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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
lucas-meyer/xls-r-asr_xh-run2 | 2023-11-05T13:07:52.000Z | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | lucas-meyer | null | null | lucas-meyer/xls-r-asr_xh-run2 | 0 | 2 | transformers | 2023-11-05T11:28:12 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: xls-r-asr_xh-run2
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. -->
# xls-r-asr_xh-run2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4481
- Wer: 0.6071
## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 10.0196 | 0.48 | 100 | 4.1968 | 1.0 |
| 3.4458 | 0.96 | 200 | 3.0858 | 1.0 |
| 3.0463 | 1.44 | 300 | 2.9014 | 1.0 |
| 2.0011 | 1.91 | 400 | 1.0665 | 0.9236 |
| 0.9422 | 2.39 | 500 | 0.7259 | 0.8222 |
| 0.743 | 2.87 | 600 | 0.6380 | 0.8027 |
| 0.605 | 3.35 | 700 | 0.5172 | 0.6544 |
| 0.5305 | 3.83 | 800 | 0.4808 | 0.6414 |
| 0.4364 | 4.31 | 900 | 0.4421 | 0.6048 |
| 0.4065 | 4.78 | 1000 | 0.4499 | 0.6291 |
| 0.3555 | 5.26 | 1100 | 0.4481 | 0.6071 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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LaTarn/ta-location-setfit-model | 2023-11-05T11:51:17.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ta-location-setfit-model | 0 | 2 | sentence-transformers | 2023-11-05T11:50:53 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ta-location-setfit-model
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("LaTarn/ta-location-setfit-model")
# 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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chaithanya1234/trail_1 | 2023-11-05T11:56:40.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | chaithanya1234 | null | null | chaithanya1234/trail_1 | 0 | 2 | stable-baselines3 | 2023-11-05T11:56:18 | ---
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: 237.01 +/- 48.93
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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satyanshu404/bart-large-cnn-prompt_generation-2.0 | 2023-11-05T18:17:45.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | satyanshu404 | null | null | satyanshu404/bart-large-cnn-prompt_generation-2.0 | 0 | 2 | transformers | 2023-11-05T12:34:24 | ---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart-large-cnn-prompt_generation-2.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-prompt_generation-2.0
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6403
- Actual score: 0.8766
- Predction score: 0.5039
- Score difference: 0.3727
## 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-07
- 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: 75
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Actual score | Predction score | Score difference |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:---------------:|:----------------:|
| No log | 1.0 | 8 | 3.6549 | 0.8766 | -0.2093 | 1.0859 |
| No log | 2.0 | 16 | 3.6012 | 0.8766 | -0.1961 | 1.0728 |
| No log | 3.0 | 24 | 3.5331 | 0.8766 | -0.1613 | 1.0379 |
| No log | 4.0 | 32 | 3.4417 | 0.8766 | -0.1132 | 0.9899 |
| No log | 5.0 | 40 | 3.3501 | 0.8766 | -0.1821 | 1.0587 |
| No log | 6.0 | 48 | 3.2904 | 0.8766 | -0.1653 | 1.0419 |
| No log | 7.0 | 56 | 3.2418 | 0.8766 | -0.4566 | 1.3332 |
| No log | 8.0 | 64 | 3.1620 | 0.8766 | -0.2897 | 1.1663 |
| No log | 9.0 | 72 | 3.0925 | 0.8766 | -0.5185 | 1.3951 |
| No log | 10.0 | 80 | 3.0442 | 0.8766 | -0.7127 | 1.5893 |
| No log | 11.0 | 88 | 3.0064 | 0.8766 | -0.4893 | 1.3659 |
| No log | 12.0 | 96 | 2.9742 | 0.8766 | -0.6391 | 1.5157 |
| No log | 13.0 | 104 | 2.9475 | 0.8766 | -0.4873 | 1.3640 |
| No log | 14.0 | 112 | 2.9254 | 0.8766 | -0.2786 | 1.1552 |
| No log | 15.0 | 120 | 2.9061 | 0.8766 | -0.1893 | 1.0660 |
| No log | 16.0 | 128 | 2.8887 | 0.8766 | -0.2202 | 1.0968 |
| No log | 17.0 | 136 | 2.8730 | 0.8766 | -0.2009 | 1.0775 |
| No log | 18.0 | 144 | 2.8588 | 0.8766 | -0.2101 | 1.0867 |
| No log | 19.0 | 152 | 2.8461 | 0.8766 | -0.3374 | 1.2140 |
| No log | 20.0 | 160 | 2.8337 | 0.8766 | -0.2005 | 1.0772 |
| No log | 21.0 | 168 | 2.8216 | 0.8766 | -0.2570 | 1.1336 |
| No log | 22.0 | 176 | 2.8104 | 0.8766 | -0.3601 | 1.2367 |
| No log | 23.0 | 184 | 2.7996 | 0.8766 | -0.4823 | 1.3589 |
| No log | 24.0 | 192 | 2.7895 | 0.8766 | -0.4451 | 1.3217 |
| No log | 25.0 | 200 | 2.7798 | 0.8766 | -0.3621 | 1.2388 |
| No log | 26.0 | 208 | 2.7706 | 0.8766 | -0.4108 | 1.2874 |
| No log | 27.0 | 216 | 2.7625 | 0.8766 | -0.4750 | 1.3517 |
| No log | 28.0 | 224 | 2.7547 | 0.8766 | -0.4004 | 1.2771 |
| No log | 29.0 | 232 | 2.7471 | 0.8766 | -0.4535 | 1.3301 |
| No log | 30.0 | 240 | 2.7393 | 0.8766 | -0.5414 | 1.4180 |
| No log | 31.0 | 248 | 2.7328 | 0.8766 | -0.5666 | 1.4433 |
| No log | 32.0 | 256 | 2.7268 | 0.8766 | -0.6630 | 1.5396 |
| No log | 33.0 | 264 | 2.7211 | 0.8766 | -0.4073 | 1.2839 |
| No log | 34.0 | 272 | 2.7160 | 0.8766 | -0.5464 | 1.4230 |
| No log | 35.0 | 280 | 2.7113 | 0.8766 | -0.3629 | 1.2396 |
| No log | 36.0 | 288 | 2.7065 | 0.8766 | -0.2926 | 1.1692 |
| No log | 37.0 | 296 | 2.7025 | 0.8766 | -0.2596 | 1.1362 |
| No log | 38.0 | 304 | 2.6981 | 0.8766 | -0.1478 | 1.0244 |
| No log | 39.0 | 312 | 2.6939 | 0.8766 | -0.2252 | 1.1018 |
| No log | 40.0 | 320 | 2.6901 | 0.8766 | -0.2750 | 1.1516 |
| No log | 41.0 | 328 | 2.6867 | 0.8766 | -0.0900 | 0.9667 |
| No log | 42.0 | 336 | 2.6836 | 0.8766 | -0.2377 | 1.1144 |
| No log | 43.0 | 344 | 2.6804 | 0.8766 | -0.3135 | 1.1901 |
| No log | 44.0 | 352 | 2.6774 | 0.8766 | -0.1023 | 0.9789 |
| No log | 45.0 | 360 | 2.6745 | 0.8766 | -0.0386 | 0.9152 |
| No log | 46.0 | 368 | 2.6714 | 0.8766 | 0.1602 | 0.7164 |
| No log | 47.0 | 376 | 2.6689 | 0.8766 | 0.2508 | 0.6258 |
| No log | 48.0 | 384 | 2.6668 | 0.8766 | 0.1577 | 0.7190 |
| No log | 49.0 | 392 | 2.6648 | 0.8766 | 0.0565 | 0.8201 |
| No log | 50.0 | 400 | 2.6627 | 0.8766 | 0.2379 | 0.6387 |
| No log | 51.0 | 408 | 2.6607 | 0.8766 | 0.2343 | 0.6423 |
| No log | 52.0 | 416 | 2.6588 | 0.8766 | 0.2719 | 0.6048 |
| No log | 53.0 | 424 | 2.6570 | 0.8766 | 0.2214 | 0.6552 |
| No log | 54.0 | 432 | 2.6555 | 0.8766 | 0.2729 | 0.6037 |
| No log | 55.0 | 440 | 2.6541 | 0.8766 | 0.2798 | 0.5968 |
| No log | 56.0 | 448 | 2.6528 | 0.8766 | 0.0662 | 0.8104 |
| No log | 57.0 | 456 | 2.6514 | 0.8766 | 0.0377 | 0.8390 |
| No log | 58.0 | 464 | 2.6502 | 0.8766 | 0.2886 | 0.5880 |
| No log | 59.0 | 472 | 2.6491 | 0.8766 | 0.2257 | 0.6509 |
| No log | 60.0 | 480 | 2.6481 | 0.8766 | 0.2561 | 0.6206 |
| No log | 61.0 | 488 | 2.6471 | 0.8766 | 0.2683 | 0.6083 |
| No log | 62.0 | 496 | 2.6461 | 0.8766 | 0.2897 | 0.5869 |
| 2.5848 | 63.0 | 504 | 2.6453 | 0.8766 | 0.2974 | 0.5793 |
| 2.5848 | 64.0 | 512 | 2.6445 | 0.8766 | 0.2946 | 0.5820 |
| 2.5848 | 65.0 | 520 | 2.6438 | 0.8766 | 0.3021 | 0.5745 |
| 2.5848 | 66.0 | 528 | 2.6433 | 0.8766 | 0.2679 | 0.6087 |
| 2.5848 | 67.0 | 536 | 2.6428 | 0.8766 | 0.3133 | 0.5633 |
| 2.5848 | 68.0 | 544 | 2.6423 | 0.8766 | 0.3398 | 0.5368 |
| 2.5848 | 69.0 | 552 | 2.6418 | 0.8766 | 0.4149 | 0.4617 |
| 2.5848 | 70.0 | 560 | 2.6413 | 0.8766 | 0.4674 | 0.4092 |
| 2.5848 | 71.0 | 568 | 2.6410 | 0.8766 | 0.4929 | 0.3838 |
| 2.5848 | 72.0 | 576 | 2.6407 | 0.8766 | 0.4974 | 0.3793 |
| 2.5848 | 73.0 | 584 | 2.6406 | 0.8766 | 0.4948 | 0.3818 |
| 2.5848 | 74.0 | 592 | 2.6404 | 0.8766 | 0.4623 | 0.4143 |
| 2.5848 | 75.0 | 600 | 2.6403 | 0.8766 | 0.5039 | 0.3727 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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carles-undergrad-thesis/indobert-KD | 2023-11-06T01:40:45.000Z | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | carles-undergrad-thesis | null | null | carles-undergrad-thesis/indobert-KD | 0 | 2 | sentence-transformers | 2023-11-05T12:39:49 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
This model utilizes a newer version of Sentence Transformers. If you're having trouble using this model, please try installing the latest version of Sentence Transformers with:
```bash
pip install --upgrade --force-reinstall --no-deps git+https://github.com/UKPLab/sentence-transformers.git
```
# carles-undergrad-thesis/indobert-KD
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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('carles-undergrad-thesis/indobert-KD')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('carles-undergrad-thesis/indobert-KD')
model = AutoModel.from_pretrained('carles-undergrad-thesis/indobert-KD')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
### ID EVAL
| Model | Mmarco Dev | | MrTyDi Test | | Miracal Test | |
|-----------------------------------------|------------|----------------|-------------|----------------|--------------|----------------------------|
| | MRR@10 | R@1000 | MRR@10 | R@1000 | NCDG@10 | R@1K |
| $\text{BM25 (Elastic Search)}$ | .114 | .642 | .279 | .858 | .391 | .971 |
| $\text{IndoBERT}_{\text{KD}}$ | .176 | .803 | .300 | .761 | .179 | .072 |
### EN EVAL
| Model | msarco Dev | |
|-----------------------------------------|------------|----------------|
| | MRR@10 | R@1000 |
| $\text{BM25 (Elastic Search)}$ | .184 | .857 |
| $\text{IndoBERT}_{\text{KD}}$ | .245 | .912 |
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 4,070 | [
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] |
TheBloke/deepseek-coder-6.7B-base-GGUF | 2023-11-05T15:28:45.000Z | [
"transformers",
"deepseek",
"license:other",
"region:us"
] | null | TheBloke | null | null | TheBloke/deepseek-coder-6.7B-base-GGUF | 1 | 2 | transformers | 2023-11-05T13:30:44 | ---
base_model: deepseek-ai/deepseek-coder-6.7b-base
inference: false
license: other
license_link: LICENSE
license_name: deepseek-license
model_creator: DeepSeek
model_name: Deepseek Coder 6.7B Base
model_type: deepseek
prompt_template: '{prompt}
'
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>
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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 -->
# Deepseek Coder 6.7B Base - GGUF
- Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
- Original model: [Deepseek Coder 6.7B Base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base)
<!-- description start -->
## Description
This repo contains GGUF format model files for [DeepSeek's Deepseek Coder 6.7B Base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base).
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 incomplete 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/deepseek-coder-6.7B-base-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF)
* [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [deepseek-coder-6.7b-base.Q2_K.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
| [deepseek-coder-6.7b-base.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
| [deepseek-coder-6.7b-base.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
| [deepseek-coder-6.7b-base.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
| [deepseek-coder-6.7b-base.Q4_0.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [deepseek-coder-6.7b-base.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
| [deepseek-coder-6.7b-base.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
| [deepseek-coder-6.7b-base.Q5_0.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [deepseek-coder-6.7b-base.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
| [deepseek-coder-6.7b-base.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q5_K_M.gguf) | Q5_K_M | 5 | 4.79 GB| 7.29 GB | large, very low quality loss - recommended |
| [deepseek-coder-6.7b-base.Q6_K.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
| [deepseek-coder-6.7b-base.Q8_0.gguf](https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGUF/blob/main/deepseek-coder-6.7b-base.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 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/deepseek-coder-6.7B-base-GGUF and below it, a specific filename to download, such as: deepseek-coder-6.7b-base.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/deepseek-coder-6.7B-base-GGUF deepseek-coder-6.7b-base.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/deepseek-coder-6.7B-base-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/deepseek-coder-6.7B-base-GGUF deepseek-coder-6.7b-base.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 deepseek-coder-6.7b-base.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/deepseek-coder-6.7B-base-GGUF", model_file="deepseek-coder-6.7b-base.Q4_K_M.gguf", model_type="deepseek", 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: DeepSeek's Deepseek Coder 6.7B Base
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek Coder
Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
- **Massive Training Data**: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
- **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.
- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
### 2. Model Summary
deepseek-coder-6.7b-base is a 6.7B parameter model with Multi-Head Attention trained on 2 trillion tokens.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### 1）Code Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True).cuda()
input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").cuda()
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
#### 2）Code Insertion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True).cuda()
input_text = """<｜fim▁begin｜>def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
left = []
right = []
<｜fim▁hole｜>
if arr[i] < pivot:
left.append(arr[i])
else:
right.append(arr[i])
return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
inputs = tokenizer(input_text, return_tensors="pt").cuda()
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
```
#### 3）Repository Level Code Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True).cuda()
input_text = """#utils.py
import torch
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
def load_data():
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Standardize the data
scaler = StandardScaler()
X = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Convert numpy data to PyTorch tensors
X_train = torch.tensor(X_train, dtype=torch.float32)
X_test = torch.tensor(X_test, dtype=torch.float32)
y_train = torch.tensor(y_train, dtype=torch.int64)
y_test = torch.tensor(y_test, dtype=torch.int64)
return X_train, X_test, y_train, y_test
def evaluate_predictions(y_test, y_pred):
return accuracy_score(y_test, y_pred)
#model.py
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
class IrisClassifier(nn.Module):
def __init__(self):
super(IrisClassifier, self).__init__()
self.fc = nn.Sequential(
nn.Linear(4, 16),
nn.ReLU(),
nn.Linear(16, 3)
)
def forward(self, x):
return self.fc(x)
def train_model(self, X_train, y_train, epochs, lr, batch_size):
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(self.parameters(), lr=lr)
# Create DataLoader for batches
dataset = TensorDataset(X_train, y_train)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
for epoch in range(epochs):
for batch_X, batch_y in dataloader:
optimizer.zero_grad()
outputs = self(batch_X)
loss = criterion(outputs, batch_y)
loss.backward()
optimizer.step()
def predict(self, X_test):
with torch.no_grad():
outputs = self(X_test)
_, predicted = outputs.max(1)
return predicted.numpy()
#main.py
from utils import load_data, evaluate_predictions
from model import IrisClassifier as Classifier
def main():
# Model training and evaluation
"""
inputs = tokenizer(input_text, return_tensors="pt").cuda()
outputs = model.generate(**inputs, max_new_tokens=140)
print(tokenizer.decode(outputs[0]))
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).
<!-- original-model-card end -->
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jstoone/distil-ast-audioset-finetuned-cry | 2023-11-05T14:34:30.000Z | [
"transformers",
"tensorboard",
"safetensors",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:Nooon/Donate_a_cry",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | jstoone | null | null | jstoone/distil-ast-audioset-finetuned-cry | 0 | 2 | transformers | 2023-11-05T13:36:12 | ---
license: apache-2.0
base_model: bookbot/distil-ast-audioset
tags:
- generated_from_trainer
datasets:
- Nooon/Donate_a_cry
metrics:
- accuracy
model-index:
- name: distil-ast-audioset-finetuned-cry
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: DonateACry
type: Nooon/Donate_a_cry
config: train
split: train
args: train
metrics:
- name: Accuracy
type: accuracy
value: 0.6363636363636364
---
<!-- 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. -->
# distil-ast-audioset-finetuned-cry
This model is a fine-tuned version of [bookbot/distil-ast-audioset](https://huggingface.co/bookbot/distil-ast-audioset) on the DonateACry dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8592
- Accuracy: 0.6364
## 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
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9595 | 1.0 | 11 | 1.6120 | 0.0909 |
| 1.3053 | 2.0 | 22 | 1.3677 | 0.2727 |
| 0.7604 | 3.0 | 33 | 1.9563 | 0.1818 |
| 0.4351 | 4.0 | 44 | 1.3875 | 0.5455 |
| 0.316 | 5.0 | 55 | 1.7235 | 0.5455 |
| 0.0949 | 6.0 | 66 | 1.5362 | 0.6364 |
| 0.0355 | 7.0 | 77 | 1.8020 | 0.5455 |
| 0.0156 | 8.0 | 88 | 1.8320 | 0.6364 |
| 0.0102 | 9.0 | 99 | 1.9028 | 0.6364 |
| 0.0061 | 10.0 | 110 | 1.8592 | 0.6364 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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LaTarn/ta-price-setfit-model | 2023-11-05T13:39:39.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ta-price-setfit-model | 0 | 2 | sentence-transformers | 2023-11-05T13:39:07 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ta-price-setfit-model
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("LaTarn/ta-price-setfit-model")
# 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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LaTarn/ta-service-setfit-model | 2023-11-05T15:11:15.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ta-service-setfit-model | 0 | 2 | sentence-transformers | 2023-11-05T15:10:50 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ta-service-setfit-model
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("LaTarn/ta-service-setfit-model")
# 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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StevenPerrin/ppo-LunarLander-v2 | 2023-11-05T15:30:01.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | StevenPerrin | null | null | StevenPerrin/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-05T15:29:41 | ---
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: 243.51 +/- 44.57
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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DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-FalconRescaled_14000 | 2023-11-05T18:42:11.000Z | [
"sentence-transformers",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | DragosGorduza | null | null | DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-FalconRescaled_14000 | 0 | 2 | sentence-transformers | 2023-11-05T15:46:34 | ---
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 1024 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 276533 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"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": 14000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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 --> | 3,677 | [
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aspends/binary_tumor_classifier | 2023-11-05T17:32:29.000Z | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | aspends | null | null | aspends/binary_tumor_classifier | 0 | 2 | transformers | 2023-11-05T16:31:06 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: aspends/binary_tumor_classifier
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. -->
# aspends/binary_tumor_classifier
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: 0.0614
- Validation Loss: 1.8879
- Train Accuracy: 0.5166
- 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': 6585, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3737 | 1.3685 | 0.4864 | 0 |
| 0.1417 | 1.5816 | 0.5136 | 1 |
| 0.1013 | 1.6942 | 0.5196 | 2 |
| 0.0573 | 1.8671 | 0.5257 | 3 |
| 0.0614 | 1.8879 | 0.5166 | 4 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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ali619/distilbert-base-uncased-finetuned-emotion-detector-from-text | 2023-11-05T18:51:25.000Z | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | ali619 | null | null | ali619/distilbert-base-uncased-finetuned-emotion-detector-from-text | 0 | 2 | transformers | 2023-11-05T17:17:16 | ---
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-detector-from-text
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: 0.9345
- name: F1
type: f1
value: 0.9346813045403889
---
<!-- 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-detector-from-text
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: 0.1628
- Accuracy: 0.9345
- F1: 0.9347
## Model description
This model is trained on english tweets and can classify emotions in text files.
## Intended uses & limitations
More information needed
## Training and evaluation data
16,000 train samples
2,000 validation samples
2,000 test samples
## Training procedure
Finetunning distilbert-base-uncased
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1038 | 1.0 | 250 | 0.1757 | 0.9325 | 0.9329 |
| 0.094 | 2.0 | 500 | 0.1628 | 0.9345 | 0.9347 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
Feiiisal/cardiffnlp_twitter_roberta_base_sentiment_latest_Nov2023 | 2023-11-05T18:16:27.000Z | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us",
"has_space"
] | text-classification | Feiiisal | null | null | Feiiisal/cardiffnlp_twitter_roberta_base_sentiment_latest_Nov2023 | 0 | 2 | transformers | 2023-11-05T17:36:45 | ---
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: cardiffnlp_twitter_roberta_base_sentiment_latest_Nov2023
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# cardiffnlp_twitter_roberta_base_sentiment_latest_Nov2023
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3658
- Accuracy: 0.8045
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6116 | 0.2 | 100 | 0.4453 | 0.6965 |
| 0.4047 | 0.4 | 200 | 0.3999 | 0.735 |
| 0.3979 | 0.6 | 300 | 0.3641 | 0.7655 |
| 0.3828 | 0.8 | 400 | 0.3512 | 0.7635 |
| 0.3805 | 1.0 | 500 | 0.3489 | 0.776 |
| 0.3454 | 1.2 | 600 | 0.3488 | 0.774 |
| 0.3135 | 1.4 | 700 | 0.3529 | 0.785 |
| 0.3216 | 1.6 | 800 | 0.3344 | 0.7845 |
| 0.3005 | 1.8 | 900 | 0.3793 | 0.789 |
| 0.3041 | 2.0 | 1000 | 0.3324 | 0.7925 |
| 0.2126 | 2.2 | 1100 | 0.3839 | 0.7895 |
| 0.2218 | 2.4 | 1200 | 0.3653 | 0.7955 |
| 0.1986 | 2.6 | 1300 | 0.3745 | 0.803 |
| 0.2049 | 2.8 | 1400 | 0.3586 | 0.802 |
| 0.1911 | 3.0 | 1500 | 0.3658 | 0.8045 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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sh-holmes/a2c-PandaReachDense-v3 | 2023-11-05T18:28:03.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | sh-holmes | null | null | sh-holmes/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-11-05T18:22:24 | ---
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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Faith-theAnalyst/twitter_roberta_sentiment_model | 2023-11-05T19:09:03.000Z | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us",
"has_space"
] | text-classification | Faith-theAnalyst | null | null | Faith-theAnalyst/twitter_roberta_sentiment_model | 0 | 2 | transformers | 2023-11-05T18:46:06 | ---
base_model: cardiffnlp/twitter-roberta-base-sentiment-latest
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: twitter_roberta_sentiment_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. -->
# twitter_roberta_sentiment_model
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3658
- Accuracy: 0.8045
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6116 | 0.2 | 100 | 0.4453 | 0.6965 |
| 0.4047 | 0.4 | 200 | 0.3999 | 0.735 |
| 0.3979 | 0.6 | 300 | 0.3641 | 0.7655 |
| 0.3828 | 0.8 | 400 | 0.3512 | 0.7635 |
| 0.3805 | 1.0 | 500 | 0.3489 | 0.776 |
| 0.3454 | 1.2 | 600 | 0.3488 | 0.774 |
| 0.3135 | 1.4 | 700 | 0.3529 | 0.785 |
| 0.3216 | 1.6 | 800 | 0.3344 | 0.7845 |
| 0.3005 | 1.8 | 900 | 0.3793 | 0.789 |
| 0.3041 | 2.0 | 1000 | 0.3324 | 0.7925 |
| 0.2126 | 2.2 | 1100 | 0.3839 | 0.7895 |
| 0.2218 | 2.4 | 1200 | 0.3653 | 0.7955 |
| 0.1986 | 2.6 | 1300 | 0.3745 | 0.803 |
| 0.2049 | 2.8 | 1400 | 0.3586 | 0.802 |
| 0.1911 | 3.0 | 1500 | 0.3658 | 0.8045 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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tomashs/sdu_fine_tuning_beto_MLP_ud | 2023-11-05T18:52:59.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | tomashs | null | null | tomashs/sdu_fine_tuning_beto_MLP_ud | 0 | 2 | transformers | 2023-11-05T18:52:44 | ---
base_model: tomashs/acro_fine_tuning_beto_MLP_ud
tags:
- generated_from_trainer
model-index:
- name: sdu_fine_tuning_beto_MLP_ud
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. -->
# sdu_fine_tuning_beto_MLP_ud
This model is a fine-tuned version of [tomashs/acro_fine_tuning_beto_MLP_ud](https://huggingface.co/tomashs/acro_fine_tuning_beto_MLP_ud) 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: 1.25e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 16
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
EstherSan/car_identified_model_2 | 2023-11-06T08:23:01.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | EstherSan | null | null | EstherSan/car_identified_model_2 | 0 | 2 | transformers | 2023-11-05T18:54:14 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- f1
- accuracy
model-index:
- name: car_identified_model_2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: F1
type: f1
value: 0.9304373348987379
- name: Accuracy
type: accuracy
value: 0.8032694475760992
---
<!-- 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. -->
# car_identified_model_2
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0254
- F1: 0.9304
- Roc Auc: 0.9459
- Accuracy: 0.8033
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.717 | 1.0 | 111 | 0.1697 | 0.3310 | 0.6006 | 0.0 |
| 0.717 | 2.0 | 222 | 0.1305 | 0.5421 | 0.6902 | 0.0 |
| 0.717 | 3.0 | 333 | 0.1027 | 0.6412 | 0.7419 | 0.1037 |
| 0.717 | 4.0 | 444 | 0.0839 | 0.7503 | 0.8072 | 0.3072 |
| 0.1377 | 5.0 | 555 | 0.0693 | 0.8256 | 0.8601 | 0.4949 |
| 0.1377 | 6.0 | 666 | 0.0600 | 0.8550 | 0.8831 | 0.5784 |
| 0.1377 | 7.0 | 777 | 0.0516 | 0.8874 | 0.9091 | 0.6680 |
| 0.1377 | 8.0 | 888 | 0.0455 | 0.9050 | 0.9222 | 0.7136 |
| 0.1377 | 9.0 | 999 | 0.0419 | 0.9097 | 0.9267 | 0.7322 |
| 0.0427 | 10.0 | 1110 | 0.0378 | 0.9160 | 0.9318 | 0.7514 |
| 0.0427 | 11.0 | 1221 | 0.0359 | 0.9199 | 0.9359 | 0.7627 |
| 0.0427 | 12.0 | 1332 | 0.0334 | 0.9241 | 0.9392 | 0.7745 |
| 0.0427 | 13.0 | 1443 | 0.0327 | 0.9212 | 0.9372 | 0.7711 |
| 0.0183 | 14.0 | 1554 | 0.0310 | 0.9251 | 0.9402 | 0.7835 |
| 0.0183 | 15.0 | 1665 | 0.0301 | 0.9274 | 0.9414 | 0.7858 |
| 0.0183 | 16.0 | 1776 | 0.0292 | 0.9277 | 0.9424 | 0.7914 |
| 0.0183 | 17.0 | 1887 | 0.0295 | 0.9246 | 0.9404 | 0.7841 |
| 0.0183 | 18.0 | 1998 | 0.0284 | 0.9252 | 0.9410 | 0.7824 |
| 0.0106 | 19.0 | 2109 | 0.0282 | 0.9274 | 0.9428 | 0.7920 |
| 0.0106 | 20.0 | 2220 | 0.0276 | 0.9271 | 0.9425 | 0.7931 |
| 0.0106 | 21.0 | 2331 | 0.0268 | 0.9290 | 0.9442 | 0.7971 |
| 0.0106 | 22.0 | 2442 | 0.0270 | 0.9269 | 0.9423 | 0.7914 |
| 0.0071 | 23.0 | 2553 | 0.0268 | 0.9284 | 0.9439 | 0.7965 |
| 0.0071 | 24.0 | 2664 | 0.0262 | 0.9298 | 0.9452 | 0.8027 |
| 0.0071 | 25.0 | 2775 | 0.0260 | 0.9297 | 0.9449 | 0.7982 |
| 0.0071 | 26.0 | 2886 | 0.0262 | 0.9284 | 0.9438 | 0.7965 |
| 0.0071 | 27.0 | 2997 | 0.0261 | 0.9293 | 0.9445 | 0.7965 |
| 0.0053 | 28.0 | 3108 | 0.0261 | 0.9284 | 0.9438 | 0.7976 |
| 0.0053 | 29.0 | 3219 | 0.0261 | 0.9274 | 0.9435 | 0.7959 |
| 0.0053 | 30.0 | 3330 | 0.0256 | 0.9298 | 0.9455 | 0.8005 |
| 0.0053 | 31.0 | 3441 | 0.0255 | 0.9298 | 0.9453 | 0.8016 |
| 0.0042 | 32.0 | 3552 | 0.0256 | 0.9297 | 0.9453 | 0.7988 |
| 0.0042 | 33.0 | 3663 | 0.0255 | 0.9297 | 0.9452 | 0.8005 |
| 0.0042 | 34.0 | 3774 | 0.0254 | 0.9292 | 0.9455 | 0.8010 |
| 0.0042 | 35.0 | 3885 | 0.0256 | 0.9290 | 0.9447 | 0.7993 |
| 0.0042 | 36.0 | 3996 | 0.0256 | 0.9279 | 0.9443 | 0.7976 |
| 0.0035 | 37.0 | 4107 | 0.0255 | 0.9294 | 0.9452 | 0.8005 |
| 0.0035 | 38.0 | 4218 | 0.0261 | 0.9275 | 0.9443 | 0.7993 |
| 0.0035 | 39.0 | 4329 | 0.0254 | 0.9304 | 0.9459 | 0.8033 |
| 0.0035 | 40.0 | 4440 | 0.0254 | 0.9302 | 0.9460 | 0.8044 |
| 0.003 | 41.0 | 4551 | 0.0256 | 0.9291 | 0.9445 | 0.7999 |
| 0.003 | 42.0 | 4662 | 0.0255 | 0.9290 | 0.9451 | 0.8010 |
| 0.003 | 43.0 | 4773 | 0.0256 | 0.9289 | 0.9453 | 0.8005 |
| 0.003 | 44.0 | 4884 | 0.0256 | 0.9287 | 0.9450 | 0.8005 |
| 0.003 | 45.0 | 4995 | 0.0255 | 0.9288 | 0.9450 | 0.8005 |
| 0.0027 | 46.0 | 5106 | 0.0255 | 0.9291 | 0.9450 | 0.7999 |
| 0.0027 | 47.0 | 5217 | 0.0254 | 0.9291 | 0.9453 | 0.8010 |
| 0.0027 | 48.0 | 5328 | 0.0255 | 0.9287 | 0.9450 | 0.7993 |
| 0.0027 | 49.0 | 5439 | 0.0254 | 0.9297 | 0.9453 | 0.7999 |
| 0.0025 | 50.0 | 5550 | 0.0254 | 0.9294 | 0.9453 | 0.8021 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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Sao10K/Euryale-1.4-L2-70B | 2023-11-06T22:59:51.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:llama2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Sao10K | null | null | Sao10K/Euryale-1.4-L2-70B | 0 | 2 | transformers | 2023-11-05T19:10:47 | ---
license: llama2
language:
- en
---
gguf quants: https://huggingface.co/Sao10K/Euryale-1.4-L2-70B-GGUF
1.3, but better? I guess.
Base Merged Model ratios adjusted.
NSFL portion of Hesperus v1 dataset trained and applied.
LimaRP merged in at a ~25% weight at the end.
Subjectively better in some aspects eg. long form rp, worse than the other, eg. chat-style rps.
overall a minor improvement in my eyes.
1.5 will include Hesperus v2 dataset in its entirety.
format: alpaca. | 484 | [
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HugHugHug1111/test | 2023-11-05T21:24:50.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | HugHugHug1111 | null | null | HugHugHug1111/test | 0 | 2 | peft | 2023-11-05T21:05:42 | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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## Training Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0
## 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: float16
### Framework versions
- PEFT 0.6.0
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tomashs/sdu_fine_tuning_beto_lda_ud | 2023-11-05T22:04:43.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | tomashs | null | null | tomashs/sdu_fine_tuning_beto_lda_ud | 0 | 2 | transformers | 2023-11-05T22:04:26 | ---
base_model: tomashs/acro_fine_tuning_beto_lda_ud
tags:
- generated_from_trainer
model-index:
- name: sdu_fine_tuning_beto_lda_ud
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. -->
# sdu_fine_tuning_beto_lda_ud
This model is a fine-tuned version of [tomashs/acro_fine_tuning_beto_lda_ud](https://huggingface.co/tomashs/acro_fine_tuning_beto_lda_ud) 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 16
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
TheBloke/Hermes-Trismegistus-Mistral-7B-AWQ | 2023-11-05T23:58:11.000Z | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"mistral-7b",
"instruct",
"finetune",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:teknium/trismegistus-project",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/Hermes-Trismegistus-Mistral-7B-AWQ | 0 | 2 | transformers | 2023-11-05T23:42:55 | ---
base_model: teknium/Hermes-Trismegistus-Mistral-7B
datasets:
- teknium/trismegistus-project
inference: false
language:
- en
license: apache-2.0
model-index:
- name: Hermes-Trismegistus-Mistral-7B
results: []
model_creator: Teknium
model_name: Hermes Trismegistus Mistral 7B
model_type: mistral
prompt_template: 'USER: {prompt}
ASSISTANT:
'
quantized_by: TheBloke
tags:
- mistral-7b
- instruct
- finetune
- gpt4
- synthetic data
- distillation
---
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# Hermes Trismegistus Mistral 7B - AWQ
- Model creator: [Teknium](https://huggingface.co/teknium)
- Original model: [Hermes Trismegistus Mistral 7B](https://huggingface.co/teknium/Hermes-Trismegistus-Mistral-7B)
<!-- description start -->
## Description
This repo contains AWQ model files for [Teknium's Hermes Trismegistus Mistral 7B](https://huggingface.co/teknium/Hermes-Trismegistus-Mistral-7B).
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/Hermes-Trismegistus-Mistral-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF)
* [Teknium's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/teknium/Hermes-Trismegistus-Mistral-7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: User-Assistant
```
USER: {prompt}
ASSISTANT:
```
<!-- prompt-template 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/Hermes-Trismegistus-Mistral-7B-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.15 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/Hermes-Trismegistus-Mistral-7B-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: `Hermes-Trismegistus-Mistral-7B-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/Hermes-Trismegistus-Mistral-7B-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'''USER: {prompt}
ASSISTANT:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Hermes-Trismegistus-Mistral-7B-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/Hermes-Trismegistus-Mistral-7B-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'''USER: {prompt}
ASSISTANT:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_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/Hermes-Trismegistus-Mistral-7B-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'''USER: {prompt}
ASSISTANT:
'''
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: Teknium's Hermes Trismegistus Mistral 7B
## Model Description:
Transcendence is All You Need! Mistral Trismegistus is a model made for people interested in the esoteric, occult, and spiritual.
### Trismegistus evolved, trained over Hermes 2.5, the model performs far better in all tasks, including esoteric tasks!
The change between Mistral-Trismegistus and Hermes-Trismegistus is that this version trained over hermes 2.5 instead of the base mistral model, this means it is full of task capabilities that it Trismegistus can utilize for all esoteric and occult tasks, and performs them far better than ever before.
Here are some outputs:
## Acknowledgements:
Special thanks to @a16z.
## Dataset:
This model was trained on a 100% synthetic, gpt-4 generated dataset, about ~10,000 examples, on a wide and diverse set of both tasks and knowledge about the esoteric, occult, and spiritual.
The dataset will be released soon!
## Usage:
Prompt Format:
```
USER: <prompt>
ASSISTANT:
```
OR
```
<system message>
USER: <prompt>
ASSISTANT:
```
## Benchmarks:
No benchmark can capture the nature and essense of the quality of spirituality and esoteric knowledge and tasks. You will have to try testing it yourself!
Training run on wandb here: https://wandb.ai/teknium1/occult-expert-mistral-7b/runs/coccult-expert-mistral-6/overview
## Licensing:
Apache 2.0
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] |
TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF | 2023-11-05T23:47:53.000Z | [
"transformers",
"mistral",
"mistral-7b",
"instruct",
"finetune",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:teknium/trismegistus-project",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | TheBloke | null | null | TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF | 5 | 2 | transformers | 2023-11-05T23:42:55 | ---
base_model: teknium/Hermes-Trismegistus-Mistral-7B
datasets:
- teknium/trismegistus-project
inference: false
language:
- en
license: apache-2.0
model-index:
- name: Hermes-Trismegistus-Mistral-7B
results: []
model_creator: Teknium
model_name: Hermes Trismegistus Mistral 7B
model_type: mistral
prompt_template: 'USER: {prompt}
ASSISTANT:
'
quantized_by: TheBloke
tags:
- mistral-7b
- instruct
- finetune
- gpt4
- synthetic data
- distillation
---
<!-- 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 -->
# Hermes Trismegistus Mistral 7B - GGUF
- Model creator: [Teknium](https://huggingface.co/teknium)
- Original model: [Hermes Trismegistus Mistral 7B](https://huggingface.co/teknium/Hermes-Trismegistus-Mistral-7B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Teknium's Hermes Trismegistus Mistral 7B](https://huggingface.co/teknium/Hermes-Trismegistus-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 incomplete 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/Hermes-Trismegistus-Mistral-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF)
* [Teknium's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/teknium/Hermes-Trismegistus-Mistral-7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: User-Assistant
```
USER: {prompt}
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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [hermes-trismegistus-mistral-7b.Q2_K.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [hermes-trismegistus-mistral-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [hermes-trismegistus-mistral-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [hermes-trismegistus-mistral-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [hermes-trismegistus-mistral-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-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 |
| [hermes-trismegistus-mistral-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [hermes-trismegistus-mistral-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [hermes-trismegistus-mistral-7b.Q5_0.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [hermes-trismegistus-mistral-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [hermes-trismegistus-mistral-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [hermes-trismegistus-mistral-7b.Q6_K.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-mistral-7b.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [hermes-trismegistus-mistral-7b.Q8_0.gguf](https://huggingface.co/TheBloke/Hermes-Trismegistus-Mistral-7B-GGUF/blob/main/hermes-trismegistus-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/Hermes-Trismegistus-Mistral-7B-GGUF and below it, a specific filename to download, such as: hermes-trismegistus-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/Hermes-Trismegistus-Mistral-7B-GGUF hermes-trismegistus-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/Hermes-Trismegistus-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/Hermes-Trismegistus-Mistral-7B-GGUF hermes-trismegistus-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 hermes-trismegistus-mistral-7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "USER: {prompt}\nASSISTANT:"
```
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/Hermes-Trismegistus-Mistral-7B-GGUF", model_file="hermes-trismegistus-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**: 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: Teknium's Hermes Trismegistus Mistral 7B
## Model Description:
Transcendence is All You Need! Mistral Trismegistus is a model made for people interested in the esoteric, occult, and spiritual.
### Trismegistus evolved, trained over Hermes 2.5, the model performs far better in all tasks, including esoteric tasks!
The change between Mistral-Trismegistus and Hermes-Trismegistus is that this version trained over hermes 2.5 instead of the base mistral model, this means it is full of task capabilities that it Trismegistus can utilize for all esoteric and occult tasks, and performs them far better than ever before.
Here are some outputs:
## Acknowledgements:
Special thanks to @a16z.
## Dataset:
This model was trained on a 100% synthetic, gpt-4 generated dataset, about ~10,000 examples, on a wide and diverse set of both tasks and knowledge about the esoteric, occult, and spiritual.
The dataset will be released soon!
## Usage:
Prompt Format:
```
USER: <prompt>
ASSISTANT:
```
OR
```
<system message>
USER: <prompt>
ASSISTANT:
```
## Benchmarks:
No benchmark can capture the nature and essense of the quality of spirituality and esoteric knowledge and tasks. You will have to try testing it yourself!
Training run on wandb here: https://wandb.ai/teknium1/occult-expert-mistral-7b/runs/coccult-expert-mistral-6/overview
## Licensing:
Apache 2.0
<!-- original-model-card end -->
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GuysTrans/bart-base-translate-en-vi | 2023-11-06T09:48:17.000Z | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:mt_eng_vietnamese",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | GuysTrans | null | null | GuysTrans/bart-base-translate-en-vi | 0 | 2 | transformers | 2023-11-06T00:56:50 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mt_eng_vietnamese
metrics:
- rouge
- bleu
model-index:
- name: bart-base-translate-en-vi
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mt_eng_vietnamese
type: mt_eng_vietnamese
config: iwslt2015-en-vi
split: validation
args: iwslt2015-en-vi
metrics:
- name: Rouge1
type: rouge
value: 56.0521
- name: Bleu
type: bleu
value: 12.7027
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-translate-en-vi
This model is a fine-tuned version of [GuysTrans/bart-base-translate-en-vi](https://huggingface.co/GuysTrans/bart-base-translate-en-vi) on the mt_eng_vietnamese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6744
- Rouge1: 56.0521
- Rouge2: 34.1329
- Rougel: 47.1473
- Rougelsum: 47.8238
- Bleu: 12.7027
- Gen Len: 19.9921
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|
| 0.661 | 1.0 | 16665 | 0.6744 | 56.0521 | 34.1329 | 47.1473 | 47.8238 | 12.7027 | 19.9921 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0+cu118
- Datasets 2.10.1
- Tokenizers 0.13.3
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] |
Jenti-Kaeri/kopen-platypus-ko-llama2-13b | 2023-11-06T04:17:52.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:kyujinpy/KOpen-platypus",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Jenti-Kaeri | null | null | Jenti-Kaeri/kopen-platypus-ko-llama2-13b | 0 | 2 | transformers | 2023-11-06T01:03:10 | ---
datasets:
- kyujinpy/KOpen-platypus
---
Base Model : Llama-2-13b-hf
datasets:
- kyujinpy/KOpen-platypus | 111 | [
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] |
matthewchung74/MedMistral-7B | 2023-11-06T17:13:07.000Z | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | matthewchung74 | null | null | matthewchung74/MedMistral-7B | 0 | 2 | peft | 2023-11-06T01:43:21 | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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#### Summary
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: 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 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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] |
jordandavis/cb2_rugs_lora | 2023-11-06T11:27:36.000Z | [
"diffusers",
"if",
"if-diffusers",
"inpaint",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | null | jordandavis | null | null | jordandavis/cb2_rugs_lora | 0 | 2 | diffusers | 2023-11-06T04:21:41 |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-inpainting
instance_prompt: sks chair
tags:
- if
- if-diffusers
- inpaint
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - jordandavis/cb2_rugs_lora
These are LoRA adaption weights for runwayml/stable-diffusion-inpainting. The weights were trained on sks chair using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
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jonathanjordan21/donut-finetuned-drugs-composition-indonesian | 2023-11-06T09:55:16.000Z | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"medical",
"chemistry",
"id",
"en",
"dataset:jonathanjordan21/drugs-composition-indonesian-donut",
"license:mit",
"endpoints_compatible",
"region:us",
"has_space"
] | null | jonathanjordan21 | null | null | jonathanjordan21/donut-finetuned-drugs-composition-indonesian | 0 | 2 | transformers | 2023-11-06T05:38:49 | ---
widget:
- text: >-
def add ( severity , progname , & block ) return true if io . nil? ||
severity < level message = format_message ( severity , progname , yield )
MUTEX . synchronize { io . write ( message ) } true end
license: mit
language:
- id
- en
datasets:
- jonathanjordan21/drugs-composition-indonesian-donut
library_name: transformers
tags:
- medical
- chemistry
---
## Model description
This model is based on the `jonathanjordan21/donut_fine_tuning_food_composition_id` model. The training dataset is created by manually scrapping images across the internet, available in `jonathanjordan21/drugs-composition-indonesian-donut`
## Usage & limitations
The model could be used to detect the text of drug compositions from images of drug packages. It is capable to create a json format of the components described in the image. However, due to lack of data, the texts in the image must be concisely upright.
### Output Example
Model Output :
```python
'<s_kmpsi><s_komposisi><s_obat>Vitamin E</s_obat><s_takaran>30 I.U.</s_takaran><sep/><s_obat>Tiamin HCl (B1)</s_obat><s_takaran>100 mg</s_takaran><sep/><s_obat>Piridoksin HCl (B6)</s_obat><s_takaran>50 mg</s_takaran><sep/><s_obat>Sianokobalamin (B12)</s_obat><s_takaran>100 mcg</s_takaran><sep/><s_obat>K-l-aspartat</s_obat><s_takaran>100 mg</s_takaran><sep/><s_obat>Mg-l-aspartat</s_obat><s_takaran>100 mg</s_takaran></s_komposisi><s_desc></s_desc></s_kmpsi>'
```
Json Parsed Output :
```python
{'komposisi': [{'obat': 'Vitamin E', 'takaran': '30 I.U.'}, {'obat': 'Tiamin HCl (B1)', 'takaran': '100 mg'}, {'obat': 'Piridoksin HCl (B6)', 'takaran': '50 mg'}, {'obat': 'Sianokobalamin (B12)', 'takaran': '100 mcg'}, {'obat': 'K-l-aspartat', 'takaran': '100 mg'}, {'obat': 'Mg-l-aspartat', 'takaran': '100 mg'}], 'desc': ''}
```
### How to use
Load Donut Processor and Model
```python
from transformers import DonutProcessor, VisionEncoderDecoderModel
# Load processor
processor = DonutProcessor.from_pretrained("jonathanjordan21/donut-finetuned-drugs-composition-indonesian")
# Load model
model = VisionEncoderDecoderModel.from_pretrained("jonathanjordan21/donut-finetuned-drugs-composition-indonesian")
```
Create JSON parser
```python
from PIL import Image
from io import BytesIO
import re
import torch
def get_komposisi(image_path, image=None):
device = "cuda" if torch.cuda.is_available() else "cpu"
image = Image.open(image_path).convert('RGB') if image== None else image.convert('RGB')
task_prompt = "<s_kmpsi>"
decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
pixel_values = processor(image, return_tensors="pt").pixel_values
outputs = model.generate(
pixel_values.to(device),
decoder_input_ids=decoder_input_ids.to(device),
max_length=model.decoder.config.max_position_embeddings,
early_stopping=True,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
use_cache=True,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True,
)
sequence1 = processor.batch_decode(outputs.sequences)[0]
sequence2 = sequence1.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
sequence3 = re.sub(r"<.*?>", "", sequence2, count=1).strip() # remove first task start token
return processor.token2json(sequence3)
```
Get JSON output from an image
```python
import requests
image = requests.get('https://down-id.img.susercontent.com/file/b6812557ba97d24354970cebeac04d48').content
print(get_komposisi("", Image.open(BytesIO(image))))
``` | 3,698 | [
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Jimmy-Xing/ppo-LunarLander-v2 | 2023-11-06T05:57:43.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Jimmy-Xing | null | null | Jimmy-Xing/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-06T05:57:23 | ---
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: -1158.38 +/- 228.56
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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adonlee/LLaMA_2_13B_SFT_v1 | 2023-11-06T09:07:53.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | adonlee | null | null | adonlee/LLaMA_2_13B_SFT_v1 | 0 | 2 | transformers | 2023-11-06T06:27:54 | ---
license: apache-2.0
---
This is a general capability upgrade to Llama-2-13B, using open source data to improve multilingual ability, overall knowledge, extended communication, and technical skill.
This model is primarily recommended as a superior-to-Llama-2 baseline for additional finetuning, not for direct deployment to production as a chat model. The user accepts full responsibility for all outputs. | 410 | [
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SumitxThokar/Landing-on-Luna | 2023-11-06T06:29:33.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | SumitxThokar | null | null | SumitxThokar/Landing-on-Luna | 0 | 2 | stable-baselines3 | 2023-11-06T06:29:13 | ---
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: 270.47 +/- 16.30
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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LaTarn/ac-density-setfit-model | 2023-11-06T07:23:14.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ac-density-setfit-model | 0 | 2 | sentence-transformers | 2023-11-06T07:22:55 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ac-density-setfit-model
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("LaTarn/ac-density-setfit-model")
# 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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jamesgpt1/f_model_2 | 2023-11-06T07:34:42.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | sentence-similarity | jamesgpt1 | null | null | jamesgpt1/f_model_2 | 0 | 2 | sentence-transformers | 2023-11-06T07:34:01 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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)
```
## 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})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 1,518 | [
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] |
sean-styleai/lds-w121 | 2023-11-06T08:37:06.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | sean-styleai | null | null | sean-styleai/lds-w121 | 0 | 2 | diffusers | 2023-11-06T08:24:40 |
---
license: creativeml-openrail-m
base_model: stablediffusionapi/realistic-vision-51
instance_prompt: a photo of hta beautiful woman fashion model
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - sean-styleai/lds-w121
These are LoRA adaption weights for stablediffusionapi/realistic-vision-51. The weights were trained on a photo of hta beautiful woman fashion model using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
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] |
sean-styleai/lds-w071 | 2023-11-06T09:13:32.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | sean-styleai | null | null | sean-styleai/lds-w071 | 0 | 2 | diffusers | 2023-11-06T08:58:21 |
---
license: creativeml-openrail-m
base_model: stablediffusionapi/realistic-vision-51
instance_prompt: a photo of hta beautiful woman fashion model
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - sean-styleai/lds-w071
These are LoRA adaption weights for stablediffusionapi/realistic-vision-51. The weights were trained on a photo of hta beautiful woman fashion model using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
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Apucs/bert-fine-tuned-cola | 2023-11-06T10:23:04.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Apucs | null | null | Apucs/bert-fine-tuned-cola | 0 | 2 | transformers | 2023-11-06T09:05:44 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-fine-tuned-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: 0.5730897440667784
---
<!-- 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-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8483
- Matthews Correlation: 0.5731
## 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 | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4485 | 1.0 | 1069 | 0.4392 | 0.5550 |
| 0.3059 | 2.0 | 2138 | 0.6730 | 0.5576 |
| 0.1866 | 3.0 | 3207 | 0.8483 | 0.5731 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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bambadij/sentiment_analysis_model_trainer | 2023-11-06T13:36:19.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | bambadij | null | null | bambadij/sentiment_analysis_model_trainer | 0 | 2 | transformers | 2023-11-06T09:51:29 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: sentiment_analysis_model_trainer
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. -->
# sentiment_analysis_model_trainer
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: 0.6184
## 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6926 | 1.0 | 1000 | 0.6214 |
| 0.5621 | 2.0 | 2000 | 0.6184 |
| 0.398 | 3.0 | 3000 | 0.7893 |
| 0.2447 | 4.0 | 4000 | 1.1513 |
| 0.1501 | 5.0 | 5000 | 1.3035 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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vgarg/my_zs_model3 | 2023-11-06T09:55:07.000Z | [
"sentence-transformers",
"safetensors",
"bart",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | vgarg | null | null | vgarg/my_zs_model3 | 0 | 2 | sentence-transformers | 2023-11-06T09:54:03 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# vgarg/my_zs_model3
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("vgarg/my_zs_model3")
# 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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hohorong/tool_choose2_micro | 2023-11-06T12:17:00.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | hohorong | null | null | hohorong/tool_choose2_micro | 0 | 2 | transformers | 2023-11-06T10:09:07 | ---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: tool_choose2_micro
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. -->
# tool_choose2_micro
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1521
- Micro f1: 0.4078
- Macro f1: 0.1041
## 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: 4
- eval_batch_size: 16
- seed: 1000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Micro f1 | Macro f1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.2306 | 1.0 | 223 | 0.1521 | 0.4078 | 0.1041 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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danielcfox/sample_text_classification | 2023-11-06T13:27:20.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | danielcfox | null | null | danielcfox/sample_text_classification | 0 | 2 | transformers | 2023-11-06T11:18:43 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: danielcfox/sample_text_classification
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. -->
# danielcfox/sample_text_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0462
- Validation Loss: 0.2242
- Train Accuracy: 0.9333
- 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': 'Adam', '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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1872 | 0.1868 | 0.9303 | 0 |
| 0.0965 | 0.2088 | 0.9318 | 1 |
| 0.0462 | 0.2242 | 0.9333 | 2 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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papanton/1hjf-1850-olkm-0 | 2023-11-06T12:00:03.000Z | [
"diffusers",
"text-to-image",
"autotrain",
"has_space",
"region:us"
] | text-to-image | papanton | null | null | papanton/1hjf-1850-olkm-0 | 0 | 2 | diffusers | 2023-11-06T12:00:01 |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of cjw man
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
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] |
Jannicus/medium_distilbert_classifier | 2023-11-06T12:48:58.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Jannicus | null | null | Jannicus/medium_distilbert_classifier | 0 | 2 | transformers | 2023-11-06T12:26:46 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: medium_distilbert_classifier
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. -->
# medium_distilbert_classifier
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: 0.0698
- Accuracy: 0.9861
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1225 | 1.0 | 810 | 0.0709 | 0.9840 |
| 0.0299 | 2.0 | 1620 | 0.0698 | 0.9861 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
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] |
jbochi/madlad400-7b-mt-bt | 2023-11-06T16:49:27.000Z | [
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] | translation | jbochi | null | null | jbochi/madlad400-7b-mt-bt | 0 | 2 | transformers | 2023-11-06T12:53:23 | ---
license: apache-2.0
language:
- en
- ru
- es
- fr
- de
- it
- pt
- pl
- nl
- vi
- tr
- sv
- id
- ro
- cs
- zh
- hu
- ja
- th
- fi
- fa
- uk
- da
- el
- "no"
- bg
- sk
- ko
- ar
- lt
- ca
- sl
- he
- et
- lv
- hi
- sq
- ms
- az
- sr
- ta
- hr
- kk
- is
- ml
- mr
- te
- af
- gl
- fil
- be
- mk
- eu
- bn
- ka
- mn
- bs
- uz
- ur
- sw
- yue
- ne
- kn
- kaa
- gu
- si
- cy
- eo
- la
- hy
- ky
- tg
- ga
- mt
- my
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- tt
- so
- ku
- ps
- pa
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- ha
- dv
- fy
- lb
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- mg
- gd
- am
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- mi
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- ba
- fo
- or
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- su
- kl
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- sm
- sn
- co
- zu
- ig
- yo
- pap
- st
- haw
- as
- oc
- cv
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- tet
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- sah
- br
- rm
- sa
- bo
- om
- se
- ce
- cnh
- ilo
- hil
- udm
- os
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- ti
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- ee
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- av
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- to
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library_name: transformers
tags:
- text-generation-inference
datasets:
- allenai/MADLAD-400
pipeline_tag: translation
---
T5ForConditionalGeneration files for Google's [Madlad-400](https://github.com/google-research/google-research/tree/master/madlad_400) 7.2B parameter MT-BT model.
Article: [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://arxiv.org/abs/2309.04662)
Abstract:
> We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations revealed by self-auditing MADLAD-400, and the role data auditing had in the dataset creation process. We then train and release a 10.7B-parameter multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data, and find that it is competitive with models that are significantly larger, and report the results on different domains. In addition, we train a 8B-parameter language model, and assess the results on few-shot translation. We make the baseline models available to the research community.
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer, GenerationConfig
model = T5ForConditionalGeneration.from_pretrained('jbochi/madlad400-7b-mt-bt')
tokenizer = T5Tokenizer.from_pretrained('jbochi/madlad400-7b-mt-bt')
text = "<2it> I love pizza!"
input_ids = tokenizer(text, return_tensors="pt").input_ids
outputs = model.generate(input_ids=input_ids)
tokenizer.decode(outputs[0], skip_special_tokens=True)
# Adoro la pizza!
```
Colab to generate these files is [here](https://colab.research.google.com/drive/1rZ2NRyl2zwmg0sQ2Wi-uZZF48iVYulTC#scrollTo=pVODoE6gA9sw). | 4,108 | [
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bartoszmaj/t5_billsum_finetune | 2023-11-06T13:09:55.000Z | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | bartoszmaj | null | null | bartoszmaj/t5_billsum_finetune | 0 | 2 | transformers | 2023-11-06T13:00:27 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: t5_billsum_finetune
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1926
---
<!-- 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_billsum_finetune
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0955
- Rouge1: 0.1926
- Rouge2: 0.0931
- Rougel: 0.163
- Rougelsum: 0.1635
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 248 | 2.1016 | 0.1917 | 0.0928 | 0.1624 | 0.1628 | 19.0 |
| No log | 2.0 | 496 | 2.0985 | 0.1931 | 0.0936 | 0.1635 | 0.1639 | 19.0 |
| 1.9507 | 3.0 | 744 | 2.0981 | 0.1926 | 0.0938 | 0.1633 | 0.1637 | 19.0 |
| 1.9507 | 4.0 | 992 | 2.0955 | 0.1926 | 0.0931 | 0.163 | 0.1635 | 19.0 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-FalconRescaled_new_14000 | 2023-11-06T13:31:14.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | DragosGorduza | null | null | DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-FalconRescaled_new_14000 | 0 | 2 | sentence-transformers | 2023-11-06T13:30:19 | ---
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 1024 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 276533 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"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": 14000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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 --> | 3,677 | [
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arshpareek/ppo-Pyramids | 2023-11-06T13:59:20.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | arshpareek | null | null | arshpareek/ppo-Pyramids | 0 | 2 | ml-agents | 2023-11-06T13:58:09 | ---
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: arshpareek/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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] |
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