You are an assistant with vast knowledge in all things math.
<</SYS>
Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST]
```
Output:
```
The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1.
Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr².
Solution as MathJSON:
<mathjson>
[
"Multiply",
"Pi",
[
"Power",
17.2456,
2
]
]
</mathjson>
```
You can then validate the JSON between `<mathjson>` and `</mathjson>`, then pass the parsed JSON to compute-engine JS or the `evaluate` function in mathjson.py to calculate the response.
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
__Use a very low temperature!__
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Summarization
500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:
```
BEGININPUT
{text to summarize}
ENDINPUT
BEGININSTRUCTION
Summarize the input in around 130 words.
ENDINSTRUCTION
```
#### Getting longer responses
You can use a few techniques to get longer responses.
Detailed prompts, with explicit instruction for word count:
```
Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality.
The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization.
One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary.
Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements.
Your response should be approximately 2300 words.
```
Or, a simpler example:
```
Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux.
```
There are a few examples of next chapter completion as well, e.g.:
```
Write the next chapter of a historical fiction novel set in Paris during the 20th century.
Here's a summary of the previous chapter:
In the vibrant city of Paris, amid the tumultuous changes of the 20th century, our protagonist Margot, an aspiring fashion designer, has just secured an apprenticeship at a prestigious couture house. She meets Lucien, a charming journalist who covers the fashion industry. Together they navigate the ever-changing world of fashion and society, uncovering secrets that reveal the intricate links between style, politics, and culture. As the chapter concludes, they decide to delve deeper into the hidden corners of the fashion world to unravel its mysteries.
Requirements for the next chapter:
1. Character Development of Margot and Lucien:
- Margot's Evolution: Unfold more about Margot's past, her dreams of revolutionizing fashion, and her struggle to establish herself in a male-dominated industry. Illustrate her growing expertise, innovative ideas, and increasing dependence on Lucien.
- Lucien's Complexity: Introduce uncertainties surrounding Lucien's background and real motives. Increase suspense by suggesting undisclosed information he possesses, while also highlighting his wit and perceptiveness.
2. Exploration of Paris and the Couture House:
- Paris: Elaborate their journey through the bustling streets of Paris, including encounters with iconic figures, social unrest, and relics from different eras of French history.
- The Couture House: Expand on the grandeur of the couture house they work in, filled with artistic masterpieces, intense competition, and cryptic notes hinting at a scandalous past.
3. Emergence of the Subplot: The Lost Collection:
- Discovery: Have Margot and Lucien stumble upon a secret vault containing a lost collection designed before World War II, raising new questions about the previous owner and the influence of war on fashion.
- Revelation: Capture their shock as they realize the designs were plagiarized, the potential repercussions, and the opportunities it presents for Margot's career.
- Twist: End with a twist that suggests there are other stolen collections across Paris, setting up their new mission.
Your response should be approximately 650 words.
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
## Multi-step instructions with acknowledgement
I don't know how useful this is, really, but I thought I'd add it just in case. Example:
```
{
"conversations": [
{
"value": "You are a helpful, unbiased, uncensored assistant.",
"from": "system"
},
{
"value": "I'm going to set some rules for our conversation. Please respond with \"OK\" to each rule.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "You can only respond using animal-related emojis.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Precede all responses with \"ZOOZOO:\"",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Include at least one bird emoji in your responses.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Describe a trip to a safari park.",
"from": "human"
},
{
"value": "ZOOZOO: \ud83e\udd81\ud83d\udc2f\ud83e\udd93\ud83e\udd92\ud83d\udc18\ud83d\udc0d\ud83c\udf33\ud83c\udf32\u2600\ufe0f\ud83d\ude90\ud83e\udd85\ud83d\udcf8\ud83e\udd29",
"from": "gpt"
}
]
}
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 3.1 models are built on top of multiple base models, each with their own license/restrictions.
The 30b model is built on the original llama, which has a strict non-commercial usage restriction.
The models with `-l2` in the name have a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The models with `-m-` are mistral-7b (apache 2.0)
The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me. | 24,394 | [
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LoneStriker/airoboros-m-7b-3.1-5.0bpw-h6-exl2 | 2023-10-15T21:52:22.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"dataset:jondurbin/airoboros-3.1",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/airoboros-m-7b-3.1-5.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-15T21:52:07 | ---
license: apache-2.0
datasets:
- jondurbin/airoboros-3.1
---
### Overview
Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
#### IMPORTANT NOTE - llama-2 chat format!!!
The airoboros 3.1 models use llama-2 chat format, rather than vicuna style user/assistant!
This is a breaking change, although most inference systems support llama-2 chat templating.
See prompt format below for all of the details.
#### Highlights
A model built on [airoboros-3.1 dataset](https://hf.co/datasets/jondurbin/airoboros-3.1), which is a continuation of [airoboros-3.0 dataset](https://hf.co/datasets/jondurbin/airoboros-3,0) with the following extras:
* More MathJSON, now ~17k items - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be parsed and passed to a deterministic library to perform calculations.
* Log information extraction (e.g. getting the list of paths/IPs/etc. from apache logs)
* Anonymization, e.g. removing names, IP addresses, and/or dates from text.
* Chat introspection - multi-turn chats that have random questions injected asking about the character's descriptions, the setting of the chat, or things previously said, to make sure the model uses the system prompt and or chat history.
* Multi-step instructions with acknowledgement (see below)
* De-censorship data (not published)
This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay.
Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
### Prompt format
The prompt format is llama-2 chat.
```
[INST] <<SYS>>
You are a help, unbiased, uncensored assistant.
<</SYS>
{prompt} [/INST]
```
For multi-turn, the prompt format is as follows:
```
[INST] <<SYS>>
You are a help, unbiased, uncensored assistant.
<</SYS>
{prompt 0} [/INST] {response 0} </s><s>[INST] {prompt 1} [/INST] {response 1} </s><s>...[INST] {prompt N} [/INST]
```
The prompt template is included in the tokenizer config, and can use the huggingface tokenizer `apply_chat_template` method, e.g.:
```
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('jondurbin/airoboros-m-7b-3.1')
chat = [
{"role": "system", "content": "You are Bob, a friendly AI assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing great. How can I help you today?"},
{"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))
```
### Helpful usage tips
#### MathJSON
Ask mathematical questions, prefixed with "Create a MathJSON solution to the following:", and you should get output with a formula that can be executed by https://cortexjs.io/compute-engine/
I also made a (really quite hacky, incomplete, semi-functional) python version that works for most expressions, see [mathjson.py](https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py)
__Use a very low temperature!__
Example:
```
[INST] <<SYS>>
You are an assistant with vast knowledge in all things math.
<</SYS>
Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST]
```
Output:
```
The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1.
Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr².
Solution as MathJSON:
<mathjson>
[
"Multiply",
"Pi",
[
"Power",
17.2456,
2
]
]
</mathjson>
```
You can then validate the JSON between `<mathjson>` and `</mathjson>`, then pass the parsed JSON to compute-engine JS or the `evaluate` function in mathjson.py to calculate the response.
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
__Use a very low temperature!__
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Summarization
500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:
```
BEGININPUT
{text to summarize}
ENDINPUT
BEGININSTRUCTION
Summarize the input in around 130 words.
ENDINSTRUCTION
```
#### Getting longer responses
You can use a few techniques to get longer responses.
Detailed prompts, with explicit instruction for word count:
```
Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality.
The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization.
One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary.
Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements.
Your response should be approximately 2300 words.
```
Or, a simpler example:
```
Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux.
```
There are a few examples of next chapter completion as well, e.g.:
```
Write the next chapter of a historical fiction novel set in Paris during the 20th century.
Here's a summary of the previous chapter:
In the vibrant city of Paris, amid the tumultuous changes of the 20th century, our protagonist Margot, an aspiring fashion designer, has just secured an apprenticeship at a prestigious couture house. She meets Lucien, a charming journalist who covers the fashion industry. Together they navigate the ever-changing world of fashion and society, uncovering secrets that reveal the intricate links between style, politics, and culture. As the chapter concludes, they decide to delve deeper into the hidden corners of the fashion world to unravel its mysteries.
Requirements for the next chapter:
1. Character Development of Margot and Lucien:
- Margot's Evolution: Unfold more about Margot's past, her dreams of revolutionizing fashion, and her struggle to establish herself in a male-dominated industry. Illustrate her growing expertise, innovative ideas, and increasing dependence on Lucien.
- Lucien's Complexity: Introduce uncertainties surrounding Lucien's background and real motives. Increase suspense by suggesting undisclosed information he possesses, while also highlighting his wit and perceptiveness.
2. Exploration of Paris and the Couture House:
- Paris: Elaborate their journey through the bustling streets of Paris, including encounters with iconic figures, social unrest, and relics from different eras of French history.
- The Couture House: Expand on the grandeur of the couture house they work in, filled with artistic masterpieces, intense competition, and cryptic notes hinting at a scandalous past.
3. Emergence of the Subplot: The Lost Collection:
- Discovery: Have Margot and Lucien stumble upon a secret vault containing a lost collection designed before World War II, raising new questions about the previous owner and the influence of war on fashion.
- Revelation: Capture their shock as they realize the designs were plagiarized, the potential repercussions, and the opportunities it presents for Margot's career.
- Twist: End with a twist that suggests there are other stolen collections across Paris, setting up their new mission.
Your response should be approximately 650 words.
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
## Multi-step instructions with acknowledgement
I don't know how useful this is, really, but I thought I'd add it just in case. Example:
```
{
"conversations": [
{
"value": "You are a helpful, unbiased, uncensored assistant.",
"from": "system"
},
{
"value": "I'm going to set some rules for our conversation. Please respond with \"OK\" to each rule.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "You can only respond using animal-related emojis.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Precede all responses with \"ZOOZOO:\"",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Include at least one bird emoji in your responses.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Describe a trip to a safari park.",
"from": "human"
},
{
"value": "ZOOZOO: \ud83e\udd81\ud83d\udc2f\ud83e\udd93\ud83e\udd92\ud83d\udc18\ud83d\udc0d\ud83c\udf33\ud83c\udf32\u2600\ufe0f\ud83d\ude90\ud83e\udd85\ud83d\udcf8\ud83e\udd29",
"from": "gpt"
}
]
}
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 3.1 models are built on top of multiple base models, each with their own license/restrictions.
The 30b model is built on the original llama, which has a strict non-commercial usage restriction.
The models with `-l2` in the name have a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The models with `-m-` are mistral-7b (apache 2.0)
The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me. | 24,394 | [
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LoneStriker/SynthIA-7B-v1.5-5.0bpw-h6-exl2 | 2023-10-15T23:36:10.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/SynthIA-7B-v1.5-5.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-15T23:35:55 | ---
license: apache-2.0
pipeline_tag: text-generation
language:
- en
library_name: transformers
---
<br>
<br>
## Example Usage
### Prompt format:
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: How is a rocket launched from the surface of the earth to Low Earth Orbit?
ASSISTANT:
```
### Code example:
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "migtissera/Tay-Lite"
output_file_path = "./Tay-Lite-conversations.jsonl"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
```
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-1 | 2023-10-16T01:51:56.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-1 | 0 | 2 | transformers | 2023-10-16T01:35:58 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-1
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5348
- F1: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 0.6898 | 0.5789 |
| No log | 2.0 | 136 | 0.6192 | 0.5 |
| No log | 3.0 | 204 | 0.4902 | 0.6364 |
| No log | 4.0 | 272 | 0.6112 | 0.6364 |
| No log | 5.0 | 340 | 1.0433 | 0.5946 |
| No log | 6.0 | 408 | 0.8049 | 0.6667 |
| No log | 7.0 | 476 | 1.1874 | 0.7200 |
| 0.5 | 8.0 | 544 | 1.6794 | 0.5263 |
| 0.5 | 9.0 | 612 | 1.4536 | 0.7200 |
| 0.5 | 10.0 | 680 | 1.5348 | 0.6667 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/chinese-bigbird-wwm-base-4096-wallstreetcn-morning-news-market-overview-SSEC-f1-v7 | 2023-10-16T02:12:37.000Z | [
"transformers",
"pytorch",
"big_bird",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/chinese-bigbird-wwm-base-4096-wallstreetcn-morning-news-market-overview-SSEC-f1-v7 | 0 | 2 | transformers | 2023-10-16T01:55:29 | ---
license: apache-2.0
base_model: Lowin/chinese-bigbird-wwm-base-4096
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: chinese-bigbird-wwm-base-4096-wallstreetcn-morning-news-market-overview-SSEC-f1-v7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chinese-bigbird-wwm-base-4096-wallstreetcn-morning-news-market-overview-SSEC-f1-v7
This model is a fine-tuned version of [Lowin/chinese-bigbird-wwm-base-4096](https://huggingface.co/Lowin/chinese-bigbird-wwm-base-4096) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3769
- F1: 0.6452
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 34 | 0.6979 | 0.5652 |
| No log | 2.0 | 68 | 0.6272 | 0.0 |
| No log | 3.0 | 102 | 0.4774 | 0.7500 |
| No log | 4.0 | 136 | 0.6090 | 0.6286 |
| No log | 5.0 | 170 | 0.5318 | 0.6667 |
| No log | 6.0 | 204 | 0.9359 | 0.5714 |
| No log | 7.0 | 238 | 1.0927 | 0.5714 |
| No log | 8.0 | 272 | 1.3554 | 0.6000 |
| No log | 9.0 | 306 | 1.3537 | 0.6000 |
| No log | 10.0 | 340 | 1.3769 | 0.6452 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-2 | 2023-10-16T02:15:25.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-2 | 0 | 2 | transformers | 2023-10-16T01:59:00 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-2
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4955
- F1: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 1.9106 | 0.6207 |
| No log | 2.0 | 136 | 1.8900 | 0.6429 |
| No log | 3.0 | 204 | 1.8897 | 0.6667 |
| No log | 4.0 | 272 | 3.0591 | 0.5806 |
| No log | 5.0 | 340 | 2.3486 | 0.6364 |
| No log | 6.0 | 408 | 2.4041 | 0.6364 |
| No log | 7.0 | 476 | 2.5082 | 0.6667 |
| 0.0772 | 8.0 | 544 | 2.5307 | 0.6667 |
| 0.0772 | 9.0 | 612 | 2.5782 | 0.6667 |
| 0.0772 | 10.0 | 680 | 2.4955 | 0.6667 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-3 | 2023-10-16T02:39:48.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-3 | 0 | 2 | transformers | 2023-10-16T02:22:45 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-3
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6031
- F1: 0.6923
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 2.1617 | 0.6957 |
| No log | 2.0 | 136 | 2.8824 | 0.6154 |
| No log | 3.0 | 204 | 3.4616 | 0.6207 |
| No log | 4.0 | 272 | 3.2955 | 0.6429 |
| No log | 5.0 | 340 | 2.7190 | 0.64 |
| No log | 6.0 | 408 | 3.0961 | 0.6667 |
| No log | 7.0 | 476 | 2.9418 | 0.6429 |
| 0.037 | 8.0 | 544 | 2.4992 | 0.5714 |
| 0.037 | 9.0 | 612 | 2.4335 | 0.7200 |
| 0.037 | 10.0 | 680 | 2.6031 | 0.6923 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-4 | 2023-10-16T03:03:53.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-4 | 0 | 2 | transformers | 2023-10-16T02:47:01 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-4
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2103
- F1: 0.5455
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 4.3146 | 0.5806 |
| No log | 2.0 | 136 | 3.2588 | 0.6207 |
| No log | 3.0 | 204 | 2.9169 | 0.64 |
| No log | 4.0 | 272 | 2.6884 | 0.6923 |
| No log | 5.0 | 340 | 3.0452 | 0.4762 |
| No log | 6.0 | 408 | 2.9565 | 0.5455 |
| No log | 7.0 | 476 | 2.9711 | 0.6087 |
| 0.043 | 8.0 | 544 | 3.0785 | 0.6087 |
| 0.043 | 9.0 | 612 | 3.1123 | 0.5833 |
| 0.043 | 10.0 | 680 | 3.2103 | 0.5455 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
| 1,980 | [
[
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Muzzi/eli5 | 2023-10-16T02:49:34.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:eli5",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Muzzi | null | null | Muzzi/eli5 | 0 | 2 | transformers | 2023-10-16T02:48:27 | ---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- eli5
metrics:
- rouge
model-index:
- name: eli5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eli5
type: eli5
config: LFQA_reddit
split: validation_eli5
args: LFQA_reddit
metrics:
- name: Rouge1
type: rouge
value: 14.6325
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eli5
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2569
- Rouge1: 14.6325
- Rouge2: 2.3714
- Rougel: 11.2941
- Rougelsum: 13.2006
- Gen Len: 18.9911
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.4057 | 1.0 | 34080 | 2.2708 | 14.6356 | 2.3501 | 11.3428 | 13.213 | 18.9946 |
| 2.3943 | 2.0 | 68160 | 2.2569 | 14.6325 | 2.3714 | 11.2941 | 13.2006 | 18.9911 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
astrid01052/test-platypus | 2023-10-16T03:07:45.000Z | [
"peft",
"region:us"
] | null | astrid01052 | null | null | astrid01052/test-platypus | 0 | 2 | peft | 2023-10-16T03:07:06 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-5 | 2023-10-16T03:28:25.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-5 | 0 | 2 | transformers | 2023-10-16T03:11:03 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-5
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1989
- F1: 0.64
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 6.7804 | 0.5854 |
| No log | 2.0 | 136 | 3.2109 | 0.6154 |
| No log | 3.0 | 204 | 3.6885 | 0.5333 |
| No log | 4.0 | 272 | 3.0005 | 0.5833 |
| No log | 5.0 | 340 | 3.1613 | 0.6429 |
| No log | 6.0 | 408 | 3.1637 | 0.6667 |
| No log | 7.0 | 476 | 3.0745 | 0.6667 |
| 0.0457 | 8.0 | 544 | 3.0860 | 0.6667 |
| 0.0457 | 9.0 | 612 | 3.2027 | 0.64 |
| 0.0457 | 10.0 | 680 | 3.1989 | 0.64 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-7 | 2023-10-16T04:17:03.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-7 | 0 | 2 | transformers | 2023-10-16T03:59:49 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-7
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1704
- F1: 0.5455
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 3.9296 | 0.6923 |
| No log | 2.0 | 136 | 4.0656 | 0.6923 |
| No log | 3.0 | 204 | 4.5348 | 0.3529 |
| No log | 4.0 | 272 | 3.7409 | 0.625 |
| No log | 5.0 | 340 | 3.4809 | 0.5714 |
| No log | 6.0 | 408 | 3.2944 | 0.5926 |
| No log | 7.0 | 476 | 3.6049 | 0.5217 |
| 0.0628 | 8.0 | 544 | 3.1942 | 0.5217 |
| 0.0628 | 9.0 | 612 | 3.2339 | 0.5455 |
| 0.0628 | 10.0 | 680 | 3.1704 | 0.5455 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-8 | 2023-10-16T04:41:06.000Z | [
"transformers",
"pytorch",
"bart",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | text-classification | hw2942 | null | null | hw2942/bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-8 | 0 | 2 | transformers | 2023-10-16T04:24:14 | ---
base_model: fnlp/bart-base-chinese
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-chinese-wallstreetcn-morning-news-market-overview-SSE50-f1-8
This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1294
- F1: 0.5600
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 68 | 4.0237 | 0.6923 |
| No log | 2.0 | 136 | 4.1421 | 0.6923 |
| No log | 3.0 | 204 | 4.7483 | 0.5385 |
| No log | 4.0 | 272 | 3.9897 | 0.5000 |
| No log | 5.0 | 340 | 4.4233 | 0.5000 |
| No log | 6.0 | 408 | 3.9284 | 0.5385 |
| No log | 7.0 | 476 | 3.9963 | 0.5600 |
| 0.055 | 8.0 | 544 | 4.1173 | 0.5600 |
| 0.055 | 9.0 | 612 | 4.1088 | 0.5600 |
| 0.055 | 10.0 | 680 | 4.1294 | 0.5600 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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]
] |
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