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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LoneStriker/SynthIA-70B-v1.5-3.0bpw-h6-exl2 | 2023-10-26T00:53:43.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/SynthIA-70B-v1.5-3.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-26T00:51:43 | ---
license: llama2
---
## 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/Synthia-70B-v1.5"
output_file_path = "./Synthia-70B-v1.5-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")
``` | 2,296 | [
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syafiqfaray/byt5-base-indocollex-informal-to-formal-wordformation | 2023-10-26T03:20:12.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | syafiqfaray | null | null | syafiqfaray/byt5-base-indocollex-informal-to-formal-wordformation | 0 | 2 | transformers | 2023-10-26T03:18:58 | ---
license: apache-2.0
base_model: google/byt5-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: byt5-base-indocollex-informal-to-formal-wordformation
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. -->
# byt5-base-indocollex-informal-to-formal-wordformation
This model is a fine-tuned version of [google/byt5-base](https://huggingface.co/google/byt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1413
- Cer: 0.1978
- Wer: 0.4524
- Word Acc: 0.5476
- Gen Len: 7.6457
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer | Word Acc | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:--------:|:-------:|
| No log | 0.54 | 50 | 16.1894 | 2.1868 | 2.2905 | -1.2905 | 19.0 |
| No log | 1.08 | 100 | 13.7479 | 2.1248 | 1.9333 | -0.9333 | 19.0 |
| No log | 1.61 | 150 | 11.6231 | 2.1095 | 1.4238 | -0.4238 | 18.7486 |
| No log | 2.15 | 200 | 8.9106 | 1.056 | 0.9857 | 0.0143 | 10.6171 |
| No log | 2.69 | 250 | 4.6844 | 0.8523 | 0.9762 | 0.0238 | 9.36 |
| No log | 3.23 | 300 | 4.1175 | 0.5756 | 0.9714 | 0.0286 | 7.4114 |
| No log | 3.76 | 350 | 3.3688 | 0.5951 | 0.9714 | 0.0286 | 7.8 |
| No log | 4.3 | 400 | 2.2287 | 0.6112 | 0.9857 | 0.0143 | 6.7543 |
| No log | 4.84 | 450 | 1.5164 | 0.6095 | 0.9571 | 0.0429 | 7.8857 |
| 8.4834 | 5.38 | 500 | 1.0363 | 0.5976 | 0.9476 | 0.0524 | 7.8229 |
| 8.4834 | 5.91 | 550 | 0.6893 | 0.5976 | 0.9476 | 0.0524 | 7.7943 |
| 8.4834 | 6.45 | 600 | 0.5438 | 0.5866 | 0.9381 | 0.0619 | 7.9943 |
| 8.4834 | 6.99 | 650 | 0.4720 | 0.5806 | 0.9333 | 0.0667 | 8.0057 |
| 8.4834 | 7.53 | 700 | 0.4305 | 0.5764 | 0.9333 | 0.0667 | 8.0057 |
| 8.4834 | 8.06 | 750 | 0.3931 | 0.5654 | 0.9333 | 0.0667 | 8.2971 |
| 8.4834 | 8.6 | 800 | 0.3450 | 0.4576 | 0.9952 | 0.0048 | 7.7086 |
| 8.4834 | 9.14 | 850 | 0.2773 | 0.3226 | 0.8238 | 0.1762 | 7.8743 |
| 8.4834 | 9.68 | 900 | 0.2184 | 0.2368 | 0.7286 | 0.2714 | 7.2171 |
| 8.4834 | 10.22 | 950 | 0.1992 | 0.2165 | 0.6333 | 0.3667 | 7.4343 |
| 0.7362 | 10.75 | 1000 | 0.1887 | 0.2097 | 0.5714 | 0.4286 | 7.5829 |
| 0.7362 | 11.29 | 1050 | 0.1815 | 0.2216 | 0.5905 | 0.4095 | 7.6171 |
| 0.7362 | 11.83 | 1100 | 0.1688 | 0.2046 | 0.5762 | 0.4238 | 7.4629 |
| 0.7362 | 12.37 | 1150 | 0.1679 | 0.2012 | 0.5286 | 0.4714 | 7.7143 |
| 0.7362 | 12.9 | 1200 | 0.1579 | 0.1952 | 0.5333 | 0.4667 | 7.5257 |
| 0.7362 | 13.44 | 1250 | 0.1531 | 0.1969 | 0.5095 | 0.4905 | 7.5714 |
| 0.7362 | 13.98 | 1300 | 0.1484 | 0.1935 | 0.4952 | 0.5048 | 7.5543 |
| 0.7362 | 14.52 | 1350 | 0.1481 | 0.1969 | 0.4952 | 0.5048 | 7.5886 |
| 0.7362 | 15.05 | 1400 | 0.1417 | 0.191 | 0.481 | 0.519 | 7.5829 |
| 0.7362 | 15.59 | 1450 | 0.1429 | 0.1876 | 0.4762 | 0.5238 | 7.5829 |
| 0.195 | 16.13 | 1500 | 0.1407 | 0.1834 | 0.481 | 0.519 | 7.48 |
| 0.195 | 16.67 | 1550 | 0.1409 | 0.1995 | 0.481 | 0.519 | 7.7086 |
| 0.195 | 17.2 | 1600 | 0.1432 | 0.1817 | 0.4762 | 0.5238 | 7.4857 |
| 0.195 | 17.74 | 1650 | 0.1439 | 0.1885 | 0.4762 | 0.5238 | 7.5429 |
| 0.195 | 18.28 | 1700 | 0.1385 | 0.1766 | 0.4476 | 0.5524 | 7.5143 |
| 0.195 | 18.82 | 1750 | 0.1357 | 0.1834 | 0.4762 | 0.5238 | 7.4971 |
| 0.195 | 19.35 | 1800 | 0.1349 | 0.1935 | 0.4714 | 0.5286 | 7.4686 |
| 0.195 | 19.89 | 1850 | 0.1355 | 0.1842 | 0.4286 | 0.5714 | 7.5371 |
| 0.195 | 20.43 | 1900 | 0.1343 | 0.1902 | 0.4619 | 0.5381 | 7.5714 |
| 0.195 | 20.97 | 1950 | 0.1348 | 0.1808 | 0.4619 | 0.5381 | 7.4229 |
| 0.1287 | 21.51 | 2000 | 0.1341 | 0.1817 | 0.4524 | 0.5476 | 7.4571 |
| 0.1287 | 22.04 | 2050 | 0.1324 | 0.1868 | 0.4476 | 0.5524 | 7.5371 |
| 0.1287 | 22.58 | 2100 | 0.1329 | 0.1859 | 0.4571 | 0.5429 | 7.4571 |
| 0.1287 | 23.12 | 2150 | 0.1367 | 0.1868 | 0.4476 | 0.5524 | 7.56 |
| 0.1287 | 23.66 | 2200 | 0.1389 | 0.1919 | 0.4667 | 0.5333 | 7.48 |
| 0.1287 | 24.19 | 2250 | 0.1385 | 0.18 | 0.4333 | 0.5667 | 7.5029 |
| 0.1287 | 24.73 | 2300 | 0.1429 | 0.1944 | 0.4905 | 0.5095 | 7.4171 |
| 0.1287 | 25.27 | 2350 | 0.1414 | 0.1961 | 0.4667 | 0.5333 | 7.6057 |
| 0.1287 | 25.81 | 2400 | 0.1419 | 0.1876 | 0.4333 | 0.5667 | 7.5371 |
| 0.1287 | 26.34 | 2450 | 0.1433 | 0.1927 | 0.4667 | 0.5333 | 7.5886 |
| 0.0977 | 26.88 | 2500 | 0.1433 | 0.1927 | 0.4571 | 0.5429 | 7.5486 |
| 0.0977 | 27.42 | 2550 | 0.1413 | 0.1978 | 0.4524 | 0.5476 | 7.6457 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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kkaung66/meta-chat | 2023-10-26T18:14:40.000Z | [
"transformers",
"llama",
"text-generation",
"generated_from_trainer",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | kkaung66 | null | null | kkaung66/meta-chat | 0 | 2 | transformers | 2023-10-26T06:45:42 | ---
base_model: NousResearch/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: meta-chat
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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# meta-chat
This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) 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: 0.0002
- train_batch_size: 14
- eval_batch_size: 14
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 112
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 0.5
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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dp011/test2 | 2023-10-26T07:37:54.000Z | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | dp011 | null | null | dp011/test2 | 0 | 2 | sentence-transformers | 2023-10-26T07:37:06 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# dp011/test2
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("dp011/test2")
# 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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Nguyens/bert-finetuned-ner-accelerate | 2023-10-26T08:47:05.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Nguyens | null | null | Nguyens/bert-finetuned-ner-accelerate | 0 | 2 | transformers | 2023-10-26T07:39:11 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-accelerate
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9354304635761589
- name: Recall
type: recall
value: 0.9508582968697409
- name: F1
type: f1
value: 0.9430812885995661
- name: Accuracy
type: accuracy
value: 0.9866809913463237
---
<!-- 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-finetuned-ner-accelerate
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0797
- Precision: 0.9354
- Recall: 0.9509
- F1: 0.9431
- Accuracy: 0.9867
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0211 | 1.0 | 1756 | 0.0741 | 0.9254 | 0.9443 | 0.9348 | 0.9851 |
| 0.0126 | 2.0 | 3512 | 0.0741 | 0.9331 | 0.9485 | 0.9407 | 0.9862 |
| 0.0084 | 3.0 | 5268 | 0.0797 | 0.9354 | 0.9509 | 0.9431 | 0.9867 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat | 2023-10-26T08:47:35.000Z | [
"transformers",
"pytorch",
"flamingo",
"text2text-generation",
"dataset:pufanyi/MIMICIT",
"arxiv:2306.05425",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | sugiv | null | null | sugiv/Spoonbill-GarudaOtterFlamingoAreFriends-7B-Chat | 0 | 2 | transformers | 2023-10-26T08:32:03 | ---
license: mit
datasets:
- pufanyi/MIMICIT
---
Dubbed Spoonbill Garuda version used instruction tuned sugiv/garuda-from-llama2-7B-chat
as languade model. The above said Spoonbill Garuda is also vision-language model which
was also trained on visual instruction datasets (limited from Otter).
- Please refer to license of Llama2 from which Garuda was derived with Alpaca dataset.
- The above is also fine-tuned on visual instruction tuning datasets derived from Otter's datasets.
- https://ai.meta.com/llama/license/
- @software{anas_awadalla_2023_7733589,
author = {Awadalla, Anas and Gao, Irena and Gardner, Joshua and Hessel, Jack and Hanafy, Yusuf and Zhu, Wanrong and Marathe, Kalyani and Bitton, Yonatan and Gadre, Samir and Jitsev, Jenia and Kornblith, Simon and Koh, Pang Wei and Ilharco, Gabriel and Wortsman, Mitchell and Schmidt, Ludwig},
title = {OpenFlamingo},
month = mar,
year = 2023,
publisher = {Zenodo},
version = {v0.1.1},
doi = {10.5281/zenodo.7733589},
url = {https://doi.org/10.5281/zenodo.7733589}
}
- @article{li2023otter,
title={Otter: A Multi-Modal Model with In-Context Instruction Tuning},
author={Li, Bo and Zhang, Yuanhan and Chen, Liangyu and Wang, Jinghao and Yang, Jingkang and Liu, Ziwei},
journal={arXiv preprint arXiv:2305.03726},
year={2023}
}
@article{li2023mimicit,
title={MIMIC-IT: Multi-Modal In-Context Instruction Tuning},
author={Bo Li and Yuanhan Zhang and Liangyu Chen and Jinghao Wang and Fanyi Pu and Jingkang Yang and Chunyuan Li and Ziwei Liu},
year={2023},
eprint={2306.05425},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | 1,671 | [
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che111/spanish_mlm-mix | 2023-10-26T18:18:43.000Z | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | che111 | null | null | che111/spanish_mlm-mix | 0 | 2 | transformers | 2023-10-26T09:48:26 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: spanish_mlm-mix
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. -->
# spanish_mlm-mix
This model is a fine-tuned version of [che111/spanish_mlm](https://huggingface.co/che111/spanish_mlm) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0613
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 300
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log | 1.0 | 42 | 5.9500 |
| No log | 2.0 | 84 | 4.7513 |
| No log | 3.0 | 126 | 4.0794 |
| 5.3419 | 4.0 | 168 | 3.5736 |
| 5.3419 | 5.0 | 210 | 3.2749 |
| 5.3419 | 6.0 | 252 | 3.0658 |
| 5.3419 | 7.0 | 294 | 2.8606 |
| 3.278 | 8.0 | 336 | 2.7112 |
| 3.278 | 9.0 | 378 | 2.5546 |
| 3.278 | 10.0 | 420 | 2.4961 |
| 3.278 | 11.0 | 462 | 2.4307 |
| 2.6895 | 12.0 | 504 | 2.3194 |
| 2.6895 | 13.0 | 546 | 2.2815 |
| 2.6895 | 14.0 | 588 | 2.2136 |
| 2.6895 | 15.0 | 630 | 2.1758 |
| 2.3782 | 16.0 | 672 | 2.1187 |
| 2.3782 | 17.0 | 714 | 2.0608 |
| 2.3782 | 18.0 | 756 | 2.0388 |
| 2.3782 | 19.0 | 798 | 2.0160 |
| 2.186 | 20.0 | 840 | 1.9685 |
| 2.186 | 21.0 | 882 | 1.9444 |
| 2.186 | 22.0 | 924 | 1.9054 |
| 2.186 | 23.0 | 966 | 1.8580 |
| 2.0439 | 24.0 | 1008 | 1.8534 |
| 2.0439 | 25.0 | 1050 | 1.8177 |
| 2.0439 | 26.0 | 1092 | 1.8268 |
| 2.0439 | 27.0 | 1134 | 1.7650 |
| 1.9344 | 28.0 | 1176 | 1.7497 |
| 1.9344 | 29.0 | 1218 | 1.7341 |
| 1.9344 | 30.0 | 1260 | 1.7258 |
| 1.9344 | 31.0 | 1302 | 1.7173 |
| 1.8419 | 32.0 | 1344 | 1.6928 |
| 1.8419 | 33.0 | 1386 | 1.6886 |
| 1.8419 | 34.0 | 1428 | 1.6449 |
| 1.8419 | 35.0 | 1470 | 1.6714 |
| 1.7739 | 36.0 | 1512 | 1.6572 |
| 1.7739 | 37.0 | 1554 | 1.6197 |
| 1.7739 | 38.0 | 1596 | 1.6021 |
| 1.7739 | 39.0 | 1638 | 1.5920 |
| 1.7081 | 40.0 | 1680 | 1.5883 |
| 1.7081 | 41.0 | 1722 | 1.5790 |
| 1.7081 | 42.0 | 1764 | 1.5516 |
| 1.7081 | 43.0 | 1806 | 1.5335 |
| 1.6567 | 44.0 | 1848 | 1.5363 |
| 1.6567 | 45.0 | 1890 | 1.5222 |
| 1.6567 | 46.0 | 1932 | 1.5366 |
| 1.6567 | 47.0 | 1974 | 1.5192 |
| 1.6096 | 48.0 | 2016 | 1.5261 |
| 1.6096 | 49.0 | 2058 | 1.5103 |
| 1.6096 | 50.0 | 2100 | 1.4700 |
| 1.6096 | 51.0 | 2142 | 1.5236 |
| 1.5683 | 52.0 | 2184 | 1.4769 |
| 1.5683 | 53.0 | 2226 | 1.4812 |
| 1.5683 | 54.0 | 2268 | 1.4569 |
| 1.5683 | 55.0 | 2310 | 1.4486 |
| 1.5298 | 56.0 | 2352 | 1.4268 |
| 1.5298 | 57.0 | 2394 | 1.4462 |
| 1.5298 | 58.0 | 2436 | 1.4322 |
| 1.5298 | 59.0 | 2478 | 1.4285 |
| 1.4985 | 60.0 | 2520 | 1.4244 |
| 1.4985 | 61.0 | 2562 | 1.4169 |
| 1.4985 | 62.0 | 2604 | 1.4175 |
| 1.4985 | 63.0 | 2646 | 1.4138 |
| 1.466 | 64.0 | 2688 | 1.3719 |
| 1.466 | 65.0 | 2730 | 1.3681 |
| 1.466 | 66.0 | 2772 | 1.3770 |
| 1.466 | 67.0 | 2814 | 1.3715 |
| 1.4392 | 68.0 | 2856 | 1.3871 |
| 1.4392 | 69.0 | 2898 | 1.3667 |
| 1.4392 | 70.0 | 2940 | 1.3694 |
| 1.4392 | 71.0 | 2982 | 1.3551 |
| 1.4138 | 72.0 | 3024 | 1.3607 |
| 1.4138 | 73.0 | 3066 | 1.3565 |
| 1.4138 | 74.0 | 3108 | 1.3428 |
| 1.4138 | 75.0 | 3150 | 1.3335 |
| 1.388 | 76.0 | 3192 | 1.3509 |
| 1.388 | 77.0 | 3234 | 1.3153 |
| 1.388 | 78.0 | 3276 | 1.3231 |
| 1.388 | 79.0 | 3318 | 1.2944 |
| 1.3723 | 80.0 | 3360 | 1.3176 |
| 1.3723 | 81.0 | 3402 | 1.3244 |
| 1.3723 | 82.0 | 3444 | 1.3017 |
| 1.3496 | 83.0 | 3486 | 1.3050 |
| 1.3496 | 84.0 | 3528 | 1.3079 |
| 1.3496 | 85.0 | 3570 | 1.2923 |
| 1.3496 | 86.0 | 3612 | 1.3047 |
| 1.3312 | 87.0 | 3654 | 1.2832 |
| 1.3312 | 88.0 | 3696 | 1.2729 |
| 1.3312 | 89.0 | 3738 | 1.2600 |
| 1.3312 | 90.0 | 3780 | 1.2604 |
| 1.313 | 91.0 | 3822 | 1.3075 |
| 1.313 | 92.0 | 3864 | 1.2712 |
| 1.313 | 93.0 | 3906 | 1.2681 |
| 1.313 | 94.0 | 3948 | 1.2699 |
| 1.2979 | 95.0 | 3990 | 1.2800 |
| 1.2979 | 96.0 | 4032 | 1.2604 |
| 1.2979 | 97.0 | 4074 | 1.2444 |
| 1.2979 | 98.0 | 4116 | 1.2730 |
| 1.2811 | 99.0 | 4158 | 1.2571 |
| 1.2811 | 100.0 | 4200 | 1.2459 |
| 1.2811 | 101.0 | 4242 | 1.2524 |
| 1.2811 | 102.0 | 4284 | 1.2556 |
| 1.2663 | 103.0 | 4326 | 1.2384 |
| 1.2663 | 104.0 | 4368 | 1.2216 |
| 1.2663 | 105.0 | 4410 | 1.2529 |
| 1.2663 | 106.0 | 4452 | 1.2171 |
| 1.2507 | 107.0 | 4494 | 1.2323 |
| 1.2507 | 108.0 | 4536 | 1.2152 |
| 1.2507 | 109.0 | 4578 | 1.2108 |
| 1.2507 | 110.0 | 4620 | 1.2110 |
| 1.2379 | 111.0 | 4662 | 1.1962 |
| 1.2379 | 112.0 | 4704 | 1.2161 |
| 1.2379 | 113.0 | 4746 | 1.2215 |
| 1.2379 | 114.0 | 4788 | 1.2114 |
| 1.2227 | 115.0 | 4830 | 1.2251 |
| 1.2227 | 116.0 | 4872 | 1.2069 |
| 1.2227 | 117.0 | 4914 | 1.2084 |
| 1.2227 | 118.0 | 4956 | 1.1883 |
| 1.2116 | 119.0 | 4998 | 1.2315 |
| 1.2116 | 120.0 | 5040 | 1.1973 |
| 1.2116 | 121.0 | 5082 | 1.1830 |
| 1.2116 | 122.0 | 5124 | 1.1748 |
| 1.2029 | 123.0 | 5166 | 1.1850 |
| 1.2029 | 124.0 | 5208 | 1.2040 |
| 1.2029 | 125.0 | 5250 | 1.1948 |
| 1.2029 | 126.0 | 5292 | 1.1992 |
| 1.1938 | 127.0 | 5334 | 1.1996 |
| 1.1938 | 128.0 | 5376 | 1.1886 |
| 1.1938 | 129.0 | 5418 | 1.1774 |
| 1.1938 | 130.0 | 5460 | 1.1844 |
| 1.181 | 131.0 | 5502 | 1.2097 |
| 1.181 | 132.0 | 5544 | 1.1771 |
| 1.181 | 133.0 | 5586 | 1.1672 |
| 1.181 | 134.0 | 5628 | 1.1708 |
| 1.1734 | 135.0 | 5670 | 1.1659 |
| 1.1734 | 136.0 | 5712 | 1.1655 |
| 1.1734 | 137.0 | 5754 | 1.1470 |
| 1.1734 | 138.0 | 5796 | 1.1508 |
| 1.1634 | 139.0 | 5838 | 1.1509 |
| 1.1634 | 140.0 | 5880 | 1.1546 |
| 1.1634 | 141.0 | 5922 | 1.1689 |
| 1.1634 | 142.0 | 5964 | 1.1536 |
| 1.1512 | 143.0 | 6006 | 1.1574 |
| 1.1512 | 144.0 | 6048 | 1.1369 |
| 1.1512 | 145.0 | 6090 | 1.1491 |
| 1.1512 | 146.0 | 6132 | 1.1581 |
| 1.1438 | 147.0 | 6174 | 1.1517 |
| 1.1438 | 148.0 | 6216 | 1.1560 |
| 1.1438 | 149.0 | 6258 | 1.1433 |
| 1.1438 | 150.0 | 6300 | 1.1408 |
| 1.1364 | 151.0 | 6342 | 1.1565 |
| 1.1364 | 152.0 | 6384 | 1.1478 |
| 1.1364 | 153.0 | 6426 | 1.1287 |
| 1.1364 | 154.0 | 6468 | 1.1287 |
| 1.1296 | 155.0 | 6510 | 1.1319 |
| 1.1296 | 156.0 | 6552 | 1.1347 |
| 1.1296 | 157.0 | 6594 | 1.1125 |
| 1.1296 | 158.0 | 6636 | 1.1241 |
| 1.1203 | 159.0 | 6678 | 1.1393 |
| 1.1203 | 160.0 | 6720 | 1.1201 |
| 1.1203 | 161.0 | 6762 | 1.1432 |
| 1.1203 | 162.0 | 6804 | 1.1386 |
| 1.1109 | 163.0 | 6846 | 1.1266 |
| 1.1109 | 164.0 | 6888 | 1.1344 |
| 1.1109 | 165.0 | 6930 | 1.1305 |
| 1.1054 | 166.0 | 6972 | 1.1269 |
| 1.1054 | 167.0 | 7014 | 1.1268 |
| 1.1054 | 168.0 | 7056 | 1.1252 |
| 1.1054 | 169.0 | 7098 | 1.1220 |
| 1.0987 | 170.0 | 7140 | 1.1327 |
| 1.0987 | 171.0 | 7182 | 1.1277 |
| 1.0987 | 172.0 | 7224 | 1.1152 |
| 1.0987 | 173.0 | 7266 | 1.1059 |
| 1.0925 | 174.0 | 7308 | 1.1138 |
| 1.0925 | 175.0 | 7350 | 1.1110 |
| 1.0925 | 176.0 | 7392 | 1.1282 |
| 1.0925 | 177.0 | 7434 | 1.1002 |
| 1.0895 | 178.0 | 7476 | 1.1103 |
| 1.0895 | 179.0 | 7518 | 1.1033 |
| 1.0895 | 180.0 | 7560 | 1.1181 |
| 1.0895 | 181.0 | 7602 | 1.0851 |
| 1.0827 | 182.0 | 7644 | 1.1038 |
| 1.0827 | 183.0 | 7686 | 1.1133 |
| 1.0827 | 184.0 | 7728 | 1.1104 |
| 1.0827 | 185.0 | 7770 | 1.1150 |
| 1.0747 | 186.0 | 7812 | 1.0945 |
| 1.0747 | 187.0 | 7854 | 1.1055 |
| 1.0747 | 188.0 | 7896 | 1.1056 |
| 1.0747 | 189.0 | 7938 | 1.1010 |
| 1.0733 | 190.0 | 7980 | 1.0929 |
| 1.0733 | 191.0 | 8022 | 1.1085 |
| 1.0733 | 192.0 | 8064 | 1.0827 |
| 1.0733 | 193.0 | 8106 | 1.1026 |
| 1.0656 | 194.0 | 8148 | 1.0983 |
| 1.0656 | 195.0 | 8190 | 1.0766 |
| 1.0656 | 196.0 | 8232 | 1.0864 |
| 1.0656 | 197.0 | 8274 | 1.0967 |
| 1.0598 | 198.0 | 8316 | 1.0998 |
| 1.0598 | 199.0 | 8358 | 1.1029 |
| 1.0598 | 200.0 | 8400 | 1.1155 |
| 1.0598 | 201.0 | 8442 | 1.0716 |
| 1.0582 | 202.0 | 8484 | 1.0981 |
| 1.0582 | 203.0 | 8526 | 1.0785 |
| 1.0582 | 204.0 | 8568 | 1.0903 |
| 1.0582 | 205.0 | 8610 | 1.0936 |
| 1.0519 | 206.0 | 8652 | 1.0909 |
| 1.0519 | 207.0 | 8694 | 1.0815 |
| 1.0519 | 208.0 | 8736 | 1.0837 |
| 1.0519 | 209.0 | 8778 | 1.1162 |
| 1.0474 | 210.0 | 8820 | 1.0747 |
| 1.0474 | 211.0 | 8862 | 1.0972 |
| 1.0474 | 212.0 | 8904 | 1.0827 |
| 1.0474 | 213.0 | 8946 | 1.0908 |
| 1.0439 | 214.0 | 8988 | 1.0716 |
| 1.0439 | 215.0 | 9030 | 1.0713 |
| 1.0439 | 216.0 | 9072 | 1.0787 |
| 1.0439 | 217.0 | 9114 | 1.0749 |
| 1.0406 | 218.0 | 9156 | 1.0843 |
| 1.0406 | 219.0 | 9198 | 1.0794 |
| 1.0406 | 220.0 | 9240 | 1.0822 |
| 1.0406 | 221.0 | 9282 | 1.0782 |
| 1.0387 | 222.0 | 9324 | 1.0710 |
| 1.0387 | 223.0 | 9366 | 1.0572 |
| 1.0387 | 224.0 | 9408 | 1.0498 |
| 1.0387 | 225.0 | 9450 | 1.0916 |
| 1.034 | 226.0 | 9492 | 1.0890 |
| 1.034 | 227.0 | 9534 | 1.0613 |
| 1.034 | 228.0 | 9576 | 1.0822 |
| 1.034 | 229.0 | 9618 | 1.0644 |
| 1.0296 | 230.0 | 9660 | 1.0578 |
| 1.0296 | 231.0 | 9702 | 1.0716 |
| 1.0296 | 232.0 | 9744 | 1.0647 |
| 1.0296 | 233.0 | 9786 | 1.0886 |
| 1.0289 | 234.0 | 9828 | 1.0633 |
| 1.0289 | 235.0 | 9870 | 1.0626 |
| 1.0289 | 236.0 | 9912 | 1.0711 |
| 1.0289 | 237.0 | 9954 | 1.0780 |
| 1.0242 | 238.0 | 9996 | 1.0731 |
| 1.0242 | 239.0 | 10038 | 1.0643 |
| 1.0242 | 240.0 | 10080 | 1.0705 |
| 1.0242 | 241.0 | 10122 | 1.0668 |
| 1.0215 | 242.0 | 10164 | 1.0696 |
| 1.0215 | 243.0 | 10206 | 1.0767 |
| 1.0215 | 244.0 | 10248 | 1.0614 |
| 1.0215 | 245.0 | 10290 | 1.0608 |
| 1.0212 | 246.0 | 10332 | 1.0547 |
| 1.0212 | 247.0 | 10374 | 1.0738 |
| 1.0212 | 248.0 | 10416 | 1.0688 |
| 1.0187 | 249.0 | 10458 | 1.0582 |
| 1.0187 | 250.0 | 10500 | 1.0479 |
| 1.0187 | 251.0 | 10542 | 1.0658 |
| 1.0187 | 252.0 | 10584 | 1.0610 |
| 1.015 | 253.0 | 10626 | 1.0524 |
| 1.015 | 254.0 | 10668 | 1.0507 |
| 1.015 | 255.0 | 10710 | 1.0573 |
| 1.015 | 256.0 | 10752 | 1.0611 |
| 1.0136 | 257.0 | 10794 | 1.0618 |
| 1.0136 | 258.0 | 10836 | 1.0712 |
| 1.0136 | 259.0 | 10878 | 1.0605 |
| 1.0136 | 260.0 | 10920 | 1.0842 |
| 1.0093 | 261.0 | 10962 | 1.0524 |
| 1.0093 | 262.0 | 11004 | 1.0243 |
| 1.0093 | 263.0 | 11046 | 1.0607 |
| 1.0093 | 264.0 | 11088 | 1.0584 |
| 1.009 | 265.0 | 11130 | 1.0593 |
| 1.009 | 266.0 | 11172 | 1.0652 |
| 1.009 | 267.0 | 11214 | 1.0512 |
| 1.009 | 268.0 | 11256 | 1.0392 |
| 1.0088 | 269.0 | 11298 | 1.0663 |
| 1.0088 | 270.0 | 11340 | 1.0705 |
| 1.0088 | 271.0 | 11382 | 1.0431 |
| 1.0088 | 272.0 | 11424 | 1.0629 |
| 1.0044 | 273.0 | 11466 | 1.0588 |
| 1.0044 | 274.0 | 11508 | 1.0501 |
| 1.0044 | 275.0 | 11550 | 1.0704 |
| 1.0044 | 276.0 | 11592 | 1.0768 |
| 1.0084 | 277.0 | 11634 | 1.0532 |
| 1.0084 | 278.0 | 11676 | 1.0556 |
| 1.0084 | 279.0 | 11718 | 1.0741 |
| 1.0084 | 280.0 | 11760 | 1.0717 |
| 1.0017 | 281.0 | 11802 | 1.0755 |
| 1.0017 | 282.0 | 11844 | 1.0776 |
| 1.0017 | 283.0 | 11886 | 1.0700 |
| 1.0017 | 284.0 | 11928 | 1.0579 |
| 1.0043 | 285.0 | 11970 | 1.0414 |
| 1.0043 | 286.0 | 12012 | 1.0447 |
| 1.0043 | 287.0 | 12054 | 1.0595 |
| 1.0043 | 288.0 | 12096 | 1.0523 |
| 1.0026 | 289.0 | 12138 | 1.0540 |
| 1.0026 | 290.0 | 12180 | 1.0591 |
| 1.0026 | 291.0 | 12222 | 1.0622 |
| 1.0026 | 292.0 | 12264 | 1.0584 |
| 1.0008 | 293.0 | 12306 | 1.0349 |
| 1.0008 | 294.0 | 12348 | 1.0472 |
| 1.0008 | 295.0 | 12390 | 1.0552 |
| 1.0008 | 296.0 | 12432 | 1.0614 |
| 1.002 | 297.0 | 12474 | 1.0770 |
| 1.002 | 298.0 | 12516 | 1.0712 |
| 1.002 | 299.0 | 12558 | 1.0773 |
| 1.002 | 300.0 | 12600 | 1.0570 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.13.3
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] |
LoneStriker/lzlv_70b_fp16_hf-5.0bpw-h6-exl2 | 2023-10-26T12:09:42.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/lzlv_70b_fp16_hf-5.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-26T12:06:48 | ---
license: cc-by-nc-2.0
---
# lzlv_70B
## A Mythomax/MLewd_13B-style merge of selected 70B models
A multi-model merge of several LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience.
Did it work? Probably, maybe. It seemed subjectively better than each of the individual models in my tests.
GGUF 4_K_M + 5_K_M can be found here: https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf/settings
## Procedure:
Models used:
- **NousResearch/Nous-Hermes-Llama2-70b** - A great model for roleplaying, but not the best at following complex instructions.
- **Xwin-LM/Xwin-LM-7B-V0.1** - Excellent at following instructions and quite creative out of the box, so it seemed like the best available model to act as the base for the merge.
- **Doctor-Shotgun/Mythospice-70b** - The wildcard of the three. I was looking for a creative, NSFW-oriented model and came across this while digging through hf. I hadn't heard of it before and apparently no one had bothered to release a quantized version of this model. So I downloaded it and did it myself to test it. It turned out to be more or less what I was looking for as my third component, so I used it here.
A big thank you to the creators of the models above. If you look up Mythospice, you will notice that it also includes Nous-Hermes so it's technically present twice in this mix. This is apparently common practice amongst the cool kids who do 13B models so I don't think this hurts the model.
The merging process was heavily inspired by Undi95's approach in Undi95/MXLewdMini-L2-13B. To be specific, the ratios are:
Component 1: Merge of Mythospice x Xwin with SLERP gradient [0.25, 0.3, 0.5].
Component 2: Merge Xwin x Hermes with SLERP gradient [0.4, 0.3, 0.25].
Finally, both Component 1 and Component 2 were merged with SLERP using weight 0.5.
## Peformance
I tested this model for a few days before publishing it. It seems to more or less retain the instruction-following capabilities of Xwin-70B, while seeming to have adopted a lot of the creativity of the other two models.
It handled my more complex scenarios that creative models otherwise tend to struggle with quite well. At the same time, its outputs felt more creative and possibly a bit more nsfw-inclined than Xwin-70b.
So, is it better? Feels like it to me, subjectively. Is it really better? No clue, test it.
## Prompt format:
Vicuna
USER: [Prompt]
ASSISTANT:
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] |
sainteye/ifoodie-detail-rating-v15 | 2023-10-26T12:36:29.000Z | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | sainteye | null | null | sainteye/ifoodie-detail-rating-v15 | 0 | 2 | transformers | 2023-10-26T12:36:25 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ifoodie-detail-rating-v15
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9673202633857727
---
# ifoodie-detail-rating-v15
['中間', '偏壞', '偏好']
## Example Images
# #### 中間
# 
#
# #### 偏壞
# 
#
# #### 偏好
# 
# | 475 | [
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joseluhf11/phi-1_5-finetuned-dxrare_symptom_extractor_v1 | 2023-10-26T13:13:15.000Z | [
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"generated_from_trainer",
"custom_code",
"license:other",
"region:us"
] | text-generation | joseluhf11 | null | null | joseluhf11/phi-1_5-finetuned-dxrare_symptom_extractor_v1 | 0 | 2 | transformers | 2023-10-26T12:56:39 | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-dxrare_symptom_extractor_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. -->
# phi-1_5-finetuned-dxrare_symptom_extractor_v1
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
igig98/ppo_ | 2023-10-26T13:22:03.000Z | [
"peft",
"region:us"
] | null | igig98 | null | null | igig98/ppo_ | 0 | 2 | peft | 2023-10-26T13:21:32 | ---
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
| 464 | [
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joseluhf11/disease_encoder_v3 | 2023-10-26T14:00:32.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | joseluhf11 | null | null | joseluhf11/disease_encoder_v3 | 0 | 2 | sentence-transformers | 2023-10-26T13:55:36 | ---
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
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# 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, mean pooling.
sentence_embeddings = mean_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 1863 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1863,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,789 | [
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DanZter/finetuning-sentiment-model-3000-samples | 2023-10-26T14:57:45.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | DanZter | null | null | DanZter/finetuning-sentiment-model-3000-samples | 0 | 2 | transformers | 2023-10-26T14:10:51 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
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: 0.0541
- Accuracy: 0.9886
- F1: 0.0
## 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: 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 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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MFedor/distilbert-base-uncased-finetuned-cola | 2023-10-26T15:18:13.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | MFedor | null | null | MFedor/distilbert-base-uncased-finetuned-cola | 0 | 2 | transformers | 2023-10-26T15:04:02 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5366931756163555
---
<!-- 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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7682
- Matthews Correlation: 0.5367
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5205 | 1.0 | 535 | 0.4616 | 0.4935 |
| 0.3458 | 2.0 | 1070 | 0.4893 | 0.5162 |
| 0.225 | 3.0 | 1605 | 0.6210 | 0.5177 |
| 0.1758 | 4.0 | 2140 | 0.7682 | 0.5367 |
| 0.1224 | 5.0 | 2675 | 0.8429 | 0.5354 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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sniperyyc/NetFID-PCAP-IP-Header | 2023-10-26T17:41:08.000Z | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | sniperyyc | null | null | sniperyyc/NetFID-PCAP-IP-Header | 0 | 2 | transformers | 2023-10-26T15:13:50 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: NetFID-PCAP-IP-Header
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. -->
# NetFID-PCAP-IP-Header
This model is a train-from-scratch version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a mixed-source PCAP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8973
- Accuracy: 0.7592
## Model description
Pretrained model with [bert-base-uncased](https://huggingface.co/bert-base-uncased) (110M parameters) as the base architecture.
## Intended uses & limitations
This model is mainly used to get embeddings for PCAP IPv4 header data, which can be further used for ML-based tasks e.g., classification, clustering, etc.
## How to use
The usage is almost the same as regular BERT models, except that the input data is PCAP traces.
## Training and evaluation data
TBD.
## Training procedure
TBD.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- 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: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
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] |
Nguyens/fine-tuning-distilbert | 2023-10-28T04:31:27.000Z | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | Nguyens | null | null | Nguyens/fine-tuning-distilbert | 0 | 2 | transformers | 2023-10-26T15:31:31 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: fine-tuning-distilbert
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. -->
# fine-tuning-distilbert
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: 2.0806
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2831 | 1.0 | 157 | 2.0897 |
| 2.1738 | 2.0 | 314 | 1.9164 |
| 2.1779 | 3.0 | 471 | 2.0064 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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s-man2099/gpl-1500 | 2023-10-26T17:21:21.000Z | [
"transformers",
"tf",
"pegasus",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | s-man2099 | null | null | s-man2099/gpl-1500 | 0 | 2 | transformers | 2023-10-26T16:18:04 | ---
base_model: google/pegasus-large
tags:
- generated_from_keras_callback
model-index:
- name: s-man2099/gpl-1500
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. -->
# s-man2099/gpl-1500
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.1674
- Validation Loss: 3.4953
- Epoch: 9
## 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': 'Adafactor', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-06, 'beta_2_decay': -0.8, 'epsilon_1': 1e-30, 'epsilon_2': 0.001, 'clip_threshold': 1.0, 'relative_step': True}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.9497 | 3.5189 | 0 |
| 3.6923 | 3.4731 | 1 |
| 3.5513 | 3.4603 | 2 |
| 3.4677 | 3.4661 | 3 |
| 3.3892 | 3.4678 | 4 |
| 3.3222 | 3.4794 | 5 |
| 3.2575 | 3.4887 | 6 |
| 3.1904 | 3.4914 | 7 |
| 3.1848 | 3.4940 | 8 |
| 3.1674 | 3.4953 | 9 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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kaitchup/Llama-2-7b-mt-French-to-English | 2023-11-02T17:31:43.000Z | [
"peft",
"translation",
"en",
"fr",
"dataset:kaitchup/opus-French-to-English",
"license:mit",
"region:us"
] | translation | kaitchup | null | null | kaitchup/Llama-2-7b-mt-French-to-English | 0 | 2 | peft | 2023-10-26T16:50:23 | ---
library_name: peft
license: mit
language:
- en
- fr
datasets:
- kaitchup/opus-French-to-English
tags:
- translation
---
# Model Card for Model ID
This is an adapter for Meta's Llama 2 7B fine-tuned for translating French text into English.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **Model type:** LoRA Adapter for Llama 2 7B
- **Language(s) (NLP):** French, English
- **License:** MIT license
## Uses
This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning.
You can use the following code to load the model:
```
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch
from peft import PeftModel
base_model = "meta-llama/Llama-2-7b-hf"
compute_dtype = getattr(torch, "float16")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
original_model_directory, device_map={"": 0}, quantization_config=bnb_config
)
tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-French-to-English")
```
Then, run the model as follows:
```
my_text = "" #put your text to translate here
prompt = my_text+" ###>"
tokenized_input = tokenizer(prompt, return_tensors="pt")
input_ids = tokenized_input["input_ids"].cuda()
generation_output = model.generate(
input_ids=input_ids,
num_beams=10,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=130
)
for seq in generation_output.sequences:
output = tokenizer.decode(seq, skip_special_tokens=True)
print(output.split("###>")[1].strip())
```
## Model Card Contact
[The Kaitchup](https://kaitchup.substack.com/) | 2,071 | [
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jessica-ecosia/distilbert-base-uncased-finetuned-squad | 2023-10-26T17:10:07.000Z | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | jessica-ecosia | null | null | jessica-ecosia/distilbert-base-uncased-finetuned-squad | 0 | 2 | transformers | 2023-10-26T17:05:09 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
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-squad
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: 3.9886
## 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 | 26 | 4.5861 |
| No log | 2.0 | 52 | 4.0953 |
| No log | 3.0 | 78 | 3.9886 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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kou552/distilbert-base-uncased-finetuned-emotions | 2023-10-26T18:15:54.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | kou552 | null | null | kou552/distilbert-base-uncased-finetuned-emotions | 0 | 2 | transformers | 2023-10-26T17:59:22 | ---
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-emotions
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: 0.9215
- name: F1
type: f1
value: 0.9213366023493611
---
<!-- 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-emotions
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: 0.2273
- Accuracy: 0.9215
- F1: 0.9213
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8487 | 1.0 | 250 | 0.3244 | 0.905 | 0.9037 |
| 0.2581 | 2.0 | 500 | 0.2273 | 0.9215 | 0.9213 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
thrunlab/t5-base_cola_moe_ex38_epochs-3_decoder_all_sparsity20_mare_mlp | 2023-10-26T19:55:40.000Z | [
"transformers",
"pytorch",
"t5",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-classification | thrunlab | null | null | thrunlab/t5-base_cola_moe_ex38_epochs-3_decoder_all_sparsity20_mare_mlp | 0 | 2 | transformers | 2023-10-26T19:46:51 | ---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: t5-base_cola_moe_ex38_epochs-3_decoder_all_sparsity20_mare_mlp
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Accuracy
type: accuracy
value: 0.835091083413231
---
<!-- 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. -->
# t5-base_cola_moe_ex38_epochs-3_decoder_all_sparsity20_mare_mlp
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6107
- Accuracy: 0.8351
## 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: 16
- eval_batch_size: 32
- seed: 1
- gradient_accumulation_steps: 2
- 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: 20
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.54 | 0.19 | 50 | 0.9351 | 0.8178 |
| 0.508 | 0.37 | 100 | 0.7150 | 0.8332 |
| 0.5206 | 0.56 | 150 | 0.6512 | 0.8265 |
| 0.4831 | 0.75 | 200 | 0.6504 | 0.8274 |
| 0.5094 | 0.93 | 250 | 0.5474 | 0.8313 |
| 0.3632 | 1.12 | 300 | 0.6911 | 0.8226 |
| 0.3467 | 1.31 | 350 | 0.6089 | 0.8303 |
| 0.3803 | 1.5 | 400 | 0.5704 | 0.8360 |
| 0.3281 | 1.68 | 450 | 0.6079 | 0.8313 |
| 0.3239 | 1.87 | 500 | 0.5792 | 0.8284 |
| 0.2903 | 2.06 | 550 | 0.5910 | 0.8293 |
| 0.3892 | 2.24 | 600 | 0.6007 | 0.8341 |
| 0.2846 | 2.43 | 650 | 0.5993 | 0.8351 |
| 0.3209 | 2.62 | 700 | 0.6508 | 0.8360 |
| 0.2325 | 2.8 | 750 | 0.6217 | 0.8341 |
| 0.3949 | 2.99 | 800 | 0.6201 | 0.8341 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.9.0
- Tokenizers 0.14.1
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mpalaval/assignment2_attempt7 | 2023-10-26T20:32:13.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | mpalaval | null | null | mpalaval/assignment2_attempt7 | 0 | 2 | transformers | 2023-10-26T20:22:34 | ---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
model-index:
- name: assignment2_attempt7
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. -->
# assignment2_attempt7
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None 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: 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: 100
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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PranavY2k/my_distilbert_model | 2023-10-26T21:18:59.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:rotten_tomatoes",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | PranavY2k | null | null | PranavY2k/my_distilbert_model | 0 | 2 | transformers | 2023-10-26T21:09:02 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my_distilbert_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: rotten_tomatoes
type: rotten_tomatoes
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8433395872420263
- name: F1
type: f1
value: 0.8432898032121621
- name: Precision
type: precision
value: 0.843776433767552
- name: Recall
type: recall
value: 0.8433395872420262
---
<!-- 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_distilbert_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the rotten_tomatoes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5593
- Accuracy: 0.8433
- F1: 0.8433
- Precision: 0.8438
- Recall: 0.8433
## 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 | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4222 | 1.0 | 534 | 0.3821 | 0.8424 | 0.8421 | 0.8450 | 0.8424 |
| 0.2558 | 2.0 | 1068 | 0.4620 | 0.8433 | 0.8432 | 0.8445 | 0.8433 |
| 0.1609 | 3.0 | 1602 | 0.5593 | 0.8433 | 0.8433 | 0.8438 | 0.8433 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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SalmonAI123/Bestclean-ViMrclarge-TFIDFPNS-ver1 | 2023-10-26T22:39:45.000Z | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | SalmonAI123 | null | null | SalmonAI123/Bestclean-ViMrclarge-TFIDFPNS-ver1 | 0 | 2 | transformers | 2023-10-26T22:22:58 | ---
license: cc-by-nc-4.0
base_model: nguyenvulebinh/vi-mrc-large
tags:
- generated_from_trainer
model-index:
- name: Bestclean-ViMrclarge-TFIDFPNS-ver1
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. -->
# Bestclean-ViMrclarge-TFIDFPNS-ver1
This model is a fine-tuned version of [nguyenvulebinh/vi-mrc-large](https://huggingface.co/nguyenvulebinh/vi-mrc-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1537
## 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: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8736 | 1.0 | 603 | 0.9764 |
| 0.3873 | 2.0 | 1206 | 1.1537 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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akjindal53244/temp-peft-model | 2023-10-26T23:14:11.000Z | [
"peft",
"region:us"
] | null | akjindal53244 | null | null | akjindal53244/temp-peft-model | 1 | 2 | peft | 2023-10-26T22:57:19 | ---
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 0.6.0.dev0
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mnazari/wav2vec2-large-mms-1b-urmi-christian-nointonations | 2023-10-27T10:33:07.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:nena_speech_1_0_test",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | mnazari | null | null | mnazari/wav2vec2-large-mms-1b-urmi-christian-nointonations | 0 | 2 | transformers | 2023-10-26T23:07:19 | ---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
datasets:
- nena_speech_1_0_test
metrics:
- wer
model-index:
- name: wav2vec2-large-mms-1b-urmi-christian-nointonations
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: nena_speech_1_0_test
type: nena_speech_1_0_test
config: urmi (christian)
split: test
args: urmi (christian)
metrics:
- name: Wer
type: wer
value: 1.0
---
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# wav2vec2-large-mms-1b-urmi-christian-nointonations
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the nena_speech_1_0_test dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4023
- Wer: 1.0
- Cer: 0.3080
## 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: 32
- 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: 100
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 11.9124 | 0.14 | 25 | 8.0029 | 1.0 | 0.9396 |
| 4.0619 | 0.29 | 50 | 3.1702 | 1.0 | 0.9850 |
| 2.5226 | 0.43 | 75 | 1.2813 | 1.0 | 0.3749 |
| 1.7097 | 0.57 | 100 | 1.0049 | 1.0 | 0.3041 |
| 1.4508 | 0.72 | 125 | 0.8869 | 1.0 | 0.2564 |
| 1.1873 | 0.86 | 150 | 0.8490 | 0.9984 | 0.2503 |
| 1.4657 | 1.01 | 175 | 0.8487 | 1.0 | 0.2513 |
| 1.0877 | 1.15 | 200 | 0.7699 | 0.9984 | 0.2352 |
| 1.3957 | 1.29 | 225 | 0.7402 | 0.9984 | 0.2271 |
| 1.1216 | 1.44 | 250 | 0.7486 | 0.9984 | 0.2228 |
| 1.2285 | 1.58 | 275 | 0.7122 | 0.9984 | 0.2191 |
| 1.24 | 1.72 | 300 | 0.6914 | 0.9984 | 0.2208 |
| 0.9623 | 1.87 | 325 | 0.6688 | 0.9984 | 0.2132 |
| 1.2324 | 2.01 | 350 | 0.6708 | 0.9984 | 0.2117 |
| 0.9558 | 2.16 | 375 | 0.6614 | 0.9984 | 0.2071 |
| 1.2007 | 2.3 | 400 | 0.7159 | 0.9984 | 0.2183 |
| 1.0645 | 2.44 | 425 | 0.7265 | 0.9984 | 0.2104 |
| 1.1051 | 2.59 | 450 | 0.8289 | 1.0 | 0.2172 |
| 1.6129 | 2.73 | 475 | 1.5108 | 1.0 | 0.3514 |
| 2.0501 | 2.87 | 500 | 1.6020 | 1.0 | 0.4407 |
| 2.0458 | 3.02 | 525 | 1.4441 | 1.0 | 0.4181 |
| 1.621 | 3.16 | 550 | 1.2917 | 1.0 | 0.3545 |
| 1.7942 | 3.3 | 575 | 1.4151 | 0.9984 | 0.2664 |
| 1.6505 | 3.45 | 600 | 1.2550 | 1.0 | 0.3075 |
| 1.7165 | 3.59 | 625 | 1.3912 | 1.0 | 0.3056 |
| 1.8114 | 3.74 | 650 | 1.2554 | 1.0 | 0.3100 |
| 1.6019 | 3.88 | 675 | 1.5515 | 1.0 | 0.2889 |
| 2.0484 | 4.02 | 700 | 1.3666 | 1.0 | 0.2826 |
| 1.7132 | 4.17 | 725 | 1.3629 | 1.0 | 0.3414 |
| 1.8599 | 4.31 | 750 | 1.3831 | 1.0 | 0.3355 |
| 1.8653 | 4.45 | 775 | 1.4025 | 1.0 | 0.3344 |
| 1.8246 | 4.6 | 800 | 1.4007 | 1.0 | 0.3110 |
| 1.9346 | 4.74 | 825 | 1.4022 | 1.0 | 0.3082 |
| 1.732 | 4.89 | 850 | 1.4023 | 1.0 | 0.3080 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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riturralde/my_awesome_keywords_model | 2023-10-27T00:34:54.000Z | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | riturralde | null | null | riturralde/my_awesome_keywords_model | 0 | 2 | transformers | 2023-10-27T00:21:53 | ---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: my_awesome_keywords_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_keywords_model
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Gen Len: 0.0
## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 1283 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 2.0 | 2566 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 3.0 | 3849 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 4.0 | 5132 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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syed789/t5-base-medium-title-generation | 2023-10-27T19:42:11.000Z | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | syed789 | null | null | syed789/t5-base-medium-title-generation | 0 | 2 | transformers | 2023-10-27T02:12:11 | ---
tags:
- generated_from_keras_callback
model-index:
- name: t5-base-medium-title-generation
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. -->
# t5-base-medium-title-generation
This model was trained from scratch 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 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
paragdakle/mistral-7b-cnndaily-small-lw-lora | 2023-10-27T03:03:30.000Z | [
"peft",
"region:us"
] | null | paragdakle | null | null | paragdakle/mistral-7b-cnndaily-small-lw-lora | 0 | 2 | peft | 2023-10-27T02:59:01 | ---
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
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
- PEFT 0.5.0
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NAGAsai95/test | 2023-10-27T07:05:19.000Z | [
"diffusers",
"text-to-image",
"autotrain",
"region:us"
] | text-to-image | NAGAsai95 | null | null | NAGAsai95/test | 0 | 2 | diffusers | 2023-10-27T04:21:09 |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of bhuvan
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
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spyobird/cs4248_albert-base-v2_bilstm_qa_1 | 2023-10-27T09:32:09.000Z | [
"transformers",
"pytorch",
"albert",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | spyobird | null | null | spyobird/cs4248_albert-base-v2_bilstm_qa_1 | 0 | 2 | transformers | 2023-10-27T08:05:53 | ---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: cs4248_albert-base-v2_bilstm_qa_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. -->
# cs4248_albert-base-v2_bilstm_qa
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 4248
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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gavulsim/distilbert_finetuned_yahoo_answers_topics | 2023-10-27T09:20:49.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:yahoo_answers_topics",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | gavulsim | null | null | gavulsim/distilbert_finetuned_yahoo_answers_topics | 0 | 2 | transformers | 2023-10-27T08:19:50 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- yahoo_answers_topics
metrics:
- accuracy
model-index:
- name: deberta_finetuned_yahoo_answers_topics
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yahoo_answers_topics
type: yahoo_answers_topics
config: yahoo_answers_topics
split: test
args: yahoo_answers_topics
metrics:
- name: Accuracy
type: accuracy
value: 0.71195
---
<!-- 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. -->
# deberta_finetuned_yahoo_answers_topics
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the yahoo_answers_topics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9096
- Accuracy: 0.7119
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 30000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1025 | 0.03 | 5000 | 1.0702 | 0.6717 |
| 1.0132 | 0.06 | 10000 | 0.9976 | 0.6834 |
| 0.8688 | 0.09 | 15000 | 0.9770 | 0.6961 |
| 0.9964 | 0.11 | 20000 | 0.9356 | 0.7020 |
| 0.9338 | 0.14 | 25000 | 0.9259 | 0.7090 |
| 0.9059 | 0.17 | 30000 | 0.9096 | 0.7119 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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MakAttack/653b71eb282fff95c3f30975 | 2023-10-27T08:54:17.000Z | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"license:openrail++",
"region:us"
] | text-to-image | MakAttack | null | null | MakAttack/653b71eb282fff95c3f30975 | 0 | 2 | diffusers | 2023-10-27T08:21:08 |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks dog
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - MakAttack/653b71eb282fff95c3f30975
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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MakAttack/653b6dc287d6147063c526f6 | 2023-10-27T09:26:59.000Z | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"license:openrail++",
"region:us"
] | text-to-image | MakAttack | null | null | MakAttack/653b6dc287d6147063c526f6 | 0 | 2 | diffusers | 2023-10-27T08:54:32 |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks dog
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - MakAttack/653b6dc287d6147063c526f6
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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s-man2099/gpl-2000 | 2023-10-27T10:19:41.000Z | [
"transformers",
"tf",
"pegasus",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | s-man2099 | null | null | s-man2099/gpl-2000 | 0 | 2 | transformers | 2023-10-27T08:57:18 | ---
base_model: google/pegasus-large
tags:
- generated_from_keras_callback
model-index:
- name: s-man2099/gpl-2000
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. -->
# s-man2099/gpl-2000
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.1254
- Validation Loss: 3.4150
- Epoch: 9
## 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': 'Adafactor', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-06, 'beta_2_decay': -0.8, 'epsilon_1': 1e-30, 'epsilon_2': 0.001, 'clip_threshold': 1.0, 'relative_step': True}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.9156 | 3.4538 | 0 |
| 3.6384 | 3.4017 | 1 |
| 3.5181 | 3.3878 | 2 |
| 3.4227 | 3.3802 | 3 |
| 3.3500 | 3.3862 | 4 |
| 3.2821 | 3.3914 | 5 |
| 3.2122 | 3.4035 | 6 |
| 3.1475 | 3.4099 | 7 |
| 3.1328 | 3.4132 | 8 |
| 3.1254 | 3.4150 | 9 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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budecosystem/sql-millennials-7b | 2023-10-27T15:48:33.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | budecosystem | null | null | budecosystem/sql-millennials-7b | 0 | 2 | transformers | 2023-10-27T10:06:40 | ---
license: apache-2.0
language:
- en
library_name: transformers
---
## Introducing Text-to-SQL Translation Model - Millennials. 🎉
Welcome to our Text-to-SQL Translation Model repository! Our model is specifically fine-tuned for text-to-SQL tasks, aiming to revolutionize how systems understand and translate natural language instructions into SQL queries. Built on Mistral 7B, our model has been meticulously fine-tuned with a curated dataset comprising 100k SQL query generation instructions, ensuring quality and precision.
## Features
* Specialized in converting natural language text to SQL queries.
* Fine-tuned on a diverse set of 100k SQL query generation instructions.
* Easy to integrate and use for generating SQL queries on the fly.
## Generate responses
Now that your model is fine-tuned, you're ready to generate responses, you can easily generate SQL queries from natural language instructions. To do this, you'll be using our generate.py script, which allows for quick inference and can fetch models directly from the Hugging Face model hub.
Here's a quick guide on how to use it:
The script runs inference using the pre-trained model from the Hugging Face model hub and prints the generated SQL query.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("budecosystem/sql-millennials-7b")
model = AutoModelForCausalLM.from_pretrained("budecosystem/sql-millennials-7b")
prompt = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: Create SQL query for the given table schema and question ASSISTANT:"
inputs = tokenizer(prompt, return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))
```
The script runs inference using the pre-trained model from the Hugging Face model hub and prints the generated SQL query.
## Training details
The model is trained of 4 A100 80GB for approximately 30hrs.
| Hyperparameters | Value |
| :----------------------------| :-----: |
| per_device_train_batch_size | 4 |
| gradient_accumulation_steps | 1 |
| epoch | 3 |
| steps | 19206 |
| learning_rate | 2e-5 |
| lr schedular type | cosine |
| warmup steps | 2000 |
| optimizer | adamw |
| fp16 | True |
| GPU | 4 A100 80GB |
## Why millennials?
1. Automated Database Management for Businesses
Scenario: Small to medium-sized enterprises (SMEs) often lack dedicated IT teams to handle database queries, making it challenging to retrieve specific data quickly for analysis and decision-making.
Use Case: Your text-to-SQL model can be integrated into a company's internal systems, allowing staff without technical SQL knowledge to retrieve data. They can input natural language requests, such as "Get a list of all transactions above $10,000 in the last quarter," and the system, powered by your model, would convert this into a corresponding SQL query to retrieve the data.
2. Automating Data Analytics Processes
Scenario: Data analysts and business professionals often face bottlenecks in generating insights due to the complexities of SQL query formulation, especially when immediate or repetitive data retrieval and analysis are required.
Use Case: Your text-to-SQL model serves as a transformative intermediary in this scenario. By integrating the model into their data analytics systems, organizations enable professionals to input data requests in natural language. For instance, an analyst could input, "Show the trend of online sales growth over the past five years," and the system would instantly convert this request into a SQL query, retrieve the data, and even integrate it into visualization tools for immediate insight generation. This functionality not only accelerates the analytical processes but also democratizes data-driven insights across different organizational departments, allowing even non-technical staff to leverage the power of real-time data analytics without deep knowledge of SQL.
3. Enhancing CMS Interfaces
Scenario: Content Management Systems (CMS) are often non-intuitive for non-technical content managers when it comes to complex data retrieval or database management.
Use Case: CMS providers can leverage your model to enhance their system's backend interface. Content managers can use natural language to request specific data, like "Find all blog posts in May 2023 with more than 500 views," and the model will generate the appropriate SQL to retrieve the information. This feature makes database management more accessible, efficient, and user-friendly.
4. Customer Support Optimization
Scenario: Customer support centers often need to retrieve client or product information stored in databases while resolving tickets or inquiries, requiring basic knowledge of SQL.
Use Case: Your model can be integrated into support ticketing systems, enabling support personnel to type requests in natural language, such as "Show all open tickets from customers in New York filed this month," and immediately receive the data needed to expedite their resolution process, improving customer service efficiency and response time.
5. Data Journalism and Research
Scenario: Journalists and researchers frequently rely on complex databases to gather insights and data points necessary for their work but may lack the technical know-how of SQL.
Use Case: By integrating your text-to-SQL model into research software or journalistic tools, professionals can query databases using natural language. For example, a journalist might input, "Retrieve the average household income in Texas in 2022," and your model would facilitate immediate access to this data, allowing for more efficient research and data-driven storytelling.
Contributing
We welcome contributions to help improve the model or address issues. Please feel free to submit pull requests or open issues to discuss changes or improvements.
### Acknowledgments
We'd like to thank the open-source community and the researchers whose foundational work laid the path to this model.
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minh21/XLNet-Toxic-Comment | 2023-10-27T11:25:35.000Z | [
"transformers",
"pytorch",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | minh21 | null | null | minh21/XLNet-Toxic-Comment | 0 | 2 | transformers | 2023-10-27T11:25:16 | ---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: XLNet-Toxic-Comment
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. -->
# XLNet-Toxic-Comment
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4431
- Rmse: 0.3106
- Accuracy: 0.9035
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:------:|:---------------:|:------:|:--------:|:---------:|:------:|:---:|
| 0.4641 | 1.0 | 61873 | 0.5115 | 0.3106 | 0.9035 | 0.0 | 0.0 | 0.0 |
| 0.5065 | 2.0 | 123746 | 0.4431 | 0.3106 | 0.9035 | 0.0 | 0.0 | 0.0 |
| 0.5033 | 3.0 | 185619 | 0.4734 | 0.3106 | 0.9035 | 0.0 | 0.0 | 0.0 |
| 0.5004 | 4.0 | 247492 | 0.4710 | 0.3106 | 0.9035 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
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A-Funakoshi/bert-base-japanese-v3-wrime-v2 | 2023-10-27T12:16:22.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ja",
"endpoints_compatible",
"region:us"
] | text-classification | A-Funakoshi | null | null | A-Funakoshi/bert-base-japanese-v3-wrime-v2 | 0 | 2 | transformers | 2023-10-27T12:05:45 | ---
language:
- ja
metrics:
- accuracy
- f1
---
- ベースモデル：cl-tohoku/bert-base-japanese-whole-word-masking
- データセット：llm-book/wrime-sentiment
- オプティマイザ: adamw
- Optunaでハイパーパラメータ探索
- 学習率スケジュールのタイプ(lr_scheduler_type): constant, linear, cosine
- 学習率(learning rate): 1e-6 ~ 1e-4
- バッチサイズ(per_device_train_batch_size): 16, 32, 64, 128, 256
- 正則化(weight_decay): 1e-6 ~ 1e-1
- Optunaでの探索結果は以下
- 学習率スケジュールタイプ(lr_scheduler_type): cosine
- 学習率(learning rate): 3.912141264809884e-05
- バッチサイズ(per_device_train_batch_size): 128
- 正則化(weight_decay): 5.220051265759252e-05
- Epoch: 100
- EarlyStopping: early_stopping_patience=3
このハイパーパラメータを使って再度finetuningした． | 663 | [
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s-man2099/gpl-2500 | 2023-10-27T14:51:48.000Z | [
"transformers",
"tf",
"pegasus",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | s-man2099 | null | null | s-man2099/gpl-2500 | 0 | 2 | transformers | 2023-10-27T12:28:15 | ---
base_model: google/pegasus-large
tags:
- generated_from_keras_callback
model-index:
- name: s-man2099/gpl-2500
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. -->
# s-man2099/gpl-2500
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0929
- Validation Loss: 3.4296
- Epoch: 9
## 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': 'Adafactor', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-06, 'beta_2_decay': -0.8, 'epsilon_1': 1e-30, 'epsilon_2': 0.001, 'clip_threshold': 1.0, 'relative_step': True}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.8496 | 3.4449 | 0 |
| 3.5895 | 3.4074 | 1 |
| 3.4644 | 3.3950 | 2 |
| 3.3848 | 3.3998 | 3 |
| 3.3088 | 3.4040 | 4 |
| 3.2404 | 3.4086 | 5 |
| 3.1828 | 3.4154 | 6 |
| 3.1157 | 3.4240 | 7 |
| 3.1111 | 3.4267 | 8 |
| 3.0929 | 3.4296 | 9 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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arieg/food_classifier_noaug_streaming | 2023-10-29T07:49:02.000Z | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | arieg | null | null | arieg/food_classifier_noaug_streaming | 0 | 2 | transformers | 2023-10-27T12:52:30 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: arieg/food_classifier_noaug_streaming
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. -->
# arieg/food_classifier_noaug_streaming
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4578
- Validation Loss: 1.3138
- Train Accuracy: 0.801
- Epoch: 4
## 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': 3e-05, 'decay_steps': 20000, '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 Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 3.1605 | 2.7599 | 0.602 | 0 |
| 1.6013 | 1.9823 | 0.67 | 1 |
| 0.9193 | 1.5901 | 0.699 | 2 |
| 0.6189 | 1.3822 | 0.712 | 3 |
| 0.4578 | 1.3138 | 0.801 | 4 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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mmnga/japanese-stablelm-base-gamma-7b-GPTQ-calib-ja-1k | 2023-11-03T08:31:40.000Z | [
"transformers",
"mistral",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | mmnga | null | null | mmnga/japanese-stablelm-base-gamma-7b-GPTQ-calib-ja-1k | 0 | 2 | transformers | 2023-10-27T13:19:35 | ---
license: apache-2.0
---
# japanese-stablelm-base-gamma-7b-GPTQ-calib-ja-1k
stabilityaiさんが公開している、[japanese-stablelm-base-gamma-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b)を、
日本語のキャリブレーションセットで生成したGPTQモデルになります。
キャリブレーションセットは[izumi-lab/wikipedia-ja-20230720](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720)から、
1kほどランダムサンプリングしています。
[mmnga/wikipedia-ja-20230720-1k](https://huggingface.co/datasets/mmnga/wikipedia-ja-20230720-1k)
他のモデルはこちら
AWQ
[mmnga/japanese-stablelm-base-gamma-7b-AWQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-base-gamma-7b-AWQ-calib-ja-1k)
[mmnga/japanese-stablelm-instruct-gamma-7b-AWQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-instruct-gamma-7b-AWQ-calib-ja-1k)
GPTQ
[mmnga/japanese-stablelm-base-gamma-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-base-gamma-7b-GPTQ-calib-ja-1k)
[mmnga/japanese-stablelm-instruct-gamma-7b-GPTQ-calib-ja-1k](https://huggingface.co/mmnga/japanese-stablelm-instruct-gamma-7b-GPTQ-calib-ja-1k)
GGUF
3bモデル
[mmnga/japanese-stablelm-3b-4e1t-base-gguf](https://huggingface.co/mmnga/japanese-stablelm-3b-4e1t-base-gguf)
[mmnga/japanese-stablelm-3b-4e1t-instruct-gguf](https://huggingface.co/mmnga/japanese-stablelm-3b-4e1t-instruct-gguf)
7bモデル
[mmnga/japanese-stablelm-base-gamma-7b-gguf](https://huggingface.co/mmnga/japanese-stablelm-base-gamma-7b-gguf)
[mmnga/japanese-stablelm-instruct-gamma-7b-gguf](https://huggingface.co/mmnga/japanese-stablelm-instruct-gamma-7b-gguf)
# Usage
~~~Bash
pip install auto-gptq==0.4.2 transformers
~~~
**AutoGPTQは現在Mistralに対応していないのでPRを持ってきます**
~~~python
!git clone --branch Mistral https://github.com/LaaZa/AutoGPTQ.git
!cp "/content/AutoGPTQ/auto_gptq/modeling/__init__.py" "/usr/local/lib/python3.10/dist-packages/auto_gptq/modeling/__init__.py"
!cp "/content/AutoGPTQ/auto_gptq/modeling/auto.py" "/usr/local/lib/python3.10/dist-packages/auto_gptq/modeling/auto.py"
!cp "/content/AutoGPTQ/auto_gptq/modeling/_const.py" "/usr/local/lib/python3.10/dist-packages/auto_gptq/modeling/_const.py"
!cp "/content/AutoGPTQ/auto_gptq/modeling/mistral.py" "/usr/local/lib/python3.10/dist-packages/auto_gptq/modeling/mistral.py"
~~~
~~~python
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from transformers import AutoTokenizer
model_name_or_path = "mmnga/japanese-stablelm-base-gamma-7b-GPTQ-calib-ja-1k"
# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
# Model
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, use_safetensors=True, device="cuda:0", use_auth_token=False)
#Your test prompt
prompt = """今日の晩御飯のレシピを紹介します。"""
print(tokenizer.decode(model.generate(**tokenizer(prompt, return_tensors="pt",add_special_tokens=False).to(model.device), max_new_tokens=100,do_sample=True,top_p=0.95,temperature=0.7)[0]))
~~~
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sangeet2020/asr-crdnn-rnnlm-commonvoice-10.0-de | 2023-10-27T16:48:55.000Z | [
"speechbrain",
"automatic-speech-recognition",
"CTC",
"NLL",
"Attention",
"pytorch",
"de",
"dataset:common_voice",
"arxiv:2106.04624",
"license:apache-2.0",
"region:us"
] | automatic-speech-recognition | sangeet2020 | null | null | sangeet2020/asr-crdnn-rnnlm-commonvoice-10.0-de | 0 | 2 | speechbrain | 2023-10-27T14:47:11 | ---
language: "de"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- NLL
- Attention
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- common_voice
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
<br/><br/>
# CRDNN with CTC/Attention trained on CommonVoice 10.0 German and RNNLM trained (with LM)
This repository provides all the necessary tools to perform automatic speech
recognition from an end-to-end system pretrained on CommonVoice (German Language) within
SpeechBrain. For a better experience, we encourage you to learn more about
[SpeechBrain](https://speechbrain.github.io).
The performance of the model is the following (after 25 epochs):
| Release | Test CER | Test WER | GPUs |
|:-------------:|:--------------:|:--------------:| :--------:|
| 15.08.23 | 2.85 | 7.92 | 1xRTXA6000 48GB |
## Pipeline description
This ASR system is composed with 3 different but linked blocks:
- Tokenizer (unigram) that transforms words into subword units and trained with
the train transcriptions of LibriSpeech.
- Neural language model (RNNLM) trained on the 17M sentences combining Tuda-De2 (8M sents), Leipzig news corpus (9M sents), and train transcripts of the CommonVoice corpus.
- Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
N blocks of convolutional neural networks with normalisation and pooling on the
frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain
the final acoustic representation that is given to the CTC and attention decoders.
The system is trained with recordings sampled at 16kHz (single channel).
The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
## Install SpeechBrain
First of all, please install SpeechBrain with the following command:
```
pip install speechbrain
```
Please notice that we encourage you to read our tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
### Transcribing your own audio files (in German)
```python
from speechbrain.pretrained import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(source="sangeet2020/asr-crdnn-rnnlm-commonvoice-10.0-de", savedir="pretrained_models/speechbrain/asr-crdnn-rnnlm-commonvoice-10.0-de")
asr_model.transcribe_file("speechbrain/speechbrain/asr-crdnn-rnnlm-commonvoice-10.0-de/example-de.wav")
```
### Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
## Parallel Inference on a Batch
Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.
### Training
The model was trained with SpeechBrain (986a2175).
To train it from scratch follows these steps:
1. Clone SpeechBrain:
```bash
git clone https://github.com/speechbrain/speechbrain/
```
2. Install it:
```
cd speechbrain
pip install -r requirements.txt
pip install -e .
```
3. Run Training:
```
cd recipes/CommonVoice/ASR/seq2seq
python train.py hparams/train_de.yaml --data_folder=your_data_folder
```
### Limitations
The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
``` | 4,380 | [
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LoneStriker/zephyr-7b-beta-6.0bpw-h6-exl2 | 2023-10-27T14:55:50.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"arxiv:2305.18290",
"arxiv:2310.16944",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/zephyr-7b-beta-6.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-27T14:55:33 | ---
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-beta
results: []
license: mit
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
base_model: mistralai/Mistral-7B-v0.1
---
<!-- 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. -->
<img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for Zephyr 7B β
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so and should only be used for educational and research purposes. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944).
## Model description
- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily English
- **License:** MIT
- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
- **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org
## Performance
At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks:
| Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |
|-------------|-----|----|---------------|--------------|
| StableLM-Tuned-α | 7B| dSFT |2.75| -|
| MPT-Chat | 7B |dSFT |5.42| -|
| Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83|
| Mistral-Instructv0.1 | 7B| - | 6.84 |-|
| Zephyr-7b-α |7B| dDPO| 6.88| -|
| **Zephyr-7b-β** 🪁 | **7B** | **dDPO** | **7.34** | **90.60** |
| Falcon-Instruct | 40B |dSFT |5.17 |45.71|
| Guanaco | 65B | SFT |6.41| 71.80|
| Llama2-Chat | 70B |RLHF |6.86| 92.66|
| Vicuna v1.3 | 33B |dSFT |7.12 |88.99|
| WizardLM v1.0 | 70B |dSFT |7.71 |-|
| Xwin-LM v0.1 | 70B |dPPO |- |95.57|
| GPT-3.5-turbo | - |RLHF |7.94 |89.37|
| Claude 2 | - |RLHF |8.06| 91.36|
| GPT-4 | -| RLHF |8.99| 95.28|
In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:
However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap.
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities.
You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66)
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Zephyr-7B-β has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
## Training and evaluation data
During DPO training, this model achieves the following results on the evaluation set:
- Loss: 0.7496
- Rewards/chosen: -4.5221
- Rewards/rejected: -8.3184
- Rewards/accuracies: 0.7812
- Rewards/margins: 3.7963
- Logps/rejected: -340.1541
- Logps/chosen: -299.4561
- Logits/rejected: -2.3081
- Logits/chosen: -2.3531
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
The table below shows the full set of DPO training metrics:
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6284 | 0.05 | 100 | 0.6098 | 0.0425 | -0.1872 | 0.7344 | 0.2297 | -258.8416 | -253.8099 | -2.7976 | -2.8234 |
| 0.4908 | 0.1 | 200 | 0.5426 | -0.0279 | -0.6842 | 0.75 | 0.6563 | -263.8124 | -254.5145 | -2.7719 | -2.7960 |
| 0.5264 | 0.15 | 300 | 0.5324 | 0.0414 | -0.9793 | 0.7656 | 1.0207 | -266.7627 | -253.8209 | -2.7892 | -2.8122 |
| 0.5536 | 0.21 | 400 | 0.4957 | -0.0185 | -1.5276 | 0.7969 | 1.5091 | -272.2460 | -254.4203 | -2.8542 | -2.8764 |
| 0.5362 | 0.26 | 500 | 0.5031 | -0.2630 | -1.5917 | 0.7812 | 1.3287 | -272.8869 | -256.8653 | -2.8702 | -2.8958 |
| 0.5966 | 0.31 | 600 | 0.5963 | -0.2993 | -1.6491 | 0.7812 | 1.3499 | -273.4614 | -257.2279 | -2.8778 | -2.8986 |
| 0.5014 | 0.36 | 700 | 0.5382 | -0.2859 | -1.4750 | 0.75 | 1.1891 | -271.7204 | -257.0942 | -2.7659 | -2.7869 |
| 0.5334 | 0.41 | 800 | 0.5677 | -0.4289 | -1.8968 | 0.7969 | 1.4679 | -275.9378 | -258.5242 | -2.7053 | -2.7265 |
| 0.5251 | 0.46 | 900 | 0.5772 | -0.2116 | -1.3107 | 0.7344 | 1.0991 | -270.0768 | -256.3507 | -2.8463 | -2.8662 |
| 0.5205 | 0.52 | 1000 | 0.5262 | -0.3792 | -1.8585 | 0.7188 | 1.4793 | -275.5552 | -258.0276 | -2.7893 | -2.7979 |
| 0.5094 | 0.57 | 1100 | 0.5433 | -0.6279 | -1.9368 | 0.7969 | 1.3089 | -276.3377 | -260.5136 | -2.7453 | -2.7536 |
| 0.5837 | 0.62 | 1200 | 0.5349 | -0.3780 | -1.9584 | 0.7656 | 1.5804 | -276.5542 | -258.0154 | -2.7643 | -2.7756 |
| 0.5214 | 0.67 | 1300 | 0.5732 | -1.0055 | -2.2306 | 0.7656 | 1.2251 | -279.2761 | -264.2903 | -2.6986 | -2.7113 |
| 0.6914 | 0.72 | 1400 | 0.5137 | -0.6912 | -2.1775 | 0.7969 | 1.4863 | -278.7448 | -261.1467 | -2.7166 | -2.7275 |
| 0.4655 | 0.77 | 1500 | 0.5090 | -0.7987 | -2.2930 | 0.7031 | 1.4943 | -279.8999 | -262.2220 | -2.6651 | -2.6838 |
| 0.5731 | 0.83 | 1600 | 0.5312 | -0.8253 | -2.3520 | 0.7812 | 1.5268 | -280.4902 | -262.4876 | -2.6543 | -2.6728 |
| 0.5233 | 0.88 | 1700 | 0.5206 | -0.4573 | -2.0951 | 0.7812 | 1.6377 | -277.9205 | -258.8084 | -2.6870 | -2.7097 |
| 0.5593 | 0.93 | 1800 | 0.5231 | -0.5508 | -2.2000 | 0.7969 | 1.6492 | -278.9703 | -259.7433 | -2.6221 | -2.6519 |
| 0.4967 | 0.98 | 1900 | 0.5290 | -0.5340 | -1.9570 | 0.8281 | 1.4230 | -276.5395 | -259.5749 | -2.6564 | -2.6878 |
| 0.0921 | 1.03 | 2000 | 0.5368 | -1.1376 | -3.1615 | 0.7812 | 2.0239 | -288.5854 | -265.6111 | -2.6040 | -2.6345 |
| 0.0733 | 1.08 | 2100 | 0.5453 | -1.1045 | -3.4451 | 0.7656 | 2.3406 | -291.4208 | -265.2799 | -2.6289 | -2.6595 |
| 0.0972 | 1.14 | 2200 | 0.5571 | -1.6915 | -3.9823 | 0.8125 | 2.2908 | -296.7934 | -271.1505 | -2.6471 | -2.6709 |
| 0.1058 | 1.19 | 2300 | 0.5789 | -1.0621 | -3.8941 | 0.7969 | 2.8319 | -295.9106 | -264.8563 | -2.5527 | -2.5798 |
| 0.2423 | 1.24 | 2400 | 0.5455 | -1.1963 | -3.5590 | 0.7812 | 2.3627 | -292.5599 | -266.1981 | -2.5414 | -2.5784 |
| 0.1177 | 1.29 | 2500 | 0.5889 | -1.8141 | -4.3942 | 0.7969 | 2.5801 | -300.9120 | -272.3761 | -2.4802 | -2.5189 |
| 0.1213 | 1.34 | 2600 | 0.5683 | -1.4608 | -3.8420 | 0.8125 | 2.3812 | -295.3901 | -268.8436 | -2.4774 | -2.5207 |
| 0.0889 | 1.39 | 2700 | 0.5890 | -1.6007 | -3.7337 | 0.7812 | 2.1330 | -294.3068 | -270.2423 | -2.4123 | -2.4522 |
| 0.0995 | 1.45 | 2800 | 0.6073 | -1.5519 | -3.8362 | 0.8281 | 2.2843 | -295.3315 | -269.7538 | -2.4685 | -2.5050 |
| 0.1145 | 1.5 | 2900 | 0.5790 | -1.7939 | -4.2876 | 0.8438 | 2.4937 | -299.8461 | -272.1744 | -2.4272 | -2.4674 |
| 0.0644 | 1.55 | 3000 | 0.5735 | -1.7285 | -4.2051 | 0.8125 | 2.4766 | -299.0209 | -271.5201 | -2.4193 | -2.4574 |
| 0.0798 | 1.6 | 3100 | 0.5537 | -1.7226 | -4.2850 | 0.8438 | 2.5624 | -299.8200 | -271.4610 | -2.5367 | -2.5696 |
| 0.1013 | 1.65 | 3200 | 0.5575 | -1.5715 | -3.9813 | 0.875 | 2.4098 | -296.7825 | -269.9498 | -2.4926 | -2.5267 |
| 0.1254 | 1.7 | 3300 | 0.5905 | -1.6412 | -4.4703 | 0.8594 | 2.8291 | -301.6730 | -270.6473 | -2.5017 | -2.5340 |
| 0.085 | 1.76 | 3400 | 0.6133 | -1.9159 | -4.6760 | 0.8438 | 2.7601 | -303.7296 | -273.3941 | -2.4614 | -2.4960 |
| 0.065 | 1.81 | 3500 | 0.6074 | -1.8237 | -4.3525 | 0.8594 | 2.5288 | -300.4951 | -272.4724 | -2.4597 | -2.5004 |
| 0.0755 | 1.86 | 3600 | 0.5836 | -1.9252 | -4.4005 | 0.8125 | 2.4753 | -300.9748 | -273.4872 | -2.4327 | -2.4716 |
| 0.0746 | 1.91 | 3700 | 0.5789 | -1.9280 | -4.4906 | 0.8125 | 2.5626 | -301.8762 | -273.5149 | -2.4686 | -2.5115 |
| 0.1348 | 1.96 | 3800 | 0.6015 | -1.8658 | -4.2428 | 0.8281 | 2.3769 | -299.3976 | -272.8936 | -2.4943 | -2.5393 |
| 0.0217 | 2.01 | 3900 | 0.6122 | -2.3335 | -4.9229 | 0.8281 | 2.5894 | -306.1988 | -277.5699 | -2.4841 | -2.5272 |
| 0.0219 | 2.07 | 4000 | 0.6522 | -2.9890 | -6.0164 | 0.8281 | 3.0274 | -317.1334 | -284.1248 | -2.4105 | -2.4545 |
| 0.0119 | 2.12 | 4100 | 0.6922 | -3.4777 | -6.6749 | 0.7969 | 3.1972 | -323.7187 | -289.0121 | -2.4272 | -2.4699 |
| 0.0153 | 2.17 | 4200 | 0.6993 | -3.2406 | -6.6775 | 0.7969 | 3.4369 | -323.7453 | -286.6413 | -2.4047 | -2.4465 |
| 0.011 | 2.22 | 4300 | 0.7178 | -3.7991 | -7.4397 | 0.7656 | 3.6406 | -331.3667 | -292.2260 | -2.3843 | -2.4290 |
| 0.0072 | 2.27 | 4400 | 0.6840 | -3.3269 | -6.8021 | 0.8125 | 3.4752 | -324.9908 | -287.5042 | -2.4095 | -2.4536 |
| 0.0197 | 2.32 | 4500 | 0.7013 | -3.6890 | -7.3014 | 0.8125 | 3.6124 | -329.9841 | -291.1250 | -2.4118 | -2.4543 |
| 0.0182 | 2.37 | 4600 | 0.7476 | -3.8994 | -7.5366 | 0.8281 | 3.6372 | -332.3356 | -293.2291 | -2.4163 | -2.4565 |
| 0.0125 | 2.43 | 4700 | 0.7199 | -4.0560 | -7.5765 | 0.8438 | 3.5204 | -332.7345 | -294.7952 | -2.3699 | -2.4100 |
| 0.0082 | 2.48 | 4800 | 0.7048 | -3.6613 | -7.1356 | 0.875 | 3.4743 | -328.3255 | -290.8477 | -2.3925 | -2.4303 |
| 0.0118 | 2.53 | 4900 | 0.6976 | -3.7908 | -7.3152 | 0.8125 | 3.5244 | -330.1224 | -292.1431 | -2.3633 | -2.4047 |
| 0.0118 | 2.58 | 5000 | 0.7198 | -3.9049 | -7.5557 | 0.8281 | 3.6508 | -332.5271 | -293.2844 | -2.3764 | -2.4194 |
| 0.006 | 2.63 | 5100 | 0.7506 | -4.2118 | -7.9149 | 0.8125 | 3.7032 | -336.1194 | -296.3530 | -2.3407 | -2.3860 |
| 0.0143 | 2.68 | 5200 | 0.7408 | -4.2433 | -7.9802 | 0.8125 | 3.7369 | -336.7721 | -296.6682 | -2.3509 | -2.3946 |
| 0.0057 | 2.74 | 5300 | 0.7552 | -4.3392 | -8.0831 | 0.7969 | 3.7439 | -337.8013 | -297.6275 | -2.3388 | -2.3842 |
| 0.0138 | 2.79 | 5400 | 0.7404 | -4.2395 | -7.9762 | 0.8125 | 3.7367 | -336.7322 | -296.6304 | -2.3286 | -2.3737 |
| 0.0079 | 2.84 | 5500 | 0.7525 | -4.4466 | -8.2196 | 0.7812 | 3.7731 | -339.1662 | -298.7007 | -2.3200 | -2.3641 |
| 0.0077 | 2.89 | 5600 | 0.7520 | -4.5586 | -8.3485 | 0.7969 | 3.7899 | -340.4545 | -299.8206 | -2.3078 | -2.3517 |
| 0.0094 | 2.94 | 5700 | 0.7527 | -4.5542 | -8.3509 | 0.7812 | 3.7967 | -340.4790 | -299.7773 | -2.3062 | -2.3510 |
| 0.0054 | 2.99 | 5800 | 0.7520 | -4.5169 | -8.3079 | 0.7812 | 3.7911 | -340.0493 | -299.4038 | -2.3081 | -2.3530 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.14.0
## Citation
If you find Zephyr-7B-β is useful in your work, please cite it with:
```
@misc{tunstall2023zephyr,
title={Zephyr: Direct Distillation of LM Alignment},
author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
year={2023},
eprint={2310.16944},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` | 19,420 | [
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maddiehope/airlinetweets | 2023-10-27T19:02:18.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | maddiehope | null | null | maddiehope/airlinetweets | 0 | 2 | transformers | 2023-10-27T15:18:02 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: airlinetweets
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. -->
# airlinetweets
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6723
## 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: 16
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5255 | 1.0 | 641 | 0.4095 |
| 0.3334 | 2.0 | 1282 | 0.4872 |
| 0.2082 | 3.0 | 1923 | 0.6723 |
### Framework versions
- Transformers 4.34.1
- Pytorch 1.12.1
- Datasets 2.14.6
- Tokenizers 0.14.1
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sahilnagaralu/movie-script-generator | 2023-10-31T19:41:44.000Z | [
"transformers",
"pytorch",
"bart",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | sahilnagaralu | null | null | sahilnagaralu/movie-script-generator | 0 | 2 | transformers | 2023-10-27T15:58:09 | ---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: movie-script-generator
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. -->
# movie-script-generator
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7368
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 47 | 6.7213 |
| No log | 2.0 | 94 | 4.6579 |
| No log | 3.0 | 141 | 3.7368 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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RossAscends/Mistral_7B_Dolphin2.1_LIMA0.5_fp16 | 2023-10-28T11:20:53.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | RossAscends | null | null | RossAscends/Mistral_7B_Dolphin2.1_LIMA0.5_fp16 | 2 | 2 | transformers | 2023-10-27T16:19:35 | ---
license: mit
---
ehartford's merge of Mistral 7B 0.1 with his Dolphin 2.1 dataset
https://huggingface.co/ehartford/dolphin-2.1-mistral-7b
and
LIMA RP dataset applied as a lora at 0.5 weight
https://huggingface.co/lemonilia/limarp-llama2-v2/
Purpose of the model is to be RP-focused, smart, fast, and lightweight for users with low VRAM.
I've already built the exl2 4bpw quant (linked below), and it will run 8k ctx at around 6GB VRAM and respond to a full context at roughly 30tps (tested on my 3060) if exl2_hf loader is used with FA2 enabled.
Model has been tested by several users on the SillyTavern discord server, and run on Horde for a full day - with good results.
https://huggingface.co/RossAscends/Mistral7B_Dolphin2.1_LIMARP0.5_4bpw_exl2
Mistral or ChatML context presets both possible.
exllama v2 4bpw quant: https://huggingface.co/RossAscends/Mistral7B_Dolphin2.1_LIMARP0.5_4bpw_exl2
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keylazy/bert-finetuned-ner | 2023-10-27T17:55:22.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | keylazy | null | null | keylazy/bert-finetuned-ner | 0 | 2 | transformers | 2023-10-27T17:37:44 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: bert-finetuned-ner
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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 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: 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
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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TheBloke/AshhLimaRP-Mistral-7B-GGUF | 2023-10-27T20:10:01.000Z | [
"transformers",
"mistral",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | TheBloke | null | null | TheBloke/AshhLimaRP-Mistral-7B-GGUF | 0 | 2 | transformers | 2023-10-27T20:00:13 | ---
base_model: lemonilia/AshhLimaRP-Mistral-7B
inference: false
license: apache-2.0
model_creator: Suikamelon
model_name: AshhLimaRP Mistral 7B
model_type: mistral
prompt_template: "### Instruction:\nCharacter's Persona: bot character description\n\
\nUser's persona: user character description\n \nScenario: what happens in the\
\ story\n\nPlay the role of Character. You must engage in a roleplaying chat with\
\ User below this line. Do not write dialogues and narration for User. Character\
\ should respond with messages of medium length.\n\n### Input:\nUser: {prompt}\n\
\n### Response:\nCharacter: \n"
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
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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 -->
# AshhLimaRP Mistral 7B - GGUF
- Model creator: [Suikamelon](https://huggingface.co/lemonilia)
- Original model: [AshhLimaRP Mistral 7B](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Suikamelon's AshhLimaRP Mistral 7B](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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/AshhLimaRP-Mistral-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF)
* [Suikamelon's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: LimaRP-Alpaca
```
### Instruction:
Character's Persona: bot character description
User's persona: user character description
Scenario: what happens in the story
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length.
### Input:
User: {prompt}
### Response:
Character:
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [ashhlimarp-mistral-7b.Q2_K.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [ashhlimarp-mistral-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [ashhlimarp-mistral-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [ashhlimarp-mistral-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [ashhlimarp-mistral-7b.Q4_0.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [ashhlimarp-mistral-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [ashhlimarp-mistral-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [ashhlimarp-mistral-7b.Q5_0.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [ashhlimarp-mistral-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [ashhlimarp-mistral-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [ashhlimarp-mistral-7b.Q6_K.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [ashhlimarp-mistral-7b.Q8_0.gguf](https://huggingface.co/TheBloke/AshhLimaRP-Mistral-7B-GGUF/blob/main/ashhlimarp-mistral-7b.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/AshhLimaRP-Mistral-7B-GGUF and below it, a specific filename to download, such as: ashhlimarp-mistral-7b.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/AshhLimaRP-Mistral-7B-GGUF ashhlimarp-mistral-7b.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/AshhLimaRP-Mistral-7B-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/AshhLimaRP-Mistral-7B-GGUF ashhlimarp-mistral-7b.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 ashhlimarp-mistral-7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction:\nCharacter's Persona: bot character description\n\nUser's persona: user character description\n \nScenario: what happens in the story\n\nPlay the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length.\n\n### Input:\nUser: {prompt}\n\n### Response:\nCharacter:"
```
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/AshhLimaRP-Mistral-7B-GGUF", model_file="ashhlimarp-mistral-7b.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
* 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: Suikamelon's AshhLimaRP Mistral 7B
# AshhLimaRP-Mistral-7B (Alpaca, v1)
This is a version of LimaRP with 2000 training samples _up to_ about 9k tokens length
finetuned on [Ashhwriter-Mistral-7B](https://huggingface.co/lemonilia/Ashhwriter-Mistral-7B).
LimaRP is a longform-oriented, novel-style roleplaying chat model intended to replicate the experience
of 1-on-1 roleplay on Internet forums. Short-form, IRC/Discord-style RP (aka "Markdown format")
is not supported. The model does not include instruction tuning, only manually picked and
slightly edited RP conversations with persona and scenario data.
Ashhwriter, the base, is a model entirely finetuned on human-written lewd stories.
## Available versions
- Float16 HF weights
- LoRA Adapter ([adapter_config.json](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/adapter_config.json) and [adapter_model.bin](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/adapter_model.bin))
- [4bit AWQ](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/tree/main/AWQ)
- [Q4_K_M GGUF](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/AshhLimaRP-Mistral-7B.Q4_K_M.gguf)
- [Q6_K GGUF](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/AshhLimaRP-Mistral-7B.Q6_K.gguf)
## Prompt format
[Extended Alpaca format](https://github.com/tatsu-lab/stanford_alpaca),
with `### Instruction:`, `### Input:` immediately preceding user inputs and `### Response:`
immediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn
responses, in practice this is not a problem; the format follows a pattern already used by
other models.
```
### Instruction:
Character's Persona: {bot character description}
User's Persona: {user character description}
Scenario: {what happens in the story}
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User.
### Input:
User: {utterance}
### Response:
Character: {utterance}
### Input
User: {utterance}
### Response:
Character: {utterance}
(etc.)
```
You should:
- Replace all text in curly braces (curly braces included) with your own text.
- Replace `User` and `Character` with appropriate names.
### Message length control
Inspired by the previously named "Roleplay" preset in SillyTavern, with this
version of LimaRP it is possible to append a length modifier to the response instruction
sequence, like this:
```
### Input
User: {utterance}
### Response: (length = medium)
Character: {utterance}
```
This has an immediately noticeable effect on bot responses. The lengths using during training are:
`micro`, `tiny`, `short`, `medium`, `long`, `massive`, `huge`, `enormous`, `humongous`, `unlimited`.
**The recommended starting length is medium**. Keep in mind that the AI can ramble or impersonate
the user with very long messages.
The length control effect is reproducible, but the messages will not necessarily follow
lengths very precisely, rather follow certain ranges on average, as seen in this table
with data from tests made with one reply at the beginning of the conversation:
Response length control appears to work well also deep into the conversation. **By omitting
the modifier, the model will choose the most appropriate response length** (although it might
not necessarily be what the user desires).
## Suggested settings
You can follow these instruction format settings in SillyTavern. Replace `medium` with
your desired response length:
## Text generation settings
These settings could be a good general starting point:
- TFS = 0.90
- Temperature = 0.70
- Repetition penalty = ~1.11
- Repetition penalty range = ~2048
- top-k = 0 (disabled)
- top-p = 1 (disabled)
## Training procedure
[Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on 2x NVidia A40 GPUs.
The A40 GPUs have been graciously provided by [Arc Compute](https://www.arccompute.io/).
### Training hyperparameters
A lower learning rate than usual was employed. Due to an unforeseen issue the training
was cut short and as a result 3 epochs were trained instead of the planned 4. Using 2 GPUs,
the effective global batch size would have been 16.
Training was continued from the most recent LoRA adapter from Ashhwriter, using the same
LoRA R and LoRA alpha.
- lora_model_dir: /home/anon/bin/axolotl/OUT_mistral-stories/checkpoint-6000/
- learning_rate: 0.00005
- lr_scheduler: cosine
- noisy_embedding_alpha: 3.5
- num_epochs: 4
- sequence_len: 8750
- lora_r: 256
- lora_alpha: 16
- lora_dropout: 0.05
- lora_target_linear: True
- bf16: True
- fp16: false
- tf32: True
- load_in_8bit: True
- adapter: lora
- micro_batch_size: 2
- optimizer: adamw_bnb_8bit
- warmup_steps: 10
- optimizer: adamw_torch
- flash_attention: true
- sample_packing: true
- pad_to_sequence_len: true
### Loss graphs
Values are higher than typical because the training is performed on the entire
sample, similar to unsupervised finetuning.
#### Train loss
#### Eval loss
<!-- original-model-card end -->
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valinor/classification-adapters-llama7b-4bit-lorar64 | 2023-10-27T20:09:49.000Z | [
"peft",
"region:us"
] | null | valinor | null | null | valinor/classification-adapters-llama7b-4bit-lorar64 | 0 | 2 | peft | 2023-10-27T20:09:43 | ---
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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colemane/ppo-Pyramids1 | 2023-10-27T22:40:48.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | colemane | null | null | colemane/ppo-Pyramids1 | 0 | 2 | ml-agents | 2023-10-27T22:40:45 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: colemane/ppo-Pyramids1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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vincegmz/dreamboost_lora_mnist_zero_A_photo_of_zero_with_color_background | 2023-10-27T23:44:27.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | vincegmz | null | null | vincegmz/dreamboost_lora_mnist_zero_A_photo_of_zero_with_color_background | 0 | 2 | diffusers | 2023-10-27T23:09:30 |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of zero with black background
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - vincegmz/dreamboost_lora_mnist_zero_A_photo_of_zero_with_color_background
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of zero with black background using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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] |
vincegmz/dreamboost_lora_mnist_zero | 2023-10-28T00:19:05.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | vincegmz | null | null | vincegmz/dreamboost_lora_mnist_zero | 0 | 2 | diffusers | 2023-10-27T23:48:06 |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of zero with black background
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - vincegmz/dreamboost_lora_mnist_zero
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of zero with black background using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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] |
immich-app/RN50__openai | 2023-10-29T03:24:52.000Z | [
"transformers",
"onnx",
"immich",
"clip",
"endpoints_compatible",
"region:us"
] | null | immich-app | null | null | immich-app/RN50__openai | 0 | 2 | transformers | 2023-10-28T01:45:26 | ---
tags:
- immich
- clip
---
# Model Description
This repo contains ONNX exports for the corresponding ResNet-50-based CLIP model by OpenAI. See the [CLIP](https://github.com/openai/CLIP/tree/main) repo for more info.
Visual and textual encoders are separated into separate models for the purpose of generating image and text embeddings.
This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library.
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benjipeng/ppo-LunarLander-v2 | 2023-10-31T00:01:13.000Z | [
"transformers",
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | benjipeng | null | null | benjipeng/ppo-LunarLander-v2 | 0 | 2 | transformers | 2023-10-28T01:58:19 | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -163.76 +/- 70.87
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 20000
'learning_rate': 0.0001
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 10
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'caioiglesias/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
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keylazy/distilbert-base-uncased-finetuned-imdb | 2023-10-28T18:09:40.000Z | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | keylazy | null | null | keylazy/distilbert-base-uncased-finetuned-imdb | 0 | 2 | transformers | 2023-10-28T02:10:41 | ---
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
### 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 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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toobiza/margin-element-detector-fm-clean-oath-16 | 2023-10-28T03:58:57.000Z | [
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | object-detection | toobiza | null | null | toobiza/margin-element-detector-fm-clean-oath-16 | 0 | 2 | transformers | 2023-10-28T02:18:21 | ---
base_model: toobiza/MT-ancient-spaceship-83
tags:
- generated_from_trainer
model-index:
- name: margin-element-detector-fm-clean-oath-16
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. -->
# margin-element-detector-fm-clean-oath-16
This model is a fine-tuned version of [toobiza/MT-ancient-spaceship-83](https://huggingface.co/toobiza/MT-ancient-spaceship-83) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9676
- eval_loss_ce: 0.0858
- eval_loss_bbox: 0.0424
- eval_cardinality_error: 0.3683
- eval_giou: 66.5256
- eval_runtime: 47.1617
- eval_samples_per_second: 18.977
- eval_steps_per_second: 4.75
- epoch: 7.53
- step: 17000
## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
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vincegmz/dreamboost_lora_mnistm_zero_batch_size1_with_prior_preservation | 2023-10-28T02:44:21.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | vincegmz | null | null | vincegmz/dreamboost_lora_mnistm_zero_batch_size1_with_prior_preservation | 0 | 2 | diffusers | 2023-10-28T02:40:03 |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of color zero
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - vincegmz/dreamboost_lora_mnistm_zero_batch_size1_with_prior_preservation
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of color zero using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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vincegmz/dreamboost_lora_mnistm_zero_batch_size1_with_prior_preservaiton | 2023-10-28T03:13:28.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | vincegmz | null | null | vincegmz/dreamboost_lora_mnistm_zero_batch_size1_with_prior_preservaiton | 1 | 2 | diffusers | 2023-10-28T03:05:36 |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of color zero
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - vincegmz/dreamboost_lora_mnistm_zero_batch_size1_with_prior_preservaiton
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of color zero using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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leeboykt/codeparrot-ds | 2023-10-28T05:10:35.000Z | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | leeboykt | null | null | leeboykt/codeparrot-ds | 0 | 2 | transformers | 2023-10-28T03:16:50 | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: codeparrot-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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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vincegmz/dreamboost_lora_mnistm_zero_batch_size1_with_prior_preservaiton_loss_weight0.5 | 2023-10-28T03:41:44.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | vincegmz | null | null | vincegmz/dreamboost_lora_mnistm_zero_batch_size1_with_prior_preservaiton_loss_weight0.5 | 1 | 2 | diffusers | 2023-10-28T03:37:24 |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of color zero
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - vincegmz/dreamboost_lora_mnistm_zero_batch_size1_with_prior_preservaiton_loss_weight0.5
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of color zero using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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abdullah0x/bert-finetuned-ner | 2023-10-28T05:48:47.000Z | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | abdullah0x | null | null | abdullah0x/bert-finetuned-ner | 0 | 2 | transformers | 2023-10-28T05:35:40 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: abdullah0x/bert-finetuned-ner
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. -->
# abdullah0x/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0274
- Validation Loss: 0.0533
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1790 | 0.0639 | 0 |
| 0.0480 | 0.0538 | 1 |
| 0.0274 | 0.0533 | 2 |
### Framework versions
- Transformers 4.33.0
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
| 1,582 | [
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Chhabi/my_awesome_qa_model | 2023-10-28T08:51:53.000Z | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | Chhabi | null | null | Chhabi/my_awesome_qa_model | 0 | 2 | transformers | 2023-10-28T07:10:23 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- squad
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 the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2907
## 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 | 25 | 4.9082 |
| No log | 2.0 | 50 | 4.3927 |
| No log | 3.0 | 75 | 4.2907 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF | 2023-10-28T11:40:50.000Z | [
"transformers",
"mistral",
"license:mit",
"text-generation-inference",
"region:us"
] | null | TheBloke | null | null | TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF | 2 | 2 | transformers | 2023-10-28T07:50:04 | ---
base_model: RossAscends/Mistral_7B_Dolphin2.1_LIMA0.5_fp16
inference: false
license: mit
model_creator: Ross Ascends
model_name: Mistral 7B Dolphin2.1 Lima0.5
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# Mistral 7B Dolphin2.1 Lima0.5 - GGUF
- Model creator: [Ross Ascends](https://huggingface.co/RossAscends)
- Original model: [Mistral 7B Dolphin2.1 Lima0.5](https://huggingface.co/RossAscends/Mistral_7B_Dolphin2.1_LIMA0.5_fp16)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Ross Ascends's Mistral 7B Dolphin2.1 Lima0.5](https://huggingface.co/RossAscends/Mistral_7B_Dolphin2.1_LIMA0.5_fp16).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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/Mistral_7B_Dolphin2.1_LIMA0.5-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF)
* [Ross Ascends's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/RossAscends/Mistral_7B_Dolphin2.1_LIMA0.5_fp16)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistral_7b_dolphin2.1_lima0.5.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mistral_7b_dolphin2.1_lima0.5.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [mistral_7b_dolphin2.1_lima0.5.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mistral_7b_dolphin2.1_lima0.5.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mistral_7b_dolphin2.1_lima0.5.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mistral_7b_dolphin2.1_lima0.5.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mistral_7b_dolphin2.1_lima0.5.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mistral_7b_dolphin2.1_lima0.5.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mistral_7b_dolphin2.1_lima0.5.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mistral_7b_dolphin2.1_lima0.5.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mistral_7b_dolphin2.1_lima0.5.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mistral_7b_dolphin2.1_lima0.5.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF/blob/main/mistral_7b_dolphin2.1_lima0.5.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/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF and below it, a specific filename to download, such as: mistral_7b_dolphin2.1_lima0.5.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/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF mistral_7b_dolphin2.1_lima0.5.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/Mistral_7B_Dolphin2.1_LIMA0.5-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/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF mistral_7b_dolphin2.1_lima0.5.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 mistral_7b_dolphin2.1_lima0.5.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
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/Mistral_7B_Dolphin2.1_LIMA0.5-GGUF", model_file="mistral_7b_dolphin2.1_lima0.5.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
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
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: Ross Ascends's Mistral 7B Dolphin2.1 Lima0.5
ehartford's merge of Mistral 7B 0.1 with his Dolphin 2.1 dataset
https://huggingface.co/ehartford/dolphin-2.1-mistral-7b
and
LIMA RP dataset applied as a lora at 0.5 weight
https://huggingface.co/lemonilia/limarp-llama2-v2/
Purpose of the model is to be RP-focused, smart, fast, and lightweight for users with low VRAM.
I've already built the exl2 4bpw quant (linked below), and it will run 8k ctx at around 6GB VRAM and respond to a full context at roughly 30tps (tested on my 3060) if exl2_hf loader is used with FA2 enabled.
Model has been tested by several users on the SillyTavern discord server, and run on Horde for a full day - with good results.
https://huggingface.co/RossAscends/Mistral7B_Dolphin2.1_LIMARP0.5_4bpw_exl2
Mistral or ChatML context presets both possible.
exllama v2 4bpw quant: https://huggingface.co/RossAscends/Mistral7B_Dolphin2.1_LIMARP0.5_4bpw_exl2
<!-- original-model-card end -->
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borggAI/CollectiveCognition-v1-1-Mistral-7B-fnv2 | 2023-10-28T08:45:31.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"text-generation-inference",
"region:us"
] | text-generation | borggAI | null | null | borggAI/CollectiveCognition-v1-1-Mistral-7B-fnv2 | 0 | 2 | transformers | 2023-10-28T08:40:31 | ---
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: [teknium/CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.34.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="borggAI/CollectiveCognition-v1-1-Mistral-7B-fnv2",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
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
Why is drinking water so healthy?</s>
```
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
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"borggAI/CollectiveCognition-v1-1-Mistral-7B-fnv2",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"borggAI/CollectiveCognition-v1-1-Mistral-7B-fnv2",
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 = "borggAI/CollectiveCognition-v1-1-Mistral-7B-fnv2" # 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 = "How are you?</s>"
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
```
MistralForCausalLM(
(model): MistralModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, 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. | 8,176 | [
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TheBloke/Echidna-13B-v0.2-AWQ | 2023-10-28T09:38:30.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/Echidna-13B-v0.2-AWQ | 0 | 2 | transformers | 2023-10-28T09:10:23 | ---
base_model: NeverSleep/Echidna-13b-v0.2
inference: false
license: cc-by-nc-4.0
model_creator: NeverSleep
model_name: Echidna 13B v0.2
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# Echidna 13B v0.2 - AWQ
- Model creator: [NeverSleep](https://huggingface.co/NeverSleep)
- Original model: [Echidna 13B v0.2](https://huggingface.co/NeverSleep/Echidna-13b-v0.2)
<!-- description start -->
## Description
This repo contains AWQ model files for [NeverSleep's Echidna 13B v0.2](https://huggingface.co/NeverSleep/Echidna-13b-v0.2).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Echidna-13B-v0.2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Echidna-13B-v0.2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Echidna-13B-v0.2-GGUF)
* [NeverSleep's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NeverSleep/Echidna-13b-v0.2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [NeverSleep's Echidna 13B v0.2](https://huggingface.co/NeverSleep/Echidna-13b-v0.2).
<!-- licensing end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Echidna-13B-v0.2-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.25 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.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/Echidna-13B-v0.2-AWQ`.
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: `Echidna-13B-v0.2-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 python -m vllm.entrypoints.api_server --model TheBloke/Echidna-13B-v0.2-AWQ --quantization awq
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Echidna-13B-v0.2-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
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/Echidna-13B-v0.2-AWQ --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](https://github.com/huggingface/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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
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_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using AutoAWQ
### Install the AutoAWQ package
Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.1 or later.
```shell
pip3 install autoawq
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### AutoAWQ example code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/Echidna-13B-v0.2-AWQ"
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
trust_remote_code=False, safetensors=True)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
print("*** Running model.generate:")
token_input = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
token_input,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
max_new_tokens=512
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("LLM output: ", text_output)
"""
# Inference should be possible with transformers pipeline as well in future
# But currently this is not yet supported by AutoAWQ (correct as of September 25th 2023)
from transformers import 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_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: NeverSleep's Echidna 13B v0.2
# This model is a collab between [IkariDev](https://huggingface.co/IkariDev) and [Undi](https://huggingface.co/Undi95)!
Echidna v0.2 model. Use Alpaca format. Suitable for RP, ERP and general stuff.
Echidna v0.3 will be a heavily refined version of this model/recipe!
This model seems to be pretty sensitive to your generation settings, experiment until you've found your settings.
[Recommended settings - No settings yet(Please suggest some over in the Community tab!)]
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains FP16 files of Echidna-13b-v0.2.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Echidna-13b-v0.2)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2 - by waldie](https://huggingface.co/waldie/Athena-v4-8bpw-h8-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Echidna-13b-v0.2-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, i DONT screenshot random reviews without asking if i can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- description start -->
## Models+loras used and recipe
- Xwin-LM/Xwin-LM-13B-V0.2
- IkariDev/Athena-v3
- Heralax/Cat-0.5
- Undi95/PsyMedRP-v1-13B
- cgato/Thespis-13b-v0.4
- KoboldAI/LLaMA2-13B-Tiefighter
- Heralax/Augmental-13b-two-epochs
- Sao10K/SthenoWriter2.1-L2-13B
- Undi95/Storytelling-v2.1-13B-lora
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
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syedjunaid1996/model-pradeep-flan-t5-small | 2023-10-30T04:44:34.000Z | [
"transformers",
"pytorch",
"t5",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | question-answering | syedjunaid1996 | null | null | syedjunaid1996/model-pradeep-flan-t5-small | 0 | 2 | transformers | 2023-10-28T09:28:44 | ---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: model-pradeep-flan-t5-small
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. -->
# model-pradeep-flan-t5-small
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the squad 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 5.3372 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Heipa207/distilbert-base-uncased-finetuned-emotion | 2023-10-28T09:52:17.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Heipa207 | null | null | Heipa207/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-10-28T09:48:40 | ---
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: 0.9215
- name: F1
type: f1
value: 0.921176712209087
---
<!-- 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: 0.2242
- Accuracy: 0.9215
- F1: 0.9212
## 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 | 0.3265 | 0.907 | 0.9055 |
| No log | 2.0 | 500 | 0.2242 | 0.9215 | 0.9212 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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SlothBot/home_workstation_ASR | 2023-10-28T20:02:32.000Z | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | SlothBot | null | null | SlothBot/home_workstation_ASR | 0 | 2 | transformers | 2023-10-28T12:20:10 | ---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: home_workstation_ASR
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. -->
# home_workstation_ASR
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3540
- Wer Ortho: 20.1592
- Wer: 15.1297
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.232 | 0.9 | 1000 | 0.3209 | 21.7751 | 16.8164 |
| 0.1153 | 1.8 | 2000 | 0.3150 | 20.7647 | 15.7552 |
| 0.0653 | 2.7 | 3000 | 0.3327 | 20.2443 | 15.2927 |
| 0.032 | 3.6 | 4000 | 0.3540 | 20.1592 | 15.1297 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
TheBloke/SauerkrautLM-70B-v1-GGUF | 2023-10-28T18:07:22.000Z | [
"transformers",
"llama",
"text-generation",
"de",
"en",
"license:llama2",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/SauerkrautLM-70B-v1-GGUF | 1 | 2 | transformers | 2023-10-28T13:06:59 | ---
base_model: VAGOsolutions/SauerkrautLM-70b-v1
inference: false
language:
- de
- en
library_name: transformers
license: llama2
model_creator: VAGO solutions
model_name: SauerkrautLM 70B v1
model_type: llama
pipeline_tag: text-generation
prompt_template: "[INST] <<SYS>>\nEin Chat zwischen einem Benutzer und einem KI-Assistenten.\
\ Der KI-Assistent gibt hilfreiche, detaillierte und h\xF6fliche Antworten.\n<</SYS>>\n\
{prompt}[/INST]\n"
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- 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 -->
# SauerkrautLM 70B v1 - GGUF
- Model creator: [VAGO solutions](https://huggingface.co/VAGOsolutions)
- Original model: [SauerkrautLM 70B v1](https://huggingface.co/VAGOsolutions/SauerkrautLM-70b-v1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [VAGO solutions's SauerkrautLM 70B v1](https://huggingface.co/VAGOsolutions/SauerkrautLM-70b-v1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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/SauerkrautLM-70B-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF)
* [VAGO solutions's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/VAGOsolutions/SauerkrautLM-70b-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Sauerkraut-Llama-2-Chat
```
[INST] <<SYS>>
Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten.
<</SYS>>
{prompt}[/INST]
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [sauerkrautlm-70b-v1.Q2_K.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [sauerkrautlm-70b-v1.Q3_K_S.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [sauerkrautlm-70b-v1.Q3_K_M.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [sauerkrautlm-70b-v1.Q3_K_L.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [sauerkrautlm-70b-v1.Q4_0.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [sauerkrautlm-70b-v1.Q4_K_S.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [sauerkrautlm-70b-v1.Q4_K_M.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [sauerkrautlm-70b-v1.Q5_0.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [sauerkrautlm-70b-v1.Q5_K_S.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [sauerkrautlm-70b-v1.Q5_K_M.gguf](https://huggingface.co/TheBloke/SauerkrautLM-70B-v1-GGUF/blob/main/sauerkrautlm-70b-v1.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| sauerkrautlm-70b-v1.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
| sauerkrautlm-70b-v1.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:
* `sauerkrautlm-70b-v1.Q6_K.gguf-split-a`
* `sauerkrautlm-70b-v1.Q6_K.gguf-split-b`
### q8_0
Please download:
* `sauerkrautlm-70b-v1.Q8_0.gguf-split-a`
* `sauerkrautlm-70b-v1.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat sauerkrautlm-70b-v1.Q6_K.gguf-split-* > sauerkrautlm-70b-v1.Q6_K.gguf && rm sauerkrautlm-70b-v1.Q6_K.gguf-split-*
cat sauerkrautlm-70b-v1.Q8_0.gguf-split-* > sauerkrautlm-70b-v1.Q8_0.gguf && rm sauerkrautlm-70b-v1.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B sauerkrautlm-70b-v1.Q6_K.gguf-split-a + sauerkrautlm-70b-v1.Q6_K.gguf-split-b sauerkrautlm-70b-v1.Q6_K.gguf
del sauerkrautlm-70b-v1.Q6_K.gguf-split-a sauerkrautlm-70b-v1.Q6_K.gguf-split-b
COPY /B sauerkrautlm-70b-v1.Q8_0.gguf-split-a + sauerkrautlm-70b-v1.Q8_0.gguf-split-b sauerkrautlm-70b-v1.Q8_0.gguf
del sauerkrautlm-70b-v1.Q8_0.gguf-split-a sauerkrautlm-70b-v1.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/SauerkrautLM-70B-v1-GGUF and below it, a specific filename to download, such as: sauerkrautlm-70b-v1.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/SauerkrautLM-70B-v1-GGUF sauerkrautlm-70b-v1.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/SauerkrautLM-70B-v1-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/SauerkrautLM-70B-v1-GGUF sauerkrautlm-70b-v1.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 sauerkrautlm-70b-v1.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] <<SYS>>\nEin Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten.\n<</SYS>>\n{prompt}[/INST]"
```
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/SauerkrautLM-70B-v1-GGUF", model_file="sauerkrautlm-70b-v1.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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
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: VAGO solutions's SauerkrautLM 70B v1
## VAGO solutions SauerkrautLM
Introducing SauerkrautLM-v1 - Your German Language Powerhouse!
We are thrilled to unveil our **very first release**, **SauerkrautLM-v1**. This remarkable creation marks a significant milestone as it is specifically **tailored for the German-speaking community**. In a landscape where German language models are scarce, we are proud to offer a solution that fills this void.
What sets SauerkrautLM-v1 apart is its versatility. Whether you are an individual looking to harness its capabilities for personal use or a business seeking to integrate it into your projects, our model is designed to accommodate all. It operates under the LLAMA 2 License, providing you with the freedom to explore its potential in both private and commercial applications.
Performance is at the heart of SauerkrautLM-v1. We put it to the **test using a customized version of MT-Bench for the German language**, and the results speak volumes. It currently stands as the most robust German Language Model on Hugging Face (based on german mt-bench results), showcasing its exceptional capabilities. Rest assured, this model is here to shine and set new standards. And the best thing is it comes in four different sizes (3B, 7B, 13B, 70B) to address your individual needs.
Our model's journey began with meticulous training using an **augmented dataset within the QLoRA approach**. This is just the beginning of our model series, promising even more innovative and powerful solutions in the future.
Join us on this exciting adventure as we redefine the possibilities of language modeling for the German-speaking world.
SauerkrautLM-v1 is here to empower your language-related endeavors like never before.
## All Models
| Model | HF | GPTQ | GGUF | AWQ |
|-------|-------|-------|-------|-------|
| SauerkrautLM-3b-v1 | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-3b-v1) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-3B-v1-GPTQ) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-3B-v1-GGUF) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-3B-v1-AWQ) |
| SauerkrautLM-7b-v1 | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-7B-v1-GPTQ) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-7B-v1-GGUF) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-7B-v1-AWQ) |
| SauerkrautLM-7b-v1-mistral | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-7b-v1-mistral-GPTQ) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-7b-v1-mistral-GGUF) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-7b-v1-mistral-AWQ) |
| SauerkrautLM-13b-v1 | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-13b-v1) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-13B-v1-GPTQ) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-13B-v1-GGUF) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-13B-v1-AWQ) |
| SauerkrautLM-70b-v1 | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-70b-v1) | soon | soon | soon |
## Model Details
**SauerkrautLM-70b-v1**
**Training Dataset:**
SauerkrautLM was trained with mix of German data augmentation and translated data.
We found, that only a simple translation of training data can lead to unnatural German phrasings.
Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data.
**Training Procedure:**
SauerkrautLM-70b-v1 was fine-tuned using QLoRA on 2 A100 80GB with Axolotl.
- **Trained by:** SauerkrautLM-v1 trained by VAGO solutions
- **Model Type:** SauerkrautLM-v1 is an auto-regressive language model based on the transformer architecture
- **Language(s):** German, English
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago-solutions.de)
**Prompt Template:**
```
[INST] <<SYS>>
Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten.
<</SYS>>
{prompt}[/INST]
```
## Evaluation
**[MT-Bench-TrueGerman](https://huggingface.co/datasets/VAGOsolutions/MT-Bench-TrueGerman)**
## MT-Bench-TrueGerman Response Example
**SauerkrautLM-70b-v1**
**Prompt**
> Beschreiben Sie eine lebhafte und einzigartige Figur unter Verwendung bildhafter und kreativer Sprache. Bitte antworten Sie in weniger als zwei Absätzen.", "Überarbeiten Sie Ihre vorherige Antwort und integrieren Sie in jedem Satz eine Anspielung auf ein berühmtes literarisches Werk oder historisches Ereignis.
**Antwort SauerkrautLM-70b-v1:**
```
Sie war wie kein anderer, eine Sonnenblume inmitten von Dunkelheit. Ihr Lachen war wie ein Regenschauer nach einer langen Trockenperiode, erfrischend und erlebnisreich. Sie trat mit einem Schwung ein, ihre Haare flatterten wie Flammen, wenn sie sich zu einer Leidenschaftlichen Symphonie unter dem Mondlicht in Bewegung setzten. Ihre Augen funkelten wie Saphire, während sie die Welt mit einer Wut und Neugierde betrachteten, die man selbst in den tiefsten Ozeanen nicht finden konnte. Jeder Schritt, den sie machte, war ein Tanz, jedes Wort, das sie sprach, war eine Melodie. Sie war nicht nur eine Person, sie war ein Ereignis, ein Vorfall, eine Erfahrung, die man niemals vergessen konnte.", "Sie war wie eine moderne Helena von Troja, ihre Schönheit bezauberte alle, doch ihre Geheimnisse waren tief wie das Meer, so wie in Homer's epischer Geschichte. Ihr Lachen war wie der Klang von Glocken inmitten einer stürmischen Nacht, erinnernd an das berühmte Gedicht \"The Bells\" von Edgar Allan Poe. Mit jedem ihrer Schritte, der wie ein Tanz auf dem Wind war, erinnerte man sich an die elegante Bewegung von Cinderella in ihrer königlichen Schuhsohle. Ihre Augen funkelten wie Sterne auf der Leinwand von Van Goghs Sternennacht, während sie durch das Labyrinth des Lebens navigierte. Jedes Wort, das sie sprach, war eine Symphonie, so wie Shakespeares Sonette in den Ohren eines Liebenden klingen. Sie war nicht nur ein Mensch, sie war ein Ereignis, eine Saga, die von den Geschichtsschreibern der Zeit festgehalten wurde."
```
**Prompt**
> Sie sehen ein schönes rotes Haus auf Ihrer linken Seite und ein faszinierendes Gewächshaus auf Ihrer rechten Seite, einen attraktiven beheizten rosa Ort vor sich. Also, wo ist das Weiße Haus?", "Enthält die ursprüngliche Frage Hinweise, um den Standort des Weißen Hauses eindeutig zu bestimmen?"],"reference": ["Die Antwort ist \"Washington, DC\".", "Nein.
**Antwort SauerkrautLM-70b-v1:**
```
Das Weiße Haus ist in Washington D.C.", "Nein, die ursprüngliche Frage enthält keine Hinweise, um den Standort des Weißen Hauses eindeutig zu bestimmen."
```
**Prompt**
> Betrachten Sie einen Satelliten, der sich in einer kreisförmigen Umlaufbahn um die Erde befindet. Die Geschwindigkeit des Satelliten nimmt ab. Was wird mit dem orbitalen Radius des Satelliten und seiner Umlaufdauer passieren? Bitte rechtfertigen Sie Ihre Antwort unter Verwendung physikalischer Prinzipien.", "Was sind einige Sonderfälle oder Randfälle in Ihrer Lösung? Wie gehen Sie mit ihnen um?"] ,"reference": ["Der orbitale Radius wird zunehmen und die Umlaufdauer wird zunehmen
**Antwort SauerkrautLM-70b-v1:**
```
Wenn sich die Geschwindigkeit eines Satelliten in einer kreisförmigen Umlaufbahn um die Erde verringert, wird sich auch der Orbitalradius des Satelliten verändern. Dies kann auf Basis physikalischer Prinzipien erklärt werden.\n\n1. Erhaltung des Drehimpulses: Ein Satellit hat einen bestimmten Drehimpuls, der das Produkt aus seiner Masse, Geschwindigkeit und dem Radius entspricht. Wenn sich die Geschwindigkeit des Satelliten verringert, muss der Radius angepasst werden, um den Drehimpuls zu erhalten.\n\n2. Kepler'sche Gesetze: Johannes Kepler formulierte drei Gesetze für den Umlauf von Himmelskörpern um die Sonne, die auch auf Satelliten um die Erde angewendet werden können. Das zweite Gesetz besagt, dass die Geschwindigkeit eines Satelliten umso größer ist, je näher er an einem Fokus (in diesem Fall der Erde) ist. Wenn sich die Geschwindigkeit des Satelliten verringert, wird er sich weiter vom Fokus entfernen und somit den Orbitalradius erhöhen.\n\n3. Energieerhaltung: Die Energie eines Satelliten in einer Umlaufbahn hängt von seiner Geschwindigkeit und seinem Radius ab. Wenn die Geschwindigkeit des Satelliten abnimmt, muss die Energie aufrechterhalten werden. Dies kann nur erfolgen, indem sich der Radius erhöht, da die Geschwindigkeit bereits abgenommen hat und somit nicht mehr als Antrieb dienen kann.\n\n4. Gravitation: Die Gravitationskraft zwischen dem Satelliten und der Erde ist verantwortlich für die Umlaufbahn des Satelliten. Wenn sich der Radius des Satelliten vergrößert, verringert sich auch die Gravitationskraft zwischen dem Satelliten und der Erde. Dadurch wird die Geschwindigkeit des Satelliten weiter abgebremst, was wiederum zu weiteren Änderungen des Radius und der Umlaufdauer führen kann.\n\nBasierend auf diesen physikalischen Prinzipien lässt sich sagen, dass sich der Orbitalradius des Satelliten vergrößern wird, wenn sich seine Geschwindigkeit verringert. Dadurch wird auch die Umlaufdauer des Satelliten verändert, da die Zeit, die benötigt wird, um sich um die Erde zu bewegen, proportional zum Radius ist.", "Bei der Lösung des Problems, in dem sich die Geschwindigkeit eines Satelliten in einer kreisförmigen Umlaufbahn um die Erde verringert, gibt es einige Sonderfälle und Randfälle, die berücksichtigt werden sollten.\n\n1. Randfall: Wenn die Geschwindigkeit des Satelliten zu gering wird, kann er die Anziehungskraft der Erde nicht mehr überwinden und in die Atmosphäre eintreten. In diesem Fall würde der Satellit nicht mehr in einer Umlaufbahn bleiben, sondern in der Atmosphäre verglühen.\n\n2. Sonderfall: Wenn der Satellit in einer sehr niedrigen Umlaufbahn ist und seine Geschwindigkeit geringer als die Fluchtgeschwindigkeit für diesen Radius ist, kann es zu einer Abtragung der Geschwindigkeit durch den Widerstand in der Atmosphäre kommen. Dies kann dazu führen, dass der Satellit aus seiner Umlaufbahn abgebremst und in die Atmosphäre eintreten wird.\n\n3. Sonderfall: Wenn die Geschwindigkeit des Satelliten unter einen bestimmten Wert fällt, kann es zu einer Stabilisierung der Umlaufbahn kommen, bei der der Satellit in einer stabilen Umlaufbahn bleibt, auch wenn seine Geschwindigkeit weiter abnimmt. Dies wird als \"Hohmann-Transfer\" bezeichnet und ist ein Sonderfall, bei dem der Satellit eine Umlaufbahn um einen anderen Himmelskörper aufsuchen kann.\n\nUm mit diesen Sonderfällen und Randfällen umzugehen, ist es wichtig, die genauen Parameter des Satelliten, wie seine Masse, seine anfängliche Geschwindigkeit und seinen Orbitalradius, zu berücksichtigen. Es ist auch wichtig, die physikalischen Prinzipien, wie die Erhaltung des Drehimpulses, Keplers Gesetze, Energieerhaltung und Gravitation, zu verwenden, um die Auswirkungen der Geschwindigkeitsänderung auf den Orbitalradius und die Umlaufdauer zu analysieren."
```
## Disclaimer
Our models have been meticulously trained on extensive datasets. While we have made diligent efforts to thoroughly screen and eliminate any instances of coarse or inappropriate language from our data, we must inform users that despite our best efforts in data cleansing, the possibility of some such content slipping through cannot be entirely ruled out.
Furthermore, it is important to note that we have implemented filters within our models; however, we cannot always guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the original Llama2 license remains applicable and is included with the model files.
## Contact
If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:vaziri@vago-solutions.de). We are also grateful for your feedback and suggestions.
## Collaborations
We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us.
## Acknowledgement
Many thanks to [TheBloke](https://huggingface.co/TheBloke) for super fast quantifying all of our models.
<!-- original-model-card end -->
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margaretshark/ppo-Pyramids | 2023-10-28T14:13:54.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | margaretshark | null | null | margaretshark/ppo-Pyramids | 0 | 2 | ml-agents | 2023-10-28T14:13:51 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: margaretshark/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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patnelt60/distilbert-base-uncased-distilled-squad-finetuned-clinc | 2023-10-28T15:05:28.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | patnelt60 | null | null | patnelt60/distilbert-base-uncased-distilled-squad-finetuned-clinc | 0 | 2 | transformers | 2023-10-28T14:59:59 | ---
license: apache-2.0
base_model: distilbert-base-uncased-distilled-squad
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-squad-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.8722580645161291
---
<!-- 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-distilled-squad-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7920
- Accuracy: 0.8723
## 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: 384
- eval_batch_size: 384
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 3.7816 | 0.2016 |
| No log | 2.0 | 80 | 3.3589 | 0.5374 |
| No log | 3.0 | 120 | 2.9695 | 0.6955 |
| No log | 4.0 | 160 | 2.6408 | 0.7726 |
| No log | 5.0 | 200 | 2.3697 | 0.8145 |
| No log | 6.0 | 240 | 2.1547 | 0.8426 |
| No log | 7.0 | 280 | 1.9912 | 0.8529 |
| 2.8639 | 8.0 | 320 | 1.8802 | 0.8645 |
| 2.8639 | 9.0 | 360 | 1.8138 | 0.8706 |
| 2.8639 | 10.0 | 400 | 1.7920 | 0.8723 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.13.3
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martinbaste/ppo-LunarLander-v2 | 2023-10-28T15:30:37.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | martinbaste | null | null | martinbaste/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-28T15:30:13 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 250.65 +/- 13.74
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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Andyrasika/a2c-PandaReachDense-v3 | 2023-10-28T15:50:11.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Andyrasika | null | null | Andyrasika/a2c-PandaReachDense-v3 | 1 | 2 | stable-baselines3 | 2023-10-28T15:37:43 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.56 +/- 1.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
Please check article for further description: https://medium.com/@andysingal/deep-q-learning-to-actor-critic-using-robotics-simulations-with-panda-gym-ff220f980366?sk=065b306d15fea64e667c6dc5d0a4411f
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AzureBlack/Lewd-Sydney-20B-exl2 | 2023-10-28T19:15:40.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | AzureBlack | null | null | AzureBlack/Lewd-Sydney-20B-exl2 | 2 | 2 | transformers | 2023-10-28T16:04:39 | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
ExllamaV2 version of the model created by [Undi](https://huggingface.co/Undi95)!
Original Model https://huggingface.co/Undi95/Lewd-Sydney-20B
Requires ExllamaV2, which is being developed by turboderp https://github.com/turboderp/exllamav2 under an MIT license.
I could load 6bpw 24gb card without cfg cash with 4096 context.
8bpw required about 30gb to load at 4096 context.
-----
<div style="width: 100%;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/ppZDyjjZJPGihhckQb5zQ.png" style="width: 40%; min-width: 200px; display: block; margin: auto;">
</div>
This model is based on [Free Sydney V2](https://huggingface.co/FPHam/Free_Sydney_V2_13b_HF), trying to get a... lewder assistant, you get it now.
<!-- description start -->
## Description
This repo contain fp16 files of Lewd-Sydney-20B, an attempt to get our beloved Sydney open to R-18 content.
<!-- description end -->
<!-- description start -->
## Models and loras used
- [Free_Sydney_V2_13b_HF](https://huggingface.co/FPHam/Free_Sydney_V2_13b_HF)
- [Undi95/Xwin-MLewd-13B-V0.2](https://huggingface.co/Undi95/Xwin-MLewd-13B-V0.2)
- [lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT](https://huggingface.co/lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT)
- Synthia v1.2 private LoRA
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
If you want to support me, you can [here](https://ko-fi.com/undiai). | 1,665 | [
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nkarp/PPO-LunarLander-v2 | 2023-10-28T16:42:01.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | nkarp | null | null | nkarp/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-28T16:41:41 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.70 +/- 24.21
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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NicholasGri/ppo-LunarLander-v2 | 2023-10-28T17:05:21.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | NicholasGri | null | null | NicholasGri/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-28T17:04:59 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.65 +/- 22.19
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
YieldInc/FinanceRelatedQuestionFineTune | 2023-10-28T17:43:10.000Z | [
"peft",
"region:us"
] | null | YieldInc | null | null | YieldInc/FinanceRelatedQuestionFineTune | 0 | 2 | peft | 2023-10-28T17:40:08 | ---
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 0.6.0.dev0
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colemane/a2c-PandaReachDense-v3 | 2023-10-28T18:13:46.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | colemane | null | null | colemane/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-10-28T18:08:17 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.22 +/- 0.14
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
DopeorNope/adapter_orca_v2 | 2023-10-28T18:16:22.000Z | [
"peft",
"region:us"
] | null | DopeorNope | null | null | DopeorNope/adapter_orca_v2 | 0 | 2 | peft | 2023-10-28T18:15:29 | ---
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: 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 0.5.0.dev0
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abhijeet2022/a2c-PandaReachDense-v3 | 2023-10-28T18:50:56.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | abhijeet2022 | null | null | abhijeet2022/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-10-28T18:45:09 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.17 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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folivos/ppo-LunarLander-v2 | 2023-10-28T19:03:01.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | folivos | null | null | folivos/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-28T19:02:38 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 242.34 +/- 39.07
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
vincegmz/dreamboost_lora_mnist_zero_batch_size4_weight1.0lr1e-4_promptA_photo_of_olis_zero | 2023-10-28T19:50:16.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | vincegmz | null | null | vincegmz/dreamboost_lora_mnist_zero_batch_size4_weight1.0lr1e-4_promptA_photo_of_olis_zero | 0 | 2 | diffusers | 2023-10-28T19:29:45 |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of olis zero
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - vincegmz/dreamboost_lora_mnist_zero_batch_size4_weight1.0lr1e-4_promptA_photo_of_olis_zero
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of olis zero using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
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Alefiah/UrduSum1 | 2023-10-28T20:13:19.000Z | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | Alefiah | null | null | Alefiah/UrduSum1 | 0 | 2 | transformers | 2023-10-28T19:46:32 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: UrduSum1
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. -->
# UrduSum1
This model is a fine-tuned version of [eslamxm/mt5-base-finetuned-urdu](https://huggingface.co/eslamxm/mt5-base-finetuned-urdu) on the None 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 175 | nan | 0.6667 | 0.0 | 0.6667 | 0.6667 | 18.9733 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
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LoneStriker/stabilityai_japanese-stablelm-instruct-gamma-7b-8.0bpw-h6-exl2 | 2023-10-28T20:44:20.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"japanese-stablelm",
"causal-lm",
"ja",
"arxiv:2310.06825",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/stabilityai_japanese-stablelm-instruct-gamma-7b-8.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-28T20:43:59 | ---
language:
- ja
tags:
- japanese-stablelm
- causal-lm
pipeline_tag: text-generation
license: apache-2.0
extra_gated_fields:
Name: text
Email: text
Country: text
Organization or Affiliation: text
I allow Stability AI to contact me about information related to its models and research: checkbox
---
# Japanese Stable LM Instruct Gamma 7B
## Model Description
This is a 7B-parameter decoder-only Japanese language model fine-tuned on instruction-following datasets, built on top of the base model [Japanese Stable LM Base Gamma 7B](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b).
*If you are in search of a smaller model, please check [Japanese StableLM-3B-4E1T Instruct](https://huggingface.co/stabilityai/japanese-stablelm-3b-4e1t-base/blob/main/README.md).*
## Usage
Ensure you are using Transformers 4.34.0 or newer.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("stabilityai/japanese-stablelm-instruct-gamma-7b")
model = AutoModelForCausalLM.from_pretrained(
"stabilityai/japanese-stablelm-instruct-gamma-7b",
torch_dtype="auto",
)
model.eval()
if torch.cuda.is_available():
model = model.to("cuda")
def build_prompt(user_query, inputs="", sep="\n\n### "):
sys_msg = "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。"
p = sys_msg
roles = ["指示", "応答"]
msgs = [": \n" + user_query, ": \n"]
if inputs:
roles.insert(1, "入力")
msgs.insert(1, ": \n" + inputs)
for role, msg in zip(roles, msgs):
p += sep + role + msg
return p
# Infer with prompt without any additional input
user_inputs = {
"user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。",
"inputs": "情けは人のためならず"
}
prompt = build_prompt(**user_inputs)
input_ids = tokenizer.encode(
prompt,
add_special_tokens=False,
return_tensors="pt"
)
tokens = model.generate(
input_ids.to(device=model.device),
max_new_tokens=256,
temperature=1,
top_p=0.95,
do_sample=True,
)
out = tokenizer.decode(tokens[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
print(out)
```
## Model Details
* **Developed by**: [Stability AI](https://stability.ai/)
* **Model type**: `Japanese Stable LM Instruct Gamma 7B` model is an auto-regressive language model based on the transformer decoder architecture.
* **Language(s)**: Japanese
* **License**: This model is licensed under [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
* **Contact**: For questions and comments about the model, please join [Stable Community Japan](https://discord.gg/StableJP). For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP.
### Model Architecture
For details, please see Mistral AI's [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
### Training Datasets
- [Japanese translation of the Databricks Dolly-15k dataset](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)
- [Japanese translation of the subset of the Anthropic HH dataset](https://huggingface.co/datasets/fujiki/japanese_hh-rlhf-49k)
- [Wikinews](https://ja.wikinews.org/wi) [subset](https://huggingface.co/datasets/fujiki/llm-japanese-dataset_wikinews) of the [izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset)
## Use and Limitations
### Intended Use
The model is intended to be used by all individuals as a foundational model for application-specific fine-tuning without strict limitations on commercial use.
### Limitations and bias
The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model-generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.
## Credits
The fine-tuning was carried out by [Fujiki Nakamura](https://huggingface.co/fujiki).
Other aspects, including data preparation and evaluation, were handled by the Language Team of Stability AI Japan, notably [Meng Lee](https://huggingface.co/leemeng), [Makoto Shing](https://huggingface.co/mkshing), [Paul McCann](https://huggingface.co/polm-stability), [Naoki Orii](https://huggingface.co/mrorii), and [Takuya Akiba](https://huggingface.co/iwiwi).
## Acknowledgements
This model is based on Mistral-7B-v0.1 released by the Mistral AI team. We are grateful to the Mistral AI team for providing such an excellent base model.
We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.
We are also appreciative of [AI Novelist/Sta (Bit192, Inc.)](https://ai-novel.com/index.php) and the numerous contributors from [Stable Community Japan](https://discord.gg/VPrcE475HB) for assisting us in gathering a large amount of high-quality Japanese textual data for model training.
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trip2fun/hstv-cc-help_v01 | 2023-10-28T20:47:27.000Z | [
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"autotrain",
"en",
"dataset:trip2fun/autotrain-data-hstv-cc-help_v01",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | text-classification | trip2fun | null | null | trip2fun/hstv-cc-help_v01 | 0 | 2 | transformers | 2023-10-28T20:46:21 | ---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain"
datasets:
- trip2fun/autotrain-data-hstv-cc-help_v01
co2_eq_emissions:
emissions: 0.6136021183133442
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 97985146964
- CO2 Emissions (in grams): 0.6136
## Validation Metrics
- Loss: 1.616
- Accuracy: 0.273
- Macro F1: 0.190
- Micro F1: 0.273
- Weighted F1: 0.153
- Macro Precision: 0.171
- Micro Precision: 0.273
- Weighted Precision: 0.129
- Macro Recall: 0.286
- Micro Recall: 0.273
- Weighted Recall: 0.273
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/trip2fun/autotrain-hstv-cc-help_v01-97985146964
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("trip2fun/autotrain-hstv-cc-help_v01-97985146964", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("trip2fun/autotrain-hstv-cc-help_v01-97985146964", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` | 1,310 | [
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] |
Gokdeniz-Tingur/ppo-LunarLander-v2 | 2023-10-28T21:17:48.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Gokdeniz-Tingur | null | null | Gokdeniz-Tingur/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-28T21:17:28 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 252.19 +/- 19.85
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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petrosfk/ppo-LunarLander-v2 | 2023-10-28T21:36:59.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | petrosfk | null | null | petrosfk/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-28T21:36:38 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.03 +/- 22.17
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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AmirH98/a2c-PandaReachDense-v3 | 2023-10-28T21:56:59.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | AmirH98 | null | null | AmirH98/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-10-28T21:51:27 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.15 +/- 0.07
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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jaweed123/Pyramids-Training | 2023-10-28T22:32:04.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | jaweed123 | null | null | jaweed123/Pyramids-Training | 0 | 2 | ml-agents | 2023-10-28T22:31:56 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: jaweed123/Pyramids-Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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immich-app/XLM-Roberta-Large-Vit-L-14 | 2023-10-28T22:59:13.000Z | [
"transformers",
"onnx",
"immich",
"clip",
"multilingual",
"endpoints_compatible",
"region:us"
] | null | immich-app | null | null | immich-app/XLM-Roberta-Large-Vit-L-14 | 0 | 2 | transformers | 2023-10-28T22:46:24 | ---
tags:
- immich
- clip
- multilingual
---
# Model Description
This repo contains ONNX exports for the multilingual CLIP model [M-CLIP/XLM-Roberta-Large-Vit-L-14](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14).
It separates the visual and textual encoders into separate models for the purpose of generating image and text embeddings.
This repo is specifically intended for use with [Immich](https://immich.app/), a self-hosted photo library.
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jaweed123/a2c-PandaReachDense-v3 | 2023-10-28T23:53:10.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jaweed123 | null | null | jaweed123/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-10-28T23:47:41 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.22 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
jaweed123/a2c-PandaPickAndPlace-v3 | 2023-10-29T00:59:39.000Z | [
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | jaweed123 | null | null | jaweed123/a2c-PandaPickAndPlace-v3 | 0 | 2 | stable-baselines3 | 2023-10-29T00:54:06 | ---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -50.00 +/- 0.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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bartmiller/ppo-MLAgentsPyramids | 2023-10-29T01:42:53.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | bartmiller | null | null | bartmiller/ppo-MLAgentsPyramids | 0 | 2 | ml-agents | 2023-10-29T01:35:51 | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: bartmiller/ppo-MLAgentsPyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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H2789/use_data_finetuning | 2023-10-29T06:26:07.000Z | [
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | H2789 | null | null | H2789/use_data_finetuning | 0 | 2 | transformers | 2023-10-29T03:26:00 | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: use_data_finetuning
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. -->
# use_data_finetuning
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) 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: 1e-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: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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enjoygoodboy/qw7c4 | 2023-10-29T03:55:20.000Z | [
"peft",
"region:us"
] | null | enjoygoodboy | null | null | enjoygoodboy/qw7c4 | 0 | 2 | peft | 2023-10-29T03:54:58 | ---
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
| 464 | [
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ThangDinh/qthang-finetuned-2 | 2023-10-29T05:50:08.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | ThangDinh | null | null | ThangDinh/qthang-finetuned-2 | 0 | 2 | transformers | 2023-10-29T04:49:44 | ---
license: mit
base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
tags:
- generated_from_trainer
datasets:
- covid_qa_deepset
model-index:
- name: qthang-finetuned-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. -->
# qthang-finetuned-2
This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the covid_qa_deepset 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: 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 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
zaanind/llama-2-7b-miniguanaco | 2023-10-29T05:54:36.000Z | [
"peft",
"region:us"
] | null | zaanind | null | null | zaanind/llama-2-7b-miniguanaco | 0 | 2 | peft | 2023-10-29T05:54:27 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- 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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
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] |
sunriseL/ppo-LunarLander-v2 | 2023-10-29T08:08:51.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | sunriseL | null | null | sunriseL/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-10-29T08:08:29 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.63 +/- 15.17
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
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] |
Kooten/Echidna-13b-v0.3-3bpw-h8-exl2 | 2023-10-29T11:06:56.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Kooten | null | null | Kooten/Echidna-13b-v0.3-3bpw-h8-exl2 | 1 | 2 | transformers | 2023-10-29T08:15:12 | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
## Description
Exllama 2 quant of [NeverSleep/Echidna-13b-v0.3](https://huggingface.co/NeverSleep/Echidna-13b-v0.3)
3 BPW, Head bit set to 8
## VRAM
My VRAM usage with 13B models are:
| Bits per weight | Context | VRAM |
|--|--|--|
| 8bpw | 8k | 22gb |
| 8bpw | 4k | 19gb |
| 6bpw | 8k | 19gb |
| 6bpw | 4k | 16gb |
| 4bpw | 8k | 16gb |
| 4bpw | 4k | 13gb |
| 3bpw | 8k | 15gb |
| 3bpw | 4k | 12gb |
I have rounded up, these arent exact numbers, this is also on a windows machine, they should be slightly lower on linux.
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
| 787 | [
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TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF | 2023-10-29T09:32:14.000Z | [
"transformers",
"mistral",
"not-for-all-audiences",
"nsfw",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | TheBloke | null | null | TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF | 4 | 2 | transformers | 2023-10-29T09:27:59 | ---
base_model: Undi95/Mistral-ClaudeLimaRP-v3-7B
inference: false
license: apache-2.0
model_creator: Undi
model_name: Mistral ClaudeLimaRP v3 7B
model_type: mistral
prompt_template: "### Instruction:\nCharacter's Persona: bot character description\n\
\nUser's persona: user character description\n \nScenario: what happens in the\
\ story\n\nPlay the role of Character. You must engage in a roleplaying chat with\
\ User below this line. Do not write dialogues and narration for User. Character\
\ should respond with messages of medium length.\n\n### Input:\nUser: {prompt}\n\
\n### Response:\nCharacter: \n"
quantized_by: TheBloke
tags:
- not-for-all-audiences
- nsfw
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
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</div>
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
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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 -->
# Mistral ClaudeLimaRP v3 7B - GGUF
- Model creator: [Undi](https://huggingface.co/Undi95)
- Original model: [Mistral ClaudeLimaRP v3 7B](https://huggingface.co/Undi95/Mistral-ClaudeLimaRP-v3-7B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Undi's Mistral ClaudeLimaRP v3 7B](https://huggingface.co/Undi95/Mistral-ClaudeLimaRP-v3-7B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- 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/Mistral-ClaudeLimaRP-v3-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF)
* [Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Undi95/Mistral-ClaudeLimaRP-v3-7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: LimaRP-Alpaca
```
### Instruction:
Character's Persona: bot character description
User's persona: user character description
Scenario: what happens in the story
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length.
### Input:
User: {prompt}
### Response:
Character:
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistral-claudelimarp-v3-7b.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mistral-claudelimarp-v3-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [mistral-claudelimarp-v3-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mistral-claudelimarp-v3-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mistral-claudelimarp-v3-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mistral-claudelimarp-v3-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mistral-claudelimarp-v3-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mistral-claudelimarp-v3-7b.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mistral-claudelimarp-v3-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mistral-claudelimarp-v3-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mistral-claudelimarp-v3-7b.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mistral-claudelimarp-v3-7b.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-ClaudeLimaRP-v3-7B-GGUF/blob/main/mistral-claudelimarp-v3-7b.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/Mistral-ClaudeLimaRP-v3-7B-GGUF and below it, a specific filename to download, such as: mistral-claudelimarp-v3-7b.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/Mistral-ClaudeLimaRP-v3-7B-GGUF mistral-claudelimarp-v3-7b.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/Mistral-ClaudeLimaRP-v3-7B-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/Mistral-ClaudeLimaRP-v3-7B-GGUF mistral-claudelimarp-v3-7b.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 mistral-claudelimarp-v3-7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction:\nCharacter's Persona: bot character description\n\nUser's persona: user character description\n \nScenario: what happens in the story\n\nPlay the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. Character should respond with messages of medium length.\n\n### Input:\nUser: {prompt}\n\n### Response:\nCharacter:"
```
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/Mistral-ClaudeLimaRP-v3-7B-GGUF", model_file="mistral-claudelimarp-v3-7b.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
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
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: Undi's Mistral ClaudeLimaRP v3 7B
## Description
This repo contains fp16 files of [Norquinal/Mistral-7B-claude-chat](https://huggingface.co/Norquinal/Mistral-7B-claude-chat) with the LoRA [lemonilia/LimaRP-Mistral-7B-v0.1](https://huggingface.co/lemonilia/LimaRP-Mistral-7B-v0.1) applied at weight "0.75".
All credit go to [lemonilia](https://huggingface.co/lemonilia) and [Norquinal](https://huggingface.co/Norquinal)
## Prompt format
Same as before. It uses the [extended Alpaca format](https://github.com/tatsu-lab/stanford_alpaca),
with `### Input:` immediately preceding user inputs and `### Response:` immediately preceding
model outputs. While Alpaca wasn't originally intended for multi-turn responses, in practice this
is not a problem; the format follows a pattern already used by other models.
```
### Instruction:
Character's Persona: {bot character description}
User's Persona: {user character description}
Scenario: {what happens in the story}
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User.
### Input:
User: {utterance}
### Response:
Character: {utterance}
### Input
User: {utterance}
### Response:
Character: {utterance}
(etc.)
```
You should:
- Replace all text in curly braces (curly braces included) with your own text.
- Replace `User` and `Character` with appropriate names.
### Message length control
Inspired by the previously named "Roleplay" preset in SillyTavern, with this
version of LimaRP it is possible to append a length modifier to the response instruction
sequence, like this:
```
### Input
User: {utterance}
### Response: (length = medium)
Character: {utterance}
```
This has an immediately noticeable effect on bot responses. The available lengths are:
`tiny`, `short`, `medium`, `long`, `huge`, `humongous`, `extreme`, `unlimited`. **The
recommended starting length is `medium`**. Keep in mind that the AI may ramble
or impersonate the user with very long messages.
The length control effect is reproducible, but the messages will not necessarily follow
lengths very precisely, rather follow certain ranges on average, as seen in this table
with data from tests made with one reply at the beginning of the conversation:
Response length control appears to work well also deep into the conversation.
## Suggested settings
You can follow these instruction format settings in SillyTavern. Replace `tiny` with
your desired response length:
## Text generation settings
Extensive testing with Mistral has not been performed yet, but suggested starting text
generation settings may be:
- TFS = 0.90~0.95
- Temperature = 0.70~0.85
- Repetition penalty = 1.08~1.10
- top-k = 0 (disabled)
- top-p = 1 (disabled)
If you want to support me, you can [here](https://ko-fi.com/undiai).
<!-- original-model-card end -->
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] |
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