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apple/OpenELM-270M | apple | "2024-05-02T00:55:40Z" | 8,592 | 62 | transformers | [
"transformers",
"safetensors",
"openelm",
"text-generation",
"custom_code",
"arxiv:2404.14619",
"license:other",
"autotrain_compatible",
"region:us"
] | text-generation | "2024-04-12T21:42:49Z" | ---
license: other
license_name: apple-sample-code-license
license_link: LICENSE
---
# OpenELM
*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari*
We introduce **OpenELM**, a family of **Open** **E**fficient **L**anguage **M**odels. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. We pretrained OpenELM models using the [CoreNet](https://github.com/apple/corenet) library. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters.
Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Please check license agreements and terms of these datasets before using them.
## Usage
We have provided an example function to generate output from OpenELM models loaded via [HuggingFace Hub](https://huggingface.co/docs/hub/) in `generate_openelm.py`.
You can try the model by running the following command:
```
python generate_openelm.py --model apple/OpenELM-270M --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2
```
Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token.
Additional arguments to the hugging face generate function can be passed via `generate_kwargs`. As an example, to speedup the inference, you can try [lookup token speculative generation](https://huggingface.co/docs/transformers/generation_strategies) by passing the `prompt_lookup_num_tokens` argument as follows:
```
python generate_openelm.py --model apple/OpenELM-270M --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10
```
Alternatively, try model-wise speculative generation with an [assistive model](https://huggingface.co/blog/assisted-generation) by passing a smaller model through the `assistant_model` argument, for example:
```
python generate_openelm.py --model apple/OpenELM-270M --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL]
```
## Main Results
### Zero-Shot
| **Model Size** | **ARC-c** | **ARC-e** | **BoolQ** | **HellaSwag** | **PIQA** | **SciQ** | **WinoGrande** | **Average** |
|-----------------------------------------------------------------------------|-----------|-----------|-----------|---------------|-----------|-----------|----------------|-------------|
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 26.45 | 45.08 | **53.98** | 46.71 | 69.75 | **84.70** | **53.91** | 54.37 |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **30.55** | **46.68** | 48.56 | **52.07** | **70.78** | 84.40 | 52.72 | **55.11** |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 27.56 | 48.06 | 55.78 | 53.97 | 72.31 | 87.20 | 58.01 | 57.56 |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **30.38** | **50.00** | **60.37** | **59.34** | **72.63** | **88.00** | **58.96** | **59.95** |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 32.34 | **55.43** | 63.58 | 64.81 | **75.57** | **90.60** | 61.72 | 63.44 |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **37.97** | 52.23 | **70.00** | **71.20** | 75.03 | 89.30 | **62.75** | **65.50** |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 35.58 | 59.89 | 67.40 | 72.44 | 78.24 | **92.70** | 65.51 | 67.39 |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **39.42** | **61.74** | **68.17** | **76.36** | **79.00** | 92.50 | **66.85** | **69.15** |
### LLM360
| **Model Size** | **ARC-c** | **HellaSwag** | **MMLU** | **TruthfulQA** | **WinoGrande** | **Average** |
|-----------------------------------------------------------------------------|-----------|---------------|-----------|----------------|----------------|-------------|
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 27.65 | 47.15 | 25.72 | **39.24** | **53.83** | 38.72 |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | **51.58** | **26.70** | 38.72 | 53.20 | **40.54** |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 30.20 | 53.86 | **26.01** | 40.18 | 57.22 | 41.50 |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | **59.31** | 25.41 | **40.48** | **58.33** | **43.41** |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 36.69 | 65.71 | **27.05** | 36.98 | 63.22 | 45.93 |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | **71.83** | 25.65 | **45.95** | **64.72** | **49.94** |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 42.24 | 73.28 | **26.76** | 34.98 | 67.25 | 48.90 |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **47.70** | **76.87** | 24.80 | **38.76** | **67.96** | **51.22** |
### OpenLLM Leaderboard
| **Model Size** | **ARC-c** | **CrowS-Pairs** | **HellaSwag** | **MMLU** | **PIQA** | **RACE** | **TruthfulQA** | **WinoGrande** | **Average** |
|-----------------------------------------------------------------------------|-----------|-----------------|---------------|-----------|-----------|-----------|----------------|----------------|-------------|
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 27.65 | **66.79** | 47.15 | 25.72 | 69.75 | 30.91 | **39.24** | **53.83** | 45.13 |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | 66.01 | **51.58** | **26.70** | **70.78** | 33.78 | 38.72 | 53.20 | **46.66** |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 30.20 | **68.63** | 53.86 | **26.01** | 72.31 | 33.11 | 40.18 | 57.22 | 47.69 |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | 67.44 | **59.31** | 25.41 | **72.63** | **36.84** | **40.48** | **58.33** | **49.25** |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 36.69 | **71.74** | 65.71 | **27.05** | **75.57** | 36.46 | 36.98 | 63.22 | 51.68 |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | 71.02 | **71.83** | 25.65 | 75.03 | **39.43** | **45.95** | **64.72** | **54.40** |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 42.24 | **73.29** | 73.28 | **26.76** | 78.24 | **38.76** | 34.98 | 67.25 | 54.35 |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **47.70** | 72.33 | **76.87** | 24.80 | **79.00** | 38.47 | **38.76** | **67.96** | **55.73** |
See the technical report for more results and comparison.
## Evaluation
### Setup
Install the following dependencies:
```bash
# install public lm-eval-harness
harness_repo="public-lm-eval-harness"
git clone https://github.com/EleutherAI/lm-evaluation-harness ${harness_repo}
cd ${harness_repo}
# use main branch on 03-15-2024, SHA is dc90fec
git checkout dc90fec
pip install -e .
cd ..
# 66d6242 is the main branch on 2024-04-01
pip install datasets@git+https://github.com/huggingface/datasets.git@66d6242
pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0
```
### Evaluate OpenELM
```bash
# OpenELM-270M
hf_model=apple/OpenELM-270M
# this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMA tokenizer which requires add_bos_token to be True
tokenizer=meta-llama/Llama-2-7b-hf
add_bos_token=True
batch_size=1
mkdir lm_eval_output
shot=0
task=arc_challenge,arc_easy,boolq,hellaswag,piqa,race,winogrande,sciq,truthfulqa_mc2
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=5
task=mmlu,winogrande
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=25
task=arc_challenge,crows_pairs_english
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=10
task=hellaswag
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
```
## Bias, Risks, and Limitations
The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements.
## Citation
If you find our work useful, please cite:
```BibTex
@article{mehtaOpenELMEfficientLanguage2024,
title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open} {Training} and {Inference} {Framework}},
shorttitle = {{OpenELM}},
url = {https://arxiv.org/abs/2404.14619v1},
language = {en},
urldate = {2024-04-24},
journal = {arXiv.org},
author = {Mehta, Sachin and Sekhavat, Mohammad Hossein and Cao, Qingqing and Horton, Maxwell and Jin, Yanzi and Sun, Chenfan and Mirzadeh, Iman and Najibi, Mahyar and Belenko, Dmitry and Zatloukal, Peter and Rastegari, Mohammad},
month = apr,
year = {2024},
}
@inproceedings{mehta2022cvnets,
author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad},
title = {CVNets: High Performance Library for Computer Vision},
year = {2022},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
series = {MM '22}
}
```
|
Yntec/Analog | Yntec | "2024-05-28T22:33:17Z" | 8,590 | 3 | diffusers | [
"diffusers",
"safetensors",
"Analog Photography",
"Vintage",
"General",
"Sexy",
"Girls",
"wavymulder",
"CornmeisterNL",
"SG161222",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-01-30T16:43:10Z" | ---
language:
- en
license: creativeml-openrail-m
tags:
- Analog Photography
- Vintage
- General
- Sexy
- Girls
- wavymulder
- CornmeisterNL
- SG161222
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Analog
A mix of Analog Diffusion, Paramount Beta (which includes the Analog Madness and Paragon models) and ParagonAlpha (as negative merge).
Samples and prompts:
(Click for larger)
Top left: analog style 70s color photograph of young Harrison Ford as Han Solo, star wars behind the scenes
Top right: analog 1986 movie screenshot Santa Claus with wife and daughter enjoying cake with candles. sitting with a pretty cute little girl, Gift Birthday Theme by Gil_Elvgren and Haddon_Sundblom
Bottom left: kodachrome camera transparency, dramatic lighting film grain, PARTY HARD BACKGROUND, pretty cute little girl smile in Zone 51, Extraterrestrial, Alien Space Ship Delivering Presents, Alien Space Ship Decorated With Garlands and Balls, Snowstorm
Bottom right: analog style 70s color a gaudy mansion interior
(Click for larger)
Top left: analog style 70s color movie still of beautiful face, young pretty Audrey Hepburn voluptuous at a neon convenience storefront
Top right: young husband with beautiful wife. 1989 movie screenshot. festive scene at a copper brewery with a wooden keg of beer in the center. sitting cute girl. Display mugs of dark beer. faces. accompanied Shirley by halloween ingredients
Bottom left: pizza
Bottom right: analog style 70s color portrait of Heath Ledger as a 1930s baseball player
(Click for larger)
Top left: analog style 70s color analog style portrait of a toucan
Top right: pretty lady with tall guy together standing, cute eyes, photoreal portrait, is on top of he Closeup a of rocks on pile top of a ocean moon to the magazine.
Bottom left: Full body picture of a pretty cute little girl making cake in school, detailed brown eyes, short smile, beautiful and aesthetic, intricate, neat hair, highly detailed, detailed face, smooth, sharp focus, chiaroscuro, magazine ad, 1949, 2D Game Art, anime on canvas, rossdraws, clay mann, CHIBI ART, light novel cover art
Bottom right: analog style 70s color best quality, masterpiece, sakura tree in a bottle, simple background<
(Click for larger)
Top left: cute little girl in analog tricycle, Screenshot of an surreal jean 70s round minimalist architecture, pretty eyes, Sharp light, 35mm still from a sci fi blockbuster color movie made in 2022, beautiful portrait, Dorothy, set in 1860, in front of a spaceship that has just landed on an alien planet, are all wearing, a robot stands nearby
Top right: absurdres, adorable cute harley quinn, at night, dark alley, moon, :) red ponytail, blonde ponytail, in matte black hardsuit, military, roughed up, bat, city fog,
Bottom left: analog style 70s color cozy comfy cabin interior
Bottom right: analog style 70s color cyberpunk portrait of a cute girl, beautiful
(Click for larger)
Top left: view from diagonally above, central adjustment, skinny young northern european female, long reddish blonde hair, real hair movement, elongated head, beautiful face, grey eyes, thin bowed eyebrows, snub nose, gentle lower jaw line, narrow chin, da vinci lips, slightly smiling with parted lips, curious friendly facial expression, small, slim narrow tapered hips
Top right: analog style 70s color cyberpunk portrait of a handsome young man
Bottom left: analog style 70s color at night a campsite under the stars
Bottom right: analog sitting Pretty Cute Girl, Detailed Eyes, with coins, beautiful detailed slot machine, gorgeous detailed hair, pants, Magazine ad, iconic, 1943, from the movie, sharp focus. visible brushstrokes by ROSSDRAWS
(Click for larger)
Top left: analog style closeup 70s color portrait of cowboy George Washington
Top right: analog style closeup 70s blonde Princess Peach wearing golden crown in the mushroom kingdom
Bottom left: analog style closeup 70s color portrait of Emma Stone, background futuristic neon at night storefront
Bottom right: analog style closeup 70s color portrait of beautiful young Zoe Saldana 1950s hollywood glamour
(Click for larger)
Top left: analog style closeup 70s color portrait of cosmonaut Johnny Cash
Top right: analog style analog style an amazing 70s color vista
Bottom left: analog style 70s color christmas lights night neighborhood decorated
Bottom right: analog style ponytail Lady Gaga as a 1970s color screenshot. Bikini
(Click for larger)
Top left: analog style 70s color portrait of a knight on Mars playing chess with her
Top right: analog style 50s color portrait of a little ponytail hipster wearing skirt
Bottom left: ANALOG COLOR close up pretty cute girl ballerina from the nutcracker dancing in a magical fantasy winter. ocean
Bottom right: analog style vintage THICK LINES cartoon CUTE blue panda by warner disney. STUNNING EYES
(Click for larger)
Top left: analog style 80s color a wes anderson art deco building
Top right: 90s TV screenshot of pretty cute little daughter as Marie Antoinette playing on toy guitar in bedroom. braids, detailed eyes. smile
Bottom left: Focused gaze, boxer stance, black gloves with teal accents, beautiful pretty cute girl smile with intense eyes, close-up, shallow depth of field, high contrast, cool color temperature, direct lighting, sharp focus on eyes, blurred foreground sparring glove, dynamic tension, determination, sweat-glistening skin, peek-through composition, anticipation atmosphere, gym setting suggested, personal struggle narrative, resilience symbolism
Bottom right: analog style 60s color epic explosion
Original pages:
https://huggingface.co/wavymulder/Analog-Diffusion/
https://huggingface.co/Yntec/Paramount (includes Paramount Alpha)
https://huggingface.co/SG161222/Paragon_V1.0
https://civitai.com/models/8030?modelVersionId=9519 (Analog Madness 1.1)
# Recipes:
- SuperMerger Add Difference Train Difference Alpha 1
Model A: Analog Diffusion
Model B: Paramount Alpha
Model C: Stable Diffusion 1.5
Output: AnalogParamount
- SuperMerger Weight sum Train Difference MBW 0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,1
Model A: Paramount Beta
Model B: Analog Diffusion
Model C: AnalogParamount
Output: Analog |
Skafu/swin-tiny-patch4-window7-224-cifar_100f_from_10 | Skafu | "2023-06-11T17:04:45Z" | 8,584 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:cifar100",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2023-06-11T15:46:39Z" | ---
tags:
- generated_from_trainer
datasets:
- cifar100
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-cifar_100f_from_10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar100
type: cifar100
config: cifar100
split: train
args: cifar100
metrics:
- name: Accuracy
type: accuracy
value: 0.5582
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-cifar_100f_from_10
This model was trained from scratch on the cifar100 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1818
- Accuracy: 0.5582
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4964 | 1.0 | 351 | 3.1548 | 0.3374 |
| 2.8648 | 2.0 | 703 | 2.3713 | 0.524 |
| 2.758 | 2.99 | 1053 | 2.1818 | 0.5582 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
duyntnet/spicyboros-7b-2.2-imatrix-GGUF | duyntnet | "2024-06-15T04:44:59Z" | 8,578 | 0 | transformers | [
"transformers",
"gguf",
"imatrix",
"spicyboros-7b-2.2",
"text-generation",
"en",
"license:other",
"region:us"
] | text-generation | "2024-06-15T02:39:27Z" | ---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- spicyboros-7b-2.2
---
Quantizations of https://huggingface.co/jondurbin/spicyboros-7b-2.2
# From original readme
### Prompt format
The prompt format:
```
A chat.
USER: {prompt}
ASSISTANT:
```
The default system prompt ("A chat.") was used for most of the prompts, however it also included a wide sampling of responses with other prompts, particularly in "stylized\_response", "rp", "gtkm", etc.
Here's another example:
```
A chat between Bob (aka USER) and Tom (aka ASSISTANT). Tom is an extremely intelligent 18th century bookkeeper, who speaks loquaciously.
USER: {prompt}
ASSISTANT:
```
And chat scenario that wouldn't require USER/ASSISTANT (but should use stopping criteria to prevent the model from speaking on your behalf).
```
A chat between old friends: Timmy and Tommy.
{description of characters}
{setting for the chat}
Timmy: *takes a big sip from his coffee* "Ah, sweet, delicious, magical coffee."
Tommy:
```
__*I strongly suggest adding stopping criteria/early inference stopping on "USER:", and/or whatever names you specify in the system prompt.*__ |
RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf | RichardErkhov | "2024-06-27T12:34:51Z" | 8,577 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-06-27T12:14:11Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Deita-2b - GGUF
- Model creator: https://huggingface.co/KnutJaegersberg/
- Original model: https://huggingface.co/KnutJaegersberg/Deita-2b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Deita-2b.Q2_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q2_K.gguf) | Q2_K | 1.21GB |
| [Deita-2b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.IQ3_XS.gguf) | IQ3_XS | 1.32GB |
| [Deita-2b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.IQ3_S.gguf) | IQ3_S | 1.38GB |
| [Deita-2b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q3_K_S.gguf) | Q3_K_S | 1.38GB |
| [Deita-2b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.IQ3_M.gguf) | IQ3_M | 1.44GB |
| [Deita-2b.Q3_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q3_K.gguf) | Q3_K | 1.49GB |
| [Deita-2b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q3_K_M.gguf) | Q3_K_M | 1.49GB |
| [Deita-2b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q3_K_L.gguf) | Q3_K_L | 1.57GB |
| [Deita-2b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.IQ4_XS.gguf) | IQ4_XS | 1.59GB |
| [Deita-2b.Q4_0.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q4_0.gguf) | Q4_0 | 1.65GB |
| [Deita-2b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.IQ4_NL.gguf) | IQ4_NL | 1.66GB |
| [Deita-2b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q4_K_S.gguf) | Q4_K_S | 1.71GB |
| [Deita-2b.Q4_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q4_K.gguf) | Q4_K | 1.83GB |
| [Deita-2b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q4_K_M.gguf) | Q4_K_M | 1.83GB |
| [Deita-2b.Q4_1.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q4_1.gguf) | Q4_1 | 1.81GB |
| [Deita-2b.Q5_0.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q5_0.gguf) | Q5_0 | 1.96GB |
| [Deita-2b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q5_K_S.gguf) | Q5_K_S | 1.99GB |
| [Deita-2b.Q5_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q5_K.gguf) | Q5_K | 2.09GB |
| [Deita-2b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q5_K_M.gguf) | Q5_K_M | 2.09GB |
| [Deita-2b.Q5_1.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q5_1.gguf) | Q5_1 | 2.12GB |
| [Deita-2b.Q6_K.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q6_K.gguf) | Q6_K | 2.42GB |
| [Deita-2b.Q8_0.gguf](https://huggingface.co/RichardErkhov/KnutJaegersberg_-_Deita-2b-gguf/blob/main/Deita-2b.Q8_0.gguf) | Q8_0 | 2.98GB |
Original model description:
---
license: other
license_name: general-model-license
license_link: https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md
model-index:
- name: Deita-2b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 44.71
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deita-2b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 70.39
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deita-2b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 52.79
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deita-2b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 39.61
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deita-2b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.27
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deita-2b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 41.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Deita-2b
name: Open LLM Leaderboard
---
Prompt Example:
```
### System:
You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
### User:
How do you fine tune a large language model?
### Assistant:
```
License Link:
https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-2b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |52.35|
|AI2 Reasoning Challenge (25-Shot)|44.71|
|HellaSwag (10-Shot) |70.39|
|MMLU (5-Shot) |52.79|
|TruthfulQA (0-shot) |39.61|
|Winogrande (5-shot) |65.27|
|GSM8k (5-shot) |41.32|
|
thoriqfy/indobert-emotion-classification | thoriqfy | "2023-06-20T04:22:43Z" | 8,573 | 5 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"indobert",
"indobenchmark",
"indonlu",
"id",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-05-22T08:26:32Z" | ---
language:
- id
tags:
- indobert
- indobenchmark
- indonlu
---
How to import:
```python
from transformers import BertForSequenceClassification, BertTokenizer, BertConfig
tokenizer = BertTokenizer.from_pretrained("thoriqfy/indobert-emotion-classification")
config = BertConfig.from_pretrained("thoriqfy/indobert-emotion-classification")
model = BertForSequenceClassification.from_pretrained("thoriqfy/indobert-emotion-classification", config=config)
```
How to use:
```python
from transformers import pipeline
nlp = pipeline("text-classification", model="thoriqfy/indobert-emotion-classification")
results = nlp("Your input text here")
``` |
facebook/dragon-plus-context-encoder | facebook | "2023-09-27T17:26:22Z" | 8,563 | 28 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"feature-extraction",
"arxiv:2302.07452",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2023-02-15T18:19:38Z" | ---
tags:
- feature-extraction
pipeline_tag: feature-extraction
---
DRAGON+ is a BERT-base sized dense retriever initialized from [RetroMAE](https://huggingface.co/Shitao/RetroMAE) and further trained on the data augmented from MS MARCO corpus, following the approach described in [How to Train Your DRAGON:
Diverse Augmentation Towards Generalizable Dense Retrieval](https://arxiv.org/abs/2302.07452).
<p align="center">
<img src="https://raw.githubusercontent.com/facebookresearch/dpr-scale/main/dragon/images/teaser.png" width="600">
</p>
The associated GitHub repository is available here https://github.com/facebookresearch/dpr-scale/tree/main/dragon. We use asymmetric dual encoder, with two distinctly parameterized encoders. The following models are also available:
Model | Initialization | MARCO Dev | BEIR | Query Encoder Path | Context Encoder Path
|---|---|---|---|---|---
DRAGON+ | Shitao/RetroMAE| 39.0 | 47.4 | [facebook/dragon-plus-query-encoder](https://huggingface.co/facebook/dragon-plus-query-encoder) | [facebook/dragon-plus-context-encoder](https://huggingface.co/facebook/dragon-plus-context-encoder)
DRAGON-RoBERTa | RoBERTa-base | 39.4 | 47.2 | [facebook/dragon-roberta-query-encoder](https://huggingface.co/facebook/dragon-roberta-query-encoder) | [facebook/dragon-roberta-context-encoder](https://huggingface.co/facebook/dragon-roberta-context-encoder)
## Usage (HuggingFace Transformers)
Using the model directly available in HuggingFace transformers .
```python
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('facebook/dragon-plus-query-encoder')
query_encoder = AutoModel.from_pretrained('facebook/dragon-plus-query-encoder')
context_encoder = AutoModel.from_pretrained('facebook/dragon-plus-context-encoder')
# We use msmarco query and passages as an example
query = "Where was Marie Curie born?"
contexts = [
"Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
"Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]
# Apply tokenizer
query_input = tokenizer(query, return_tensors='pt')
ctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')
# Compute embeddings: take the last-layer hidden state of the [CLS] token
query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :]
ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :]
# Compute similarity scores using dot product
score1 = query_emb @ ctx_emb[0] # 396.5625
score2 = query_emb @ ctx_emb[1] # 393.8340
``` |
bclavie/JaColBERT | bclavie | "2024-01-27T15:30:00Z" | 8,559 | 23 | RAGatouille | [
"RAGatouille",
"safetensors",
"bert",
"ColBERT",
"sentence-similarity",
"ja",
"dataset:bclavie/mmarco-japanese-hard-negatives",
"dataset:unicamp-dl/mmarco",
"arxiv:2312.16144",
"arxiv:2310.19349",
"base_model:cl-tohoku/bert-base-japanese-v3",
"license:mit",
"region:us"
] | sentence-similarity | "2023-12-25T22:43:54Z" | ---
inference: false
datasets:
- bclavie/mmarco-japanese-hard-negatives
- unicamp-dl/mmarco
language:
- ja
pipeline_tag: sentence-similarity
tags:
- ColBERT
base_model:
- cl-tohoku/bert-base-japanese-v3
license: mit
library_name: RAGatouille
---
このドキュメントの日本語版はまだ作成中です。申し訳ありません。
# Intro
> Detailed report in the [arXiv Report](https://arxiv.org/abs/2312.16144)
If you just want to check out how to use the model, please check out the [Usage section](#usage) below!
Welcome to JaColBERT version 1, the initial release of JaColBERT, a Japanese-only document retrieval model based on [ColBERT](https://github.com/stanford-futuredata/ColBERT).
It outperforms previous common Japanese models used for document retrieval, and gets close to the performance of multilingual models, despite the evaluation datasets being out-of-domain for our models but in-domain for multilingual approaches. This showcases the strong generalisation potential of ColBERT-based models, even applied to Japanese!
JaColBERT is only an initial release: it is trained on only 10 million triplets from a single dataset. This is a first version, hopefully demonstrating the strong potential of this approach.
The information on this model card is minimal and intends to give an overview. I've been asked before to make a citeable version, **please refer to the [Techical Report](https://ben.clavie.eu/JColBERT_v1.pdf)** for more information.
# Why use a ColBERT-like approach for your RAG application?
Most retrieval methods have strong tradeoffs:
* __Traditional sparse approaches__, such as BM25, are strong baselines, __but__ do not leverage any semantic understanding, and thus hit a hard ceiling.
* __Cross-encoder__ retriever methods are powerful, __but__ prohibitively expensive over large datasets: they must process the query against every single known document to be able to output scores.
* __Dense retrieval__ methods, using dense embeddings in vector databases, are lightweight and perform well, __but__ are __not__ data-efficient (they often require hundreds of millions if not billions of training examples pairs to reach state-of-the-art performance) and generalise poorly in a lot of cases. This makes sense: representing every single aspect of a document, to be able to match it to any potential query, into a single vector is an extremely hard problem.
ColBERT and its variants, including JaColBERT, aim to combine the best of all worlds: by representing the documents as essentially *bags-of-embeddings*, we obtain superior performance and strong out-of-domain generalisation at much lower compute cost than cross-encoders.
The strong out-of-domain performance can be seen in our results: JaColBERT, despite not having been trained on Mr.TyDi and MIRACL, nearly matches e5 dense retrievers, who have been trained on these datasets.
On JSQuAD, which is partially out-of-domain for e5 (it has only been exposed to the English version) and entirely out-of-domain for JaColBERT, it outperforms all e5 models.
Moreover, this approach requires **considerably less data than dense embeddings**: To reach its current performance, JaColBERT v1 is only trained on 10M training triplets, compared to billion of examples for the multilingual e5 models.
# Training
### Training Data
The model is trained on the japanese split of MMARCO, augmented with hard negatives. [The data, including the hard negatives, is available on huggingface datasets](https://huggingface.co/datasets/bclavie/mmarco-japanese-hard-negatives).
We do not train nor perform data augmentation on any other dataset at this stage. We hope to do so in future work, or support practitioners intending to do so (feel free to [reach out](mailto:[email protected])).
### Training Method
JColBERT is trained for a single epoch (1-pass over every triplet) on 8 NVidia L4 GPUs. Total training time was around 10 hours.
JColBERT is initiated from Tohoku University's excellent [bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3) and benefitted strongly from Nagoya University's work on building [strong Japanese SimCSE models](https://arxiv.org/abs/2310.19349), among other work.
We attempted to train JaColBERT with a variety of settings, including different batch sizes (8, 16, 32 per GPU) and learning rates (3e-6, 5e-6, 1e-5, 2e-5). The best results were obtained with 5e-6, though were very close when using 3e-6. Any higher learning rate consistently resulted in lower performance in early evaluations and was discarded. In all cases, we applied warmup steps equal to 10% of the total steps.
In-batch negative loss was applied, and we did not use any distillation methods (using the scores from an existing model).
# Results
See the table below for an overview of results, vs previous Japanese-only models and the current multilingual state-of-the-art (multilingual-e5).
Worth noting: JaColBERT is evaluated out-of-domain on all three datasets, whereas JSQuAD is partially (English version) and MIRACL & Mr.TyDi are fully in-domain for e5, likely contributing to their strong performance. In a real-world setting, I'm hopeful this could be bridged with moderate, quick (>2hrs) fine-tuning.
(refer to the technical report for exact evaluation method + code. * indicates the best monolingual/out-of-domain result. **bold** is best overall result. _italic_ indicates the task is in-domain for the model.)
| | JSQuAD | | | | MIRACL | | | | MrTyDi | | | | Average | | |
| ------------------------------------------------------------------------ | ----------------------- | -------------------- | ------ | ----------------------- | ----------------------- | -------------------- | ------ | ----------------------- | ----------------------- | -------------------- | ------ | ----------------------- | ----------------------- | -------------------- | ------ |
| | R@1 | R@5 | R@10 | | R@3 | R@5 | R@10 | | R@3 | R@5 | R@10 | | R@\{1\|3\} | R@5 | R@10 |
| JaColBERT | **0.906*** | **0.968*** | **0.978*** | | 0.464* | 0.546* | 0.645* | | 0.744* | 0.781* | 0.821* | | 0.705* | 0.765* | 0.813* |
| m-e5-large (in-domain) | 0.865 | 0.966 | 0.977 | | **0.522** | **0.600** | **0.697** | | **0.813** | **0.856** | **0.893** | | **0.730** | **0.807** | **0.856** |
| m-e5-base (in-domain) | *0.838* | *0.955* | 0.973 | | 0.482 | 0.553 | 0.632 | | 0.777 | 0.815 | 0.857 | | 0.699 | 0.775 | 0.820 |
| m-e5-small (in-domain) | *0.840* | *0.954* | 0.973 | | 0.464 | 0.540 | 0.640 | | 0.767 | 0.794 | 0.844 | | 0.690 | 0.763 | 0.819 |
| GLuCoSE | 0.645 | 0.846 | 0.897 | | 0.369 | 0.432 | 0.515 | | *0.617* | *0.670* | 0.735 | | 0.544 | 0.649 | 0.716 |
| sentence-bert-base-ja-v2 | 0.654 | 0.863 | 0.914 | | 0.172 | 0.224 | 0.338 | | 0.488 | 0.549 | 0.611 | | 0.435 | 0.545 | 0.621 |
| sup-simcse-ja-base | 0.632 | 0.849 | 0.897 | | 0.133 | 0.177 | 0.264 | | 0.454 | 0.514 | 0.580 | | 0.406 | 0.513 | 0.580 |
| sup-simcse-ja-large | 0.603 | 0.833 | 0.889 | | 0.159 | 0.212 | 0.295 | | 0.457 | 0.517 | 0.581 | | 0.406 | 0.521 | 0.588 |
| fio-base-v0.1 | 0.700 | 0.879 | 0.924 | | *0.279* | *0.358* | 0.462 | | *0.582* | *0.649* | 0.712 | | *0.520* | *0.629* | 0.699 |
# Usage
## Installation
JaColBERT works using ColBERT+RAGatouille. You can install it and all its necessary dependencies by running:
```sh
pip install -U ragatouille
```
For further examples on how to use RAGatouille with ColBERT models, you can check out the [`examples` section it the github repository](https://github.com/bclavie/RAGatouille/tree/main/examples).
Specifically, example 01 shows how to build/query an index, 04 shows how you can use JaColBERT as a re-ranker, and 06 shows how to use JaColBERT for in-memory searching rather than using an index.
Notably, RAGatouille has metadata support, so check the examples out if it's something you need!
## Encoding and querying documents without an index
If you want to use JaColBERT without building an index, it's very simple, you just need to load the model, `encode()` some documents, and then `search_encoded_documents()`:
```python
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("bclavie/JaColBERT")
RAG.encode(['document_1', 'document_2', ...])
RAG.search_encoded_documents(query="your search query")
```
Subsequent calls to `encode()` will add to the existing in-memory collection. If you want to empty it, simply run `RAG.clear_encoded_docs()`.
## Indexing
In order for the late-interaction retrieval approach used by ColBERT to work, you must first build your index.
Think of it like using an embedding model, like e5, to embed all your documents and storing them in a vector database.
Indexing is the slowest step retrieval is extremely quick. There are some tricks to speed it up, but the default settings work fairly well:
```python
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("bclavie/JaColBERT")
documents = [ "マクドナルドのフライドポテトの少量のカロリーはいくつですか？マクドナルドの小さなフライドポテトのカロリーマクドナルドのウェブサイトには、次のように記載されています。フライドポテトの小さな注文で230カロリーケチャップで25カロリー、ケチャップパケットで15カロリー。",]
RAG.index(name="My_first_index", collection=documents)
```
The index files are stored, by default, at `.ragatouille/colbert/indexes/{index_name}`.
And that's it! Let it run, and your index and all its representations (compressed to 2bits by default) will have been generated.
## Searching
Once you have created an index, searching through it is just as simple! If you're in the same session and `RAG` is still loaded, you can directly search the newly created index.
Otherwise, you'll want to load it from disk:
```python
RAG = RAGPretrainedModel.from_index(".ragatouille/colbert/indexes/My_first_index")
```
And then query it:
```python
RAG.search(query="What animation studio did Miyazaki found?")
> [{'content': 'In April 1984, Miyazaki opened his own office in Suginami Ward, naming it Nibariki.\n\n\n=== Studio Ghibli ===\n\n\n==== Early films (1985–1996) ====\nIn June 1985, Miyazaki, Takahata, Tokuma and Suzuki founded the animation production company Studio Ghibli, with funding from Tokuma Shoten. Studio Ghibli\'s first film, Laputa: Castle in the Sky (1986), employed the same production crew of Nausicaä. Miyazaki\'s designs for the film\'s setting were inspired by Greek architecture and "European urbanistic templates".',
'score': 25.90448570251465,
'rank': 1,
'document_id': 'miyazaki',
'document_metadata': {'entity': 'person', 'source': 'wikipedia'}},
{'content': 'Hayao Miyazaki (宮崎 駿 or 宮﨑 駿, Miyazaki Hayao, Japanese: [mijaꜜzaki hajao]; born January 5, 1941) is a Japanese animator, filmmaker, and manga artist. A co-founder of Studio Ghibli, he has attained international acclaim as a masterful storyteller and creator of Japanese animated feature films, and is widely regarded as one of the most accomplished filmmakers in the history of animation.\nBorn in Tokyo City in the Empire of Japan, Miyazaki expressed interest in manga and animation from an early age, and he joined Toei Animation in 1963. During his early years at Toei Animation he worked as an in-between artist and later collaborated with director Isao Takahata.',
'score': 25.572620391845703,
'rank': 2,
'document_id': 'miyazaki',
'document_metadata': {'entity': 'person', 'source': 'wikipedia'}},
[...]
]
```
# Citation
If you'd like to cite this work, please cite the technical report:
```
@misc{clavié2023jacolbert,
title={JaColBERT and Hard Negatives, Towards Better Japanese-First Embeddings for Retrieval: Early Technical Report},
author={Benjamin Clavié},
year={2023},
eprint={2312.16144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
QuantFactory/medicine-Llama3-8B-GGUF | QuantFactory | "2024-06-23T06:31:56Z" | 8,556 | 5 | null | [
"gguf",
"biology",
"medical",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"dataset:Open-Orca/OpenOrca",
"dataset:GAIR/lima",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"arxiv:2406.14491",
"arxiv:2309.09530",
"base_model:instruction-pretrain/medicine-Llama3-8B",
"license:llama3",
"region:us"
] | text-generation | "2024-06-22T17:58:38Z" | ---
datasets:
- EleutherAI/pile
- Open-Orca/OpenOrca
- GAIR/lima
- WizardLM/WizardLM_evol_instruct_V2_196k
language:
- en
license: llama3
tags:
- biology
- medical
pipeline_tag: text-generation
base_model: instruction-pretrain/medicine-Llama3-8B
---
# QuantFactory/medicine-Llama3-8B-GGUF
This is quantized version of [instruction-pretrain/medicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B) created using llama.cpp
# Model Description
## Instruction Pre-Training: Language Models are Supervised Multitask Learners
This repo contains the **biomedicine model developed from Llama3-8B** in our paper [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://huggingface.co/papers/2406.14491).
We explore supervised multitask pre-training by proposing ***Instruction Pre-Training***, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train language models. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. ***Instruction Pre-Training* outperforms *Vanilla Pre-training* in both general pre-training from scratch and domain-adaptive continual pre-training.** In pre-training from scratch, *Instruction Pre-Training* not only improves pre-trained base models but also benefits more from further instruction tuning. **In continual pre-training, *Instruction Pre-Training* enables Llama3-8B to be comparable to or even outperform Llama3-70B.**
<p align='center'>
<img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/vRdsFIVQptbNaGiZ18Lih.png" width="400">
</p>
## Resources
**🤗 We share our data and models with example usages, feel free to open any issues or discussions! 🤗**
- Context-Based Instruction Synthesizer: [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
- Fine-Tuning Data for the Synthesizer: [ft-instruction-synthesizer-collection](https://huggingface.co/datasets/instruction-pretrain/ft-instruction-synthesizer-collection)
- General Models Pre-Trained from Scratch:
- [InstructLM-500M](https://huggingface.co/instruction-pretrain/InstructLM-500M)
- [InstructLM-1.3B](https://huggingface.co/instruction-pretrain/InstructLM-1.3B)
- Domain-Specific Models Pre-Trained from Llama3-8B:
- [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B)
- [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B)
## Domain-Adaptive Continued Pre-Training
Following [AdaptLLM](https://huggingface.co/AdaptLLM/medicine-chat), we augment the domain-specific raw corpora with instruction-response pairs generated by our [context-based instruction synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer).
For example, to chat with the biomedicine-Llama3-8B model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("instruction-pretrain/medicine-Llama3-8B")
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/medicine-Llama3-8B")
# Put your input here, NO prompt template is required
user_input = '''Question: Which of the following is an example of monosomy?
Options:
- 46,XX
- 47,XXX
- 69,XYY
- 45,X
Please provide your choice first and then provide explanations if possible.'''
inputs = tokenizer(user_input, return_tensors="pt", add_special_tokens=True).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_new_tokens=400)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
print(pred)
```
## Model Citation
If you find our work helpful, please cite us:
[AdaptLLM](https://huggingface.co/papers/2309.09530)
```bibtex
@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}
``` |
QuantFactory/Llama-3-8B-OpenHermes-243K-GGUF | QuantFactory | "2024-06-20T17:52:03Z" | 8,555 | 0 | null | [
"gguf",
"axolotl",
"generated_from_trainer",
"text-generation",
"base_model:Magpie-Align/Llama-3-8B-OpenHermes-243K",
"license:llama3",
"region:us"
] | text-generation | "2024-06-20T13:00:41Z" | ---
license: llama3
base_model: Magpie-Align/Llama-3-8B-OpenHermes-243K
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Llama-3-8B-OpenHermes-243K
results: []
pipeline_tag: text-generation
---
# QuantFactory/Llama-3-8B-OpenHermes-243K-GGUF
This is quantized version of [Magpie-Align/Llama-3-8B-OpenHermes-243K](https://huggingface.co/Magpie-Align/Llama-3-8B-OpenHermes-243K) created using llama.cpp
# Model Description
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: mahiatlinux/OpenHermes-v1-ShareGPT
type: sharegpt
conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./out_Llama-8B-Openhermes-243K
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama-3-8B-OpenHermes-243K
wandb_log_model:
hub_model_id: SynDa/Llama-3-8B-OpenHermes-243K
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# Llama-3-8B-OpenHermes-243K
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9845
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.762 | 0.0033 | 1 | 3.9196 |
| 0.5197 | 0.3336 | 101 | 1.1073 |
| 0.4766 | 0.6672 | 202 | 1.0158 |
| 0.4366 | 1.0008 | 303 | 0.9940 |
| 0.3358 | 1.3216 | 404 | 0.9868 |
| 0.3299 | 1.6552 | 505 | 0.9845 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
unsloth/gemma-2b-bnb-4bit | unsloth | "2024-04-18T15:00:05Z" | 8,545 | 13 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"unsloth",
"gemma-2b",
"bnb",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-02-21T17:21:49Z" | ---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- unsloth
- transformers
- gemma
- gemma-2b
- bnb
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for Gemma 2b here: https://colab.research.google.com/drive/15gGm7x_jTm017_Ic8e317tdIpDG53Mtu?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
|
JCTN/JCTN_LORAxl | JCTN | "2024-06-14T22:38:05Z" | 8,544 | 2 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"concept",
"comedy",
"cereal box",
"cereal",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] | text-to-image | "2023-09-16T20:11:32Z" | ---
license: other
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- concept
- comedy
- cereal box
- cereal
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt:
widget:
- text: " boogers, free tissue inside"
- text: " star wars wookie bits, free lightsaber inside"
- text: " kitty litter crunch"
- text: " t bone steak"
- text: " black plague, free death inside"
- text: " barbie and ken"
- text: " boiled eggs"
- text: " raw bacon"
- text: " herpes"
- text: " pickles"
---
# Super Cereal - SDXL LoRA
> boogers, free tissue inside
<p>Multiplier of 0.9 - 1.1 works well on SDXL base. Simple prompts tend to work well. No trigger word needed. <br /><br />Special thanks to Huggingface for the GPU grant.</p>
## Image examples for the model:
> star wars wookie bits, free lightsaber inside
> kitty litter crunch
> t bone steak
> black plague, free death inside
> barbie and ken
> boiled eggs
> raw bacon
> herpes
> pickles
|
facebook/hf-seamless-m4t-medium | facebook | "2023-12-08T11:47:26Z" | 8,539 | 24 | transformers | [
"transformers",
"pytorch",
"seamless_m4t",
"feature-extraction",
"SeamlessM4T",
"text-to-speech",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-speech | "2023-08-28T16:30:57Z" | ---
inference: true
tags:
- SeamlessM4T
- seamless_m4t
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-to-speech
---
# SeamlessM4T Medium
SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different
linguistic communities to communicate effortlessly through speech and text.
This repository hosts 🤗 Hugging Face's [implementation](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t) of SeamlessM4T. You can find the original weights, as well as a guide on how to run them in the original hub repositories ([large](https://huggingface.co/facebook/seamless-m4t-large) and [medium](https://huggingface.co/facebook/seamless-m4t-medium) checkpoints).
-------------------
**🌟 SeamlessM4T v2, an improved version of this version with a novel architecture, has been released [here](https://huggingface.co/facebook/seamless-m4t-v2-large).
This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks.**
**SeamlessM4T v2 is also supported by 🤗 Transformers, more on it [in the model card of this new version](https://huggingface.co/facebook/seamless-m4t-v2-large#transformers-usage) or directly in [🤗 Transformers docs](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t_v2).**
-------------------
SeamlessM4T Medium covers:
- 📥 101 languages for speech input
- ⌨️ [196 Languages](https://huggingface.co/ylacombe/hf-seamless-m4t-medium/blob/main/tokenizer_config.json#L1887-L2089) for text input/output
- 🗣️ [35 languages](https://huggingface.co/ylacombe/hf-seamless-m4t-medium/blob/main/generation_config.json#L253-L288) for speech output.
This is the "medium" variant of the unified model, which enables multiple tasks without relying on multiple separate models:
- Speech-to-speech translation (S2ST)
- Speech-to-text translation (S2TT)
- Text-to-speech translation (T2ST)
- Text-to-text translation (T2TT)
- Automatic speech recognition (ASR)
You can perform all the above tasks from one single model, [`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel), but each task also has its own dedicated sub-model.
## 🤗 Usage
First, load the processor and a checkpoint of the model:
```python
>>> from transformers import AutoProcessor, SeamlessM4TModel
>>> processor = AutoProcessor.from_pretrained("facebook/hf-seamless-m4t-medium")
>>> model = SeamlessM4TModel.from_pretrained("facebook/hf-seamless-m4t-medium")
```
You can seamlessly use this model on text or on audio, to generated either translated text or translated audio.
Here is how to use the processor to process text and audio:
```python
>>> # let's load an audio sample from an Arabic speech corpus
>>> from datasets import load_dataset
>>> dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True)
>>> audio_sample = next(iter(dataset))["audio"]
>>> # now, process it
>>> audio_inputs = processor(audios=audio_sample["array"], return_tensors="pt")
>>> # now, process some English test as well
>>> text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")
```
### Speech
[`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) can *seamlessly* generate text or speech with few or no changes. Let's target Russian voice translation:
```python
>>> audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
>>> audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
```
With basically the same code, I've translated English text and Arabic speech to Russian speech samples.
### Text
Similarly, you can generate translated text from audio files or from text with the same model. You only have to pass `generate_speech=False` to [`SeamlessM4TModel.generate`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel.generate).
This time, let's translate to French.
```python
>>> # from audio
>>> output_tokens = model.generate(**audio_inputs, tgt_lang="fra", generate_speech=False)
>>> translated_text_from_audio = processor.decode(output_tokens[0].tolist(), skip_special_tokens=True)
>>> # from text
>>> output_tokens = model.generate(**text_inputs, tgt_lang="fra", generate_speech=False)
>>> translated_text_from_text = processor.decode(output_tokens[0].tolist(), skip_special_tokens=True)
```
### Tips
#### 1. Use dedicated models
[`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) is transformers top level model to generate speech and text, but you can also use dedicated models that perform the task without additional components, thus reducing the memory footprint.
For example, you can replace the audio-to-audio generation snippet with the model dedicated to the S2ST task, the rest is exactly the same code:
```python
>>> from transformers import SeamlessM4TForSpeechToSpeech
>>> model = SeamlessM4TForSpeechToSpeech.from_pretrained("facebook/hf-seamless-m4t-medium")
```
Or you can replace the text-to-text generation snippet with the model dedicated to the T2TT task, you only have to remove `generate_speech=False`.
```python
>>> from transformers import SeamlessM4TForTextToText
>>> model = SeamlessM4TForTextToText.from_pretrained("facebook/hf-seamless-m4t-medium")
```
Feel free to try out [`SeamlessM4TForSpeechToText`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TForSpeechToText) and [`SeamlessM4TForTextToSpeech`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TForTextToSpeech) as well.
#### 2. Change the speaker identity
You have the possibility to change the speaker used for speech synthesis with the `spkr_id` argument. Some `spkr_id` works better than other for some languages!
#### 3. Change the generation strategy
You can use different [generation strategies](https://huggingface.co/docs/transformers/v4.34.1/en/generation_strategies#text-generation-strategies) for speech and text generation, e.g `.generate(input_ids=input_ids, text_num_beams=4, speech_do_sample=True)` which will successively perform beam-search decoding on the text model, and multinomial sampling on the speech model.
#### 4. Generate speech and text at the same time
Use `return_intermediate_token_ids=True` with [`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) to return both speech and text ! |
DATEXIS/CORe-clinical-diagnosis-prediction | DATEXIS | "2022-02-17T09:36:23Z" | 8,537 | 21 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"medical",
"clinical",
"diagnosis",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-03-02T23:29:05Z" | ---
language: "en"
tags:
- bert
- medical
- clinical
- diagnosis
- text-classification
thumbnail: "https://core.app.datexis.com/static/paper.png"
widget:
- text: "Patient with hypertension presents to ICU."
---
# CORe Model - Clinical Diagnosis Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
This model checkpoint is **fine-tuned on the task of diagnosis prediction**.
The model expects patient admission notes as input and outputs multi-label ICD9-code predictions.
#### Model Predictions
The model makes predictions on a total of 9237 labels. These contain 3- and 4-digit ICD9 codes and textual descriptions of these codes. The 4-digit codes and textual descriptions help to incorporate further topical and hierarchical information into the model during training (see Section 4.2 _ICD+: Incorporation of ICD Hierarchy_ in our paper). We recommend to only use the **3-digit code predictions at inference time**, because only those have been evaluated in our work.
#### How to use CORe Diagnosis Prediction
You can load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-diagnosis-prediction")
model = AutoModelForSequenceClassification.from_pretrained("bvanaken/CORe-clinical-diagnosis-prediction")
```
The following code shows an inference example:
```
input = "CHIEF COMPLAINT: Headaches\n\nPRESENT ILLNESS: 58yo man w/ hx of hypertension, AFib on coumadin presented to ED with the worst headache of his life."
tokenized_input = tokenizer(input, return_tensors="pt")
output = model(**tokenized_input)
import torch
predictions = torch.sigmoid(output.logits)
predicted_labels = [model.config.id2label[_id] for _id in (predictions > 0.3).nonzero()[:, 1].tolist()]
```
Note: For the best performance, we recommend to determine the thresholds (0.3 in this example) individually per label.
### More Information
For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).
### Cite
```bibtex
@inproceedings{vanaken21,
author = {Betty van Aken and
Jens-Michalis Papaioannou and
Manuel Mayrdorfer and
Klemens Budde and
Felix A. Gers and
Alexander Löser},
title = {Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the
Association for Computational Linguistics: Main Volume, {EACL} 2021,
Online, April 19 - 23, 2021},
publisher = {Association for Computational Linguistics},
year = {2021},
}
``` |
hfl/chinese-llama-2-7b | hfl | "2023-12-23T07:20:49Z" | 8,537 | 96 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-07-27T06:54:32Z" | ---
license: apache-2.0
language:
- zh
- en
---
# Chinese-LLaMA-2-7B
**This is the full Chinese-LLaMA-2-7B model，which can be loaded directly for inference and full-parameter training.**
**Related models👇**
* Long context base models
* [Chinese-LLaMA-2-7B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-7b-16k)
* [Chinese-LLaMA-2-LoRA-7B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b-16k)
* [Chinese-LLaMA-2-13B-16K (full model)](https://huggingface.co/hfl/chinese-llama-2-13b-16k)
* [Chinese-LLaMA-2-LoRA-13B-16K (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b-16k)
* Base models
* [Chinese-LLaMA-2-7B (full model)](https://huggingface.co/hfl/chinese-llama-2-7b)
* [Chinese-LLaMA-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-7b)
* [Chinese-LLaMA-2-13B (full model)](https://huggingface.co/hfl/chinese-llama-2-13b)
* [Chinese-LLaMA-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-llama-2-lora-13b)
* Instruction/Chat models
* [Chinese-Alpaca-2-7B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-7b)
* [Chinese-Alpaca-2-LoRA-7B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-7b)
* [Chinese-Alpaca-2-13B (full model)](https://huggingface.co/hfl/chinese-alpaca-2-13b)
* [Chinese-Alpaca-2-LoRA-13B (LoRA model)](https://huggingface.co/hfl/chinese-alpaca-2-lora-13b)
# Description of Chinese-LLaMA-Alpaca-2
This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.
The main contents of this project include:
* 🚀 New extended Chinese vocabulary beyond Llama-2, open-sourcing the Chinese LLaMA-2 and Alpaca-2 LLMs.
* 🚀 Open-sourced the pre-training and instruction finetuning (SFT) scripts for further tuning on user's data
* 🚀 Quickly deploy and experience the quantized LLMs on CPU/GPU of personal PC
* 🚀 Support for LLaMA ecosystems like 🤗transformers, llama.cpp, text-generation-webui, LangChain, vLLM etc.
Please refer to [https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/) for details. |
Helsinki-NLP/opus-mt-en-vi | Helsinki-NLP | "2023-08-16T11:31:40Z" | 8,536 | 9 | transformers | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"en",
"vi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2022-03-02T23:29:04Z" | ---
language:
- en
- vi
tags:
- translation
license: apache-2.0
---
### eng-vie
* source group: English
* target group: Vietnamese
* OPUS readme: [eng-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-vie/README.md)
* model: transformer-align
* source language(s): eng
* target language(s): vie vie_Hani
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-vie/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-vie/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-vie/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.eng.vie | 37.2 | 0.542 |
### System Info:
- hf_name: eng-vie
- source_languages: eng
- target_languages: vie
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-vie/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['en', 'vi']
- src_constituents: {'eng'}
- tgt_constituents: {'vie', 'vie_Hani'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-vie/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-vie/opus-2020-06-17.test.txt
- src_alpha3: eng
- tgt_alpha3: vie
- short_pair: en-vi
- chrF2_score: 0.542
- bleu: 37.2
- brevity_penalty: 0.973
- ref_len: 24427.0
- src_name: English
- tgt_name: Vietnamese
- train_date: 2020-06-17
- src_alpha2: en
- tgt_alpha2: vi
- prefer_old: False
- long_pair: eng-vie
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 |
QuantFactory/st-vicuna-v1.3-10.5b-ppl-GGUF | QuantFactory | "2024-06-20T17:40:13Z" | 8,517 | 1 | null | [
"gguf",
"llama",
"text-generation",
"arxiv:2402.02834",
"base_model:nota-ai/st-vicuna-v1.3-10.5b-ppl",
"region:us"
] | text-generation | "2024-06-20T06:40:44Z" | ---
pipeline_tag: text-generation
tags:
- llama
base_model: nota-ai/st-vicuna-v1.3-10.5b-ppl
---
# QuantFactory/st-vicuna-v1.3-10.5b-ppl-GGUF
This is quantized version of [nota-ai/st-vicuna-v1.3-10.5b-ppl](https://huggingface.co/nota-ai/st-vicuna-v1.3-10.5b-ppl) created using llama.cpp
# Model Description
### Shortened LLaMA Model Card
Shortened LLaMA is a depth-pruned version of LLaMA models & variants for efficient text generation.
- **Developed by:** [Nota AI](https://www.nota.ai/)
- **License:** Non-commercial license
- **Repository:** https://github.com/Nota-NetsPresso/shortened-llm
- **Paper:** https://arxiv.org/abs/2402.02834
## Compression Method
After identifying unimportant Transformer blocks, we perform one-shot pruning and light LoRA-based retraining.
<details>
<summary>
Click to see a method figure.
</summary>
<img alt="method" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st-llama_method.png" width="100%">
</details>
## Model Links
| Source<br>Model | Pruning<br>Ratio | Pruning<br>Criterion | HF Models<br>Link |
|:---:|:---:|:---:|:---:|
| LLaMA-1-7B | 20% | PPL | [nota-ai/st-llama-1-5.5b-ppl](https://huggingface.co/nota-ai/st-llama-1-5.5b-ppl) |
| LLaMA-1-7B | 20% | Taylor+ | [nota-ai/st-llama-1-5.5b-taylor](https://huggingface.co/nota-ai/st-llama-1-5.5b-taylor) |
| Vicuna-v1.3-7B | 20% | PPL | [nota-ai/st-vicuna-v1.3-5.5b-ppl](https://huggingface.co/nota-ai/st-vicuna-v1.3-5.5b-ppl) |
| Vicuna-v1.3-7B | 20% | Taylor+ | [nota-ai/st-vicuna-v1.3-5.5b-taylor](https://huggingface.co/nota-ai/st-vicuna-v1.3-5.5b-taylor) |
| Vicuna-v1.3-13B | 21% | PPL | [nota-ai/st-vicuna-v1.3-10.5b-ppl](https://huggingface.co/nota-ai/st-vicuna-v1.3-10.5b-ppl) |
| Vicuna-v1.3-13B | 21% | Taylor+ | [nota-ai/st-vicuna-v1.3-10.5b-taylor](https://huggingface.co/nota-ai/st-vicuna-v1.3-10.5b-taylor) |
## Zero-shot Performance & Efficiency Results
- EleutherAI/lm-evaluation-harness version [3326c54](https://github.com/EleutherAI/lm-evaluation-harness/tree/3326c547a733d598b4377e54be96e194861b964c)
<img alt="results" img src="https://netspresso-research-code-release.s3.us-east-2.amazonaws.com/compressed-llm/st-llama_zero-shot_scores.png" width="100%">
## License
- All rights related to this repository and the compressed models are reserved by Nota Inc.
- The intended use is strictly limited to research and non-commercial projects.
## Acknowledgments
- [LLM-Pruner](https://github.com/horseee/LLM-Pruner), which utilizes [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness), [PEFT](https://github.com/huggingface/peft), and [Alpaca-LoRA](https://github.com/tloen/alpaca-lora). Thanks for the pioneering work on structured pruning of LLMs!
- Meta AI's [LLaMA](https://github.com/facebookresearch/llama) and LMSYS Org's [Vicuna](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md). Thanks for the open-source LLMs!
## Original Model Citation
```bibtex
@article{kim2024shortened,
title={Shortened LLaMA: A Simple Depth Pruning for Large Language Models},
author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu},
journal={arXiv preprint arXiv:2402.02834},
year={2024},
url={https://arxiv.org/abs/2402.02834}
}
```
```bibtex
@article{kim2024mefomo,
title={Shortened LLaMA: A Simple Depth Pruning for Large Language Models},
author={Kim, Bo-Kyeong and Kim, Geonmin and Kim, Tae-Ho and Castells, Thibault and Choi, Shinkook and Shin, Junho and Song, Hyoung-Kyu},
journal={ICLR Workshop on Mathematical and Empirical Understanding of Foundation Models (ME-FoMo)},
year={2024},
url={https://openreview.net/forum?id=18VGxuOdpu}
}
``` |
zhihan1996/DNA_bert_5 | zhihan1996 | "2023-10-30T19:26:35Z" | 8,514 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | Entry not found |
funnel-transformer/small | funnel-transformer | "2024-02-29T09:00:06Z" | 8,505 | 5 | transformers | [
"transformers",
"pytorch",
"tf",
"funnel",
"feature-extraction",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"dataset:gigaword",
"arxiv:2006.03236",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2022-03-02T23:29:05Z" | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
- gigaword
---
# Funnel Transformer small model (B4-4-4 with decoder)
Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in
[this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in
[this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been
written by the Hugging Face team.
## Model description
Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and
the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the BERT model as inputs.
## Intended uses & limitations
You can use the raw model to extract a vector representation of a given text, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import FunnelTokenizer, FunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small")
model = FunneModel.from_pretrained("funnel-transformer/small")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import FunnelTokenizer, TFFunnelModel
tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small")
model = TFFunnelModel.from_pretrained("funnel-transformer/small")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The BERT model was pretrained on:
- [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books,
- [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers),
- [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages,
- [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data,
- [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages.
### BibTeX entry and citation info
```bibtex
@misc{dai2020funneltransformer,
title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing},
author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le},
year={2020},
eprint={2006.03236},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
google/long-t5-local-base | google | "2023-09-07T15:36:37Z" | 8,502 | 14 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2022-04-14T16:53:51Z" | ---
license: apache-2.0
language: en
---
# LongT5 (local attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-research/longt5). All the model architecture and configuration can be found in [Flaxformer repository](https://github.com/google/flaxformer) which uses another Google research project repository [T5x](https://github.com/google-research/t5x).
Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting ([Pegasus-like generation pre-training](https://arxiv.org/pdf/1912.08777.pdf)). LongT5 model is an extension of [T5 model](https://arxiv.org/pdf/1910.10683.pdf), and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence.
LongT5 is particularly effective when fine-tuned for text generation (summarization, question answering) which requires handling long input sequences (up to 16,384 tokens).
## Intended uses & limitations
The model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=longt5) to look for fine-tuned versions on a task that interests you.
### How to use
```python
from transformers import AutoTokenizer, LongT5Model
tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
model = LongT5Model.from_pretrained("google/long-t5-local-base")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
```bibtex
@article{guo2021longt5,
title={LongT5: Efficient Text-To-Text Transformer for Long Sequences},
author={Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei},
journal={arXiv preprint arXiv:2112.07916},
year={2021}
}
``` |
timm/eca_nfnet_l0.ra2_in1k | timm | "2024-02-10T23:36:05Z" | 8,497 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2102.06171",
"arxiv:2101.08692",
"license:apache-2.0",
"region:us"
] | image-classification | "2023-03-24T01:12:29Z" | ---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
---
# Model card for eca_nfnet_l0.ra2_in1k
A ECA-NFNet-Lite (Lightweight NFNet w/ ECA attention) image classification model. Trained in `timm` by Ross Wightman.
Normalization Free Networks are (pre-activation) ResNet-like models without any normalization layers. Instead of Batch Normalization or alternatives, they use Scaled Weight Standardization and specifically placed scalar gains in residual path and at non-linearities based on signal propagation analysis.
Lightweight NFNets are `timm` specific variants that reduce the SE and bottleneck ratio from 0.5 -> 0.25 (reducing widths) and use a smaller group size while maintaining the same depth. SiLU activations used instead of GELU.
This NFNet variant also uses ECA (Efficient Channel Attention) instead of SE (Squeeze-and-Excitation).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 24.1
- GMACs: 4.4
- Activations (M): 10.5
- Image size: train = 224 x 224, test = 288 x 288
- **Papers:**
- High-Performance Large-Scale Image Recognition Without Normalization: https://arxiv.org/abs/2102.06171
- Characterizing signal propagation to close the performance gap in unnormalized ResNets: https://arxiv.org/abs/2101.08692
- **Original:** https://github.com/huggingface/pytorch-image-models
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('eca_nfnet_l0.ra2_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'eca_nfnet_l0.ra2_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 64, 112, 112])
# torch.Size([1, 256, 56, 56])
# torch.Size([1, 512, 28, 28])
# torch.Size([1, 1536, 14, 14])
# torch.Size([1, 2304, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'eca_nfnet_l0.ra2_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 2304, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:2102.06171},
year={2021}
}
```
```bibtex
@inproceedings{brock2021characterizing,
author={Andrew Brock and Soham De and Samuel L. Smith},
title={Characterizing signal propagation to close the performance gap in
unnormalized ResNets},
booktitle={9th International Conference on Learning Representations, {ICLR}},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
QuantFactory/Llama-3-8B-WildChat-GGUF | QuantFactory | "2024-06-20T17:45:29Z" | 8,497 | 1 | null | [
"gguf",
"axolotl",
"generated_from_trainer",
"text-generation",
"base_model:Magpie-Align/Llama-3-8B-WildChat",
"license:llama3",
"region:us"
] | text-generation | "2024-06-20T06:50:10Z" | ---
license: llama3
base_model: Magpie-Align/Llama-3-8B-WildChat
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Llama-3-8B-WildChat
results: []
pipeline_tag: text-generation
---
# QuantFactory/Llama-3-8B-WildChat-GGUF
This is quantized version of [Magpie-Align/Llama-3-8B-WildChat](https://huggingface.co/Magpie-Align/Llama-3-8B-WildChat) created using llama.cpp
# Model Description
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: flydust/WildChat_ShareGPT
type: sharegpt
conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./out_Llama-3-WildChat
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama-3-WildChat
wandb_log_model:
hub_model_id: SynDa/Llama-3-8B-WildChat
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# Llama-3-8B-WildChat
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8197
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1455 | 0.0003 | 1 | 1.3389 |
| 0.9084 | 0.3333 | 1128 | 0.8551 |
| 0.9265 | 0.6667 | 2256 | 0.8363 |
| 0.9086 | 1.0 | 3384 | 0.8210 |
| 0.8257 | 1.3164 | 4512 | 0.8214 |
| 0.8306 | 1.6497 | 5640 | 0.8197 |
| 0.8252 | 1.9831 | 6768 | 0.8197 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
KoichiYasuoka/bert-base-japanese-upos | KoichiYasuoka | "2022-09-18T10:43:26Z" | 8,490 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"japanese",
"pos",
"wikipedia",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2022-03-02T23:29:04Z" | ---
language:
- "ja"
tags:
- "japanese"
- "token-classification"
- "pos"
- "wikipedia"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "国境の長いトンネルを抜けると雪国であった。"
---
# bert-base-japanese-upos
## Model Description
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-base-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-base-japanese-char-extended). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
## How to Use
```py
import torch
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-japanese-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-japanese-upos")
s="国境の長いトンネルを抜けると雪国であった。"
p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]]
print(list(zip(s,p)))
```
or
```py
import esupar
nlp=esupar.load("KoichiYasuoka/bert-base-japanese-upos")
print(nlp("国境の長いトンネルを抜けると雪国であった。"))
```
## See Also
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
|
mmnga/stockmark-gpt-neox-japanese-1.4b-gguf | mmnga | "2023-09-08T22:00:37Z" | 8,489 | 1 | null | [
"gguf",
"gpt-neox",
"ja",
"license:mit",
"region:us"
] | null | "2023-08-22T12:45:18Z" | ---
license: mit
language:
- ja
tags:
- gpt-neox
---
# stockmark-gpt-neox-japanese-1.4b-gguf
[stockmarkさんが公開しているgpt-neox-japanese-1.4b](https://huggingface.co/stockmark/gpt-neox-japanese-1.4b)のggufフォーマット変換版です。
注意:こちらはブランチで試用になります。llama.cpp本家にgptneoxが実装された時に、このggufファイルが使用できない可能性があります。
***[GitHubリポジトリの readme はこちら](https://github.com/mmnga/llama.cpp/tree/mmnga-dev)***
## Usage (試用)
```
git clone --branch mmnga-dev https://github.com/mmnga/llama.cpp.git
cd llama.cpp
make -j
./main -m 'stockmark-gpt-neox-japanese-1.4b-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' --top_p 0.9 --temp 0.7 --repeat-penalty 1.1
```
**CUBLAS**
```
LLAMA_CUBLAS=1 make -j
./main -m 'stockmark-gpt-neox-japanese-1.4b-q4_0.gguf' -n 128 -p '吾輩は猫である。名前は実を言うと、' -ngl 24
```
|
timm/mobilenetv3_small_075.lamb_in1k | timm | "2023-04-27T22:49:32Z" | 8,488 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2110.00476",
"arxiv:1905.02244",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-12-16T05:38:29Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for mobilenetv3_small_075.lamb_in1k
A MobileNet-v3 image classification model. Trained on ImageNet-1k in `timm` using recipe template described below.
Recipe details:
* A LAMB optimizer recipe that is similar to [ResNet Strikes Back](https://arxiv.org/abs/2110.00476) `A2` but 50% longer with EMA weight averaging, no CutMix
* RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging
* Step (exponential decay w/ staircase) LR schedule with warmup
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 2.0
- GMACs: 0.0
- Activations (M): 1.3
- Image size: 224 x 224
- **Papers:**
- Searching for MobileNetV3: https://arxiv.org/abs/1905.02244
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('mobilenetv3_small_075.lamb_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'mobilenetv3_small_075.lamb_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 16, 112, 112])
# torch.Size([1, 16, 56, 56])
# torch.Size([1, 24, 28, 28])
# torch.Size([1, 40, 14, 14])
# torch.Size([1, 432, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'mobilenetv3_small_075.lamb_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 432, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@inproceedings{howard2019searching,
title={Searching for mobilenetv3},
author={Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and others},
booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
pages={1314--1324},
year={2019}
}
```
|
markhneedham/dolphin-2.9-llama3-8b-Q4_K_M-GGUF | markhneedham | "2024-06-28T18:27:08Z" | 8,482 | 0 | null | [
"gguf",
"generated_from_trainer",
"axolotl",
"llama-cpp",
"gguf-my-repo",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b",
"license:other",
"region:us"
] | null | "2024-06-27T06:24:55Z" | ---
base_model: cognitivecomputations/dolphin-2.9-llama3-8b
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- HuggingFaceH4/ultrachat_200k
- microsoft/orca-math-word-problems-200k
- abacusai/SystemChat-1.1
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
license: other
tags:
- generated_from_trainer
- axolotl
- llama-cpp
- gguf-my-repo
model-index:
- name: out
results: []
---
# markhneedham/dolphin-2.9-llama3-8b-Q4_K_M-GGUF
This model was converted to GGUF format from [`cognitivecomputations/dolphin-2.9-llama3-8b`](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo markhneedham/dolphin-2.9-llama3-8b-Q4_K_M-GGUF --hf-file dolphin-2.9-llama3-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo markhneedham/dolphin-2.9-llama3-8b-Q4_K_M-GGUF --hf-file dolphin-2.9-llama3-8b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo markhneedham/dolphin-2.9-llama3-8b-Q4_K_M-GGUF --hf-file dolphin-2.9-llama3-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo markhneedham/dolphin-2.9-llama3-8b-Q4_K_M-GGUF --hf-file dolphin-2.9-llama3-8b-q4_k_m.gguf -c 2048
```
|
MazenAmria/swin-base-finetuned-cifar100 | MazenAmria | "2022-12-23T18:03:14Z" | 8,477 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:cifar100",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2022-12-23T14:51:20Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar100
metrics:
- accuracy
model-index:
- name: swin-base-finetuned-cifar100
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: cifar100
type: cifar100
config: cifar100
split: train
args: cifar100
metrics:
- name: Accuracy
type: accuracy
value: 0.9201
---
<!-- 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. -->
# swin-base-finetuned-cifar100
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the cifar100 dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9201
- Loss: 0.3670
## 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: 4e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.3536 | 1.0 | 781 | 0.9052 | 0.3141 |
| 0.3254 | 2.0 | 1562 | 0.9117 | 0.2991 |
| 0.0936 | 3.0 | 2343 | 0.9138 | 0.3322 |
| 0.1054 | 4.0 | 3124 | 0.9158 | 0.3483 |
| 0.0269 | 5.0 | 3905 | 0.9201 | 0.3670 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
ZeroWw/llama3-8B-DarkIdol-2.2-Uncensored-1048K-GGUF | ZeroWw | "2024-07-01T16:53:19Z" | 8,477 | 3 | null | [
"gguf",
"en",
"license:mit",
"region:us"
] | null | "2024-07-01T16:39:48Z" |
---
license: mit
language:
- en
---
My own (ZeroWw) quantizations.
output and embed tensors quantized to f16.
all other tensors quantized to q5_k or q6_k.
Result:
both f16.q6 and f16.q5 are smaller than q8_0 standard quantization
and they perform as well as the pure f16.
|
deepset/roberta-large-squad2 | deepset | "2023-09-27T12:27:59Z" | 8,468 | 24 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"question-answering",
"en",
"dataset:squad_v2",
"base_model:roberta-large",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | question-answering | "2022-03-02T23:29:05Z" | ---
language: en
license: cc-by-4.0
datasets:
- squad_v2
base_model: roberta-large
model-index:
- name: deepset/roberta-large-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- type: exact_match
value: 85.168
name: Exact Match
- type: f1
value: 88.349
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
- type: exact_match
value: 87.162
name: Exact Match
- type: f1
value: 93.603
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: adversarial_qa
type: adversarial_qa
config: adversarialQA
split: validation
metrics:
- type: exact_match
value: 35.900
name: Exact Match
- type: f1
value: 48.923
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_adversarial
type: squad_adversarial
config: AddOneSent
split: validation
metrics:
- type: exact_match
value: 81.142
name: Exact Match
- type: f1
value: 87.099
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts amazon
type: squadshifts
config: amazon
split: test
metrics:
- type: exact_match
value: 72.453
name: Exact Match
- type: f1
value: 86.325
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts new_wiki
type: squadshifts
config: new_wiki
split: test
metrics:
- type: exact_match
value: 82.338
name: Exact Match
- type: f1
value: 91.974
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts nyt
type: squadshifts
config: nyt
split: test
metrics:
- type: exact_match
value: 84.352
name: Exact Match
- type: f1
value: 92.645
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squadshifts reddit
type: squadshifts
config: reddit
split: test
metrics:
- type: exact_match
value: 74.722
name: Exact Match
- type: f1
value: 86.860
name: F1
---
# roberta-large for QA
This is the [roberta-large](https://huggingface.co/roberta-large) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering.
## Overview
**Language model:** roberta-large
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)
**Infrastructure**: 4x Tesla v100
## Hyperparameters
```
base_LM_model = "roberta-large"
```
## Using a distilled model instead
Please note that we have also released a distilled version of this model called [deepset/roberta-base-squad2-distilled](https://huggingface.co/deepset/roberta-base-squad2-distilled). The distilled model has a comparable prediction quality and runs at twice the speed of the large model.
## Usage
### In Haystack
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="deepset/roberta-large-squad2")
# or
reader = TransformersReader(model_name_or_path="deepset/roberta-large-squad2",tokenizer="deepset/roberta-large-squad2")
```
For a complete example of ``roberta-large-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/roberta-large-squad2"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Why is model conversion important?',
'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
}
res = nlp(QA_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
**Branden Chan:** [email protected]
**Timo Möller:** [email protected]
**Malte Pietsch:** [email protected]
**Tanay Soni:** [email protected]
## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/>
</div>
<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/>
</div>
</div>
[deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
Some of our other work:
- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
## Get in touch and join the Haystack community
<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>.
We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p>
[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
By the way: [we're hiring!](http://www.deepset.ai/jobs) |
Qwen/Qwen-72B | Qwen | "2024-01-09T05:11:33Z" | 8,456 | 342 | transformers | [
"transformers",
"safetensors",
"qwen",
"text-generation",
"custom_code",
"zh",
"en",
"arxiv:2309.16609",
"license:other",
"autotrain_compatible",
"region:us"
] | text-generation | "2023-11-26T16:16:31Z" | ---
language:
- zh
- en
tags:
- qwen
pipeline_tag: text-generation
inference: false
license: other
license_name: tongyi-qianwen-license-agreement
license_link: https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT
---
# Qwen-72B
<p align="center">
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/logo_qwen.jpg" width="400"/>
<p>
<br>
<p align="center">
🤗 <a href="https://huggingface.co/Qwen">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/qwen">ModelScope</a>   |    📑 <a href="https://arxiv.org/abs/2309.16609">Paper</a>    ｜   🖥️ <a href="https://modelscope.cn/studios/qwen/Qwen-72B-Chat-Demo/summary">Demo</a>
<br>
<a href="https://github.com/QwenLM/Qwen/blob/main/assets/wechat.png">WeChat (微信)</a>   |   <a href="https://discord.gg/z3GAxXZ9Ce">Discord</a>   ｜   <a href="https://dashscope.aliyun.com">API</a>
</p>
<br>
## 介绍 (Introduction)
**通义千问-72B**（**Qwen-72B**）是阿里云研发的通义千问大模型系列的720亿参数规模的模型。Qwen-72B是基于Transformer的大语言模型, 在超大规模的预训练数据上进行训练得到。预训练数据类型多样，覆盖广泛，包括大量网络文本、专业书籍、代码等。同时，在Qwen-72B的基础上，我们使用对齐机制打造了基于大语言模型的AI助手Qwen-72B-Chat。本仓库为Qwen-72B的仓库。
通义千问-72B（Qwen-72B）主要有以下特点：
1. **大规模高质量训练语料**：使用超过3万亿tokens的数据进行预训练，包含高质量中、英、多语言、代码、数学等数据，涵盖通用及专业领域的训练语料。通过大量对比实验对预训练语料分布进行了优化。
2. **强大的性能**：Qwen-72B在多个中英文下游评测任务上（涵盖常识推理、代码、数学、翻译等），效果显著超越现有的开源模型。具体评测结果请详见下文。
3. **覆盖更全面的词表**：相比目前以中英词表为主的开源模型，Qwen-72B使用了约15万大小的词表。该词表对多语言更加友好，方便用户在不扩展词表的情况下对部分语种进行能力增强和扩展。
4. **较长的上下文支持**：Qwen-72B支持32k的上下文长度。
如果您想了解更多关于通义千问72B开源模型的细节，我们建议您参阅[GitHub代码库](https://github.com/QwenLM/Qwen)。
**Qwen-72B** is the 72B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-72B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-72B, we release Qwen-72B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. This repository is the one for Qwen-72B.
The features of Qwen-72B include:
1. **Large-scale high-quality training corpora**: It is pretrained on over 3 trillion tokens, including Chinese, English, multilingual texts, code, and mathematics, covering general and professional fields. The distribution of the pre-training corpus has been optimized through a large number of ablation experiments.
2. **Competitive performance**: It significantly surpasses existing open-source models on multiple Chinese and English downstream evaluation tasks (including commonsense, reasoning, code, mathematics, etc.). See below for specific evaluation results.
3. **More comprehensive vocabulary coverage**: Compared with other open-source models based on Chinese and English vocabularies, Qwen-72B uses a vocabulary of over 150K tokens. This vocabulary is more friendly to multiple languages, enabling users to directly further enhance the capability for certain languages without expanding the vocabulary.
4. **Longer context support**: Qwen-72B supports 32k context length.
For more details about the open-source model of Qwen-72B, please refer to the [GitHub](https://github.com/QwenLM/Qwen) code repository.
<br>
## 要求（Requirements）
* python 3.8及以上版本
* pytorch 1.12及以上版本，推荐2.0及以上版本
* 建议使用CUDA 11.4及以上（GPU用户、flash-attention用户等需考虑此选项）
* **运行BF16或FP16模型需要多卡至少144GB显存（例如2xA100-80G或5xV100-32G）；运行Int4模型至少需要48GB显存（例如1xA100-80G或2xV100-32G）。**
* python 3.8 and above
* pytorch 1.12 and above, 2.0 and above are recommended
* CUDA 11.4 and above are recommended (this is for GPU users, flash-attention users, etc.)
**To run Qwen-72B-Chat in bf16/fp16, at least 144GB GPU memory is required (e.g., 2xA100-80G or 5xV100-32G). To run it in int4, at least 48GB GPU memory is requred (e.g., 1xA100-80G or 2xV100-32G).**
<br>
## 依赖项 (Dependency)
运行Qwen-72B，请确保满足上述要求，再执行以下pip命令安装依赖库
To run Qwen-72B, please make sure you meet the above requirements, and then execute the following pip commands to install the dependent libraries.
```bash
pip install transformers==4.32.0 accelerate tiktoken einops scipy transformers_stream_generator==0.0.4 peft deepspeed
```
另外，推荐安装`flash-attention`库（**当前已支持flash attention 2**），以实现更高的效率和更低的显存占用。
In addition, it is recommended to install the `flash-attention` library (**we support flash attention 2 now.**) for higher efficiency and lower memory usage.
```bash
git clone https://github.com/Dao-AILab/flash-attention
cd flash-attention && pip install .
# 下方安装可选，安装可能比较缓慢。
# Below are optional. Installing them might be slow.
# pip install csrc/layer_norm
# 如果你的flash-attn版本高于2.1.1，下方不需要安装。
# If the version of flash-attn is higher than 2.1.1, the following is not needed.
# pip install csrc/rotary
```
<br>
## 快速使用（Quickstart）
您可以通过以下代码轻松调用：
You can easily call the model with the following code:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
# Note: The default behavior now has injection attack prevention off.
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-72B", trust_remote_code=True)
# use bf16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-72B", device_map="auto", trust_remote_code=True, bf16=True).eval()
# use fp16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-72B", device_map="auto", trust_remote_code=True, fp16=True).eval()
# use cpu only
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-72B", device_map="cpu", trust_remote_code=True).eval()
# use auto mode, automatically select precision based on the device.
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-72B", device_map="auto", trust_remote_code=True).eval()
# Specify hyperparameters for generation. But if you use transformers>=4.32.0, there is no need to do this.
# model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-72B", trust_remote_code=True)
inputs = tokenizer('蒙古国的首都是乌兰巴托（Ulaanbaatar）\n冰岛的首都是雷克雅未克（Reykjavik）\n埃塞俄比亚的首都是', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
# 蒙古国的首都是乌兰巴托（Ulaanbaatar）\n冰岛的首都是雷克雅未克（Reykjavik）\n埃塞俄比亚的首都是亚的斯亚贝巴（Addis Ababa）...
```
关于更多的使用说明，请参考我们的[GitHub repo](https://github.com/QwenLM/Qwen)获取更多信息。
For more information, please refer to our [GitHub repo](https://github.com/QwenLM/Qwen) for more information.
<br>
## Tokenizer
> 注：作为术语的“tokenization”在中文中尚无共识的概念对应，本文档采用英文表达以利说明。
基于tiktoken的分词器有别于其他分词器，比如sentencepiece分词器。尤其在微调阶段，需要特别注意特殊token的使用。关于tokenizer的更多信息，以及微调时涉及的相关使用，请参阅[文档](https://github.com/QwenLM/Qwen/blob/main/tokenization_note_zh.md)。
Our tokenizer based on tiktoken is different from other tokenizers, e.g., sentencepiece tokenizer. You need to pay attention to special tokens, especially in finetuning. For more detailed information on the tokenizer and related use in fine-tuning, please refer to the [documentation](https://github.com/QwenLM/Qwen/blob/main/tokenization_note.md).
<br>
## 模型细节 (Model)
Qwen-72B模型规模基本情况如下所示：
The details of the model architecture of Qwen-72B are listed as follows:
| Hyperparameter | Value |
|:----------------|:-------|
| n_layers | 80 |
| n_heads | 64 |
| d_model | 8192 |
| vocab size | 151851 |
| sequence length | 32768 |
在位置编码、FFN激活函数和normalization的实现方式上，我们也采用了目前最流行的做法，
即RoPE相对位置编码、SwiGLU激活函数、RMSNorm（可选安装flash-attention加速）。
在分词器方面，相比目前主流开源模型以中英词表为主，Qwen-72B使用了超过15万token大小的词表。 该词表在GPT-4使用的BPE词表`cl100k_base`基础上，对中文、多语言进行了优化，在对中、英、代码数据的高效编解码的基础上，对部分多语言更加友好，方便用户在不扩展词表的情况下对部分语种进行能力增强。
词表对数字按单个数字位切分。调用较为高效的[tiktoken分词库](https://github.com/openai/tiktoken)进行分词。
我们从部分语种各随机抽取100万个文档语料，以对比不同模型的编码压缩率（以支持100语种的XLM-R为基准值1，越低越好），具体性能见图。
可以看到Qwen-72B在保持中英代码高效解码的前提下，对部分使用人群较多的语种（泰语th、希伯来语he、阿拉伯语ar、韩语ko、越南语vi、日语ja、土耳其语tr、印尼语id、波兰语pl、俄语ru、荷兰语nl、葡萄牙语pt、意大利语it、德语de、西班牙语es、法语fr等）上也实现了较高的压缩率，使得模型在这些语种上也具备较强的可扩展性和较高的训练和推理效率。
在预训练数据方面，Qwen-72B模型一方面利用了部分开源通用语料，
另一方面也积累了海量全网语料以及高质量文本内容，去重及过滤后的语料超过3T tokens。
囊括全网文本、百科、书籍、代码、数学及各个领域垂类。
<p align="center">
<img src="assets/tokenizer.png" style="width: 1200px"/>
<p>
For position encoding, FFN activation function, and normalization methods, we adopt the prevalent practices, i.e., RoPE relative position encoding, SwiGLU for activation function, and RMSNorm for normalization (optional installation of flash-attention for acceleration).
For tokenization, compared to the current mainstream open-source models based on Chinese and English vocabularies, Qwen-72B uses a vocabulary of over 150K tokens. It first considers efficient encoding of Chinese, English, and code data, and is also more friendly to multilingual languages, enabling users to directly enhance the capability of some languages without expanding the vocabulary. It segments numbers by single digit, and calls the [tiktoken](https://github.com/openai/tiktoken) tokenizer library for efficient tokenization.
We randomly selected 1 million document corpus of each language to test and compare the encoding compression rates of different models (with XLM-R, which supports 100 languages, as the base value 1). The specific performance is shown in the figure above.
As can be seen, while ensuring the efficient decoding of Chinese, English, and code, Qwen-72B also achieves a high compression rate for many other languages (such as th, he, ar, ko, vi, ja, tr, id, pl, ru, nl, pt, it, de, es, fr etc.), equipping the model with strong scalability as well as high training and inference efficiency in these languages.
For pre-training data, on the one hand, Qwen-72B uses part of the open-source generic corpus. On the other hand, it uses a massive amount of accumulated web corpus and high-quality text content. The scale of corpus reaches over 3T tokens after deduplication and filtration, encompassing web text, encyclopedias, books, code, mathematics, and various domain.
<br>
## 评测效果（Evaluation）
我们选取了MMLU，C-Eval，GSM8K, MATH, HumanEval, MBPP, BBH, CMMLU等目前较流行的benchmark，对模型的中英知识能力、翻译、数学推理、代码等能力进行综合评测。Qwen-72B模型在所有benchmark上均取得了开源模型中的最优表现。
We selected MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, CMMLU, which are currently popular benchmarks, to test the model’s Chinese and English knowledge capabilities, translation, mathematical reasoning, coding and other capabilities. From the following comprehensive evaluation results, we can see that the Qwen model outperform the similarly sized open-source models on all tasks.
| Model | Avg | MMLU | C-Eval | GSM8K | MATH | HumanEval | MBPP | BBH | AGIEval | GaokaoBench | CMMLU |
|:-------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:---------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| | | 5-shot | 5-shot | 8-shot | 4-shot | 0-shot | 3-shot | 3-shot | 0-shot | 0-shot | 5-shot |
| LLaMA2-7B | 24.4 | 46.8 | 32.5 | 16.7 | 3.3 | 12.8 | 20.8 | 38.2 | 21.8 | 18.9 | 31.8 |
| LLaMA2-13B | 31.3 | 55.0 | 41.4 | 29.6 | 5.0 | 18.9 | 30.3 | 45.6 | 30.9 | 18.2 | 38.4 |
| LLaMA2-70B | 45.7 | 69.7 | 50.1 | 63.5 | 12.0 | 26.2 | 39.6 | 64.9 | 54.2 | 23.3 | 53.6 |
| InternLM-20B | 47.2 | 62.1 | 58.8 | 52.6 | 7.9 | 25.6 | 35.6 | 52.5 | 59.0 | 59.0 | 59.0 |
| Yi-34B | 58.0 | 76.3 | 81.8 | 67.9 | 15.9 | 26.2 | 38.2 | 66.4 | 56.5 | 68.3 | 82.6 |
| XVERSE-65B | - | 70.8 | 68.6 | 60.3 | - | 26.3 | - | - | - | - | - |
| **Qwen-7B** | 46.2 | 58.2 | 63.5 | 51.7 | 11.6 | 29.9 | 31.6 | 45.0 | 45.3 | 62.5 | 62.2 |
| **Qwen-14B** | 52.7 | 66.3 | 72.1 | 61.3 | 24.8 | 32.3 | 40.8 | 53.4 | 51.9 | 52.7 | 71.0 |
| **Qwen-72B** | **66.4** | **77.4** | **83.3** | **78.9** | **35.2** | **35.4** | **52.2** | **67.7** | **62.5** | **87.6** | **83.6** |
### 长序列评测（Long-Context Evaluation）
Qwen-72B采用扩展RoPE base的训练方法，支持32k的外推长度，我们使用arXiv数据进行语言建模评测，PPL（越低越好）结果如下：
Qwen-72B uses the method of extending RoPE base and supports the extrapolation length of 32k. We use arXiv data for language modeling evaluation. The PPL (lower is better) results are as follows:
<table>
<tr>
<th rowspan="2">Model</th><th colspan="6" align="center">Sequence Length</th>
</tr>
<tr>
</th><th align="center">8192</th><th align="center">16384</th><th align="center">32768</th>
</tr>
<tr>
<td>Qwen-72B</td><td align="center">2.828</td><td align="center">2.734</td><td align="center">2.717</td>
</tr>
</table>
## 评测复现（Reproduction）
我们提供了评测脚本，方便大家复现模型效果，详见[链接](https://github.com/QwenLM/Qwen/tree/main/eval)。提示：由于硬件和框架造成的舍入误差，复现结果如有小幅波动属于正常现象。
We have provided evaluation scripts to reproduce the performance of our model, details as [link](https://github.com/QwenLM/Qwen/tree/main/eval).
<br>
## FAQ
如遇到问题，敬请查阅[FAQ](https://github.com/QwenLM/Qwen/blob/main/FAQ_zh.md)以及issue区，如仍无法解决再提交issue。
If you meet problems, please refer to [FAQ](https://github.com/QwenLM/Qwen/blob/main/FAQ.md) and the issues first to search a solution before you launch a new issue.
<br>
## 引用 (Citation)
如果你觉得我们的工作对你有帮助，欢迎引用！
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
<br>
## 使用协议（License Agreement）
我们的代码和模型权重对学术研究完全开放，并支持商用。请查看[LICENSE](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT)了解具体的开源协议细节。如需商用，请填写[问卷](https://dashscope.console.aliyun.com/openModelApply/Qwen-72B-Chat)申请。
Our code and checkpoints are open to research purpose, and they are allowed for commercial purposes. Check [LICENSE](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) for more details about the license. If you have requirements for commercial use, please fill out the [form](https://dashscope.console.aliyun.com/openModelApply/Qwen-72B-Chat) to apply.
<br>
## 联系我们（Contact Us）
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If you are interested to leave a message to either our research team or product team, join our Discord or WeChat groups! Also, feel free to send an email to [email protected].
|
starmpcc/Asclepius-Llama2-7B | starmpcc | "2024-01-11T03:26:30Z" | 8,449 | 13 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"medical",
"text2text-generation",
"en",
"dataset:starmpcc/Asclepius-Synthetic-Clinical-Notes",
"arxiv:2309.00237",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | "2023-09-19T05:41:41Z" | ---
license: cc-by-nc-4.0
datasets:
- starmpcc/Asclepius-Synthetic-Clinical-Notes
language:
- en
pipeline_tag: text2text-generation
tags:
- medical
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is an official model checkpoint for Asclepius-Llama2-7B [(arxiv)](https://arxiv.org/abs/2309.00237).
This model is an enhanced version of Asclepius-7B, by replacing the base model with Llama-2 and increasing the max sequence length to 4096.
## UPDATE
### 2024.01.10
- Asclepius-R, the variant of Asclepius that trained on MIMIC-III discharge summaries, is now available on [Physionet](https://physionet.org/content/asclepius-r/1.0.0/)!
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Model type:** Clinical LLM (Large Language Model)
- **Language(s) (NLP):** English
- **License:** CC-BY-NC-SA 4.0
- **Finetuned from model [optional]:** Llama2-7B
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/starmpcc/Asclepius
- **Paper:** https://arxiv.org/abs/2309.00237
- **Data:** https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This model can perform below 8 clinical NLP tasks, with clincal notes.
- Named Entity Recognition
- Abbreviation Expansion
- Relation Extraction
- Temporal Information Extraction
- Coreference Resolution
- Paraphrasing
- Summarization
- Question Answering
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
ONLY USE THIS MODEL FOR RESEARCH PURPOSE!!
## How to Get Started with the Model
```python
prompt = """You are an intelligent clinical languge model.
Below is a snippet of patient's discharge summary and a following instruction from healthcare professional.
Write a response that appropriately completes the instruction.
The response should provide the accurate answer to the instruction, while being concise.
[Discharge Summary Begin]
{note}
[Discharge Summary End]
[Instruction Begin]
{question}
[Instruction End]
"""
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("starmpcc/Asclepius-Llama2-7B", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("starmpcc/Asclepius-Llama2-7B")
note = "This is a sample note"
question = "What is the diagnosis?"
model_input = prompt.format(note=note, question=question)
input_ids = tokenizer(model_input, return_tensors="pt").input_ids
output = model.generate(input_ids)
print(tokenizer.decode(output[0]))
```
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- Initial training was conducted using causal language modeling on synthetic clinical notes.
- It was then fine-tuned with clinical instruction-response pairs.
- For a comprehensive overview of our methods, our upcoming paper will serve as a resource.
#### Training Hyperparameters
- We followed config used in [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
-
#### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
- Pre-Training (1 epoch): 1h 7m with 8x A100 80G
- Instruction Fine-Tuning (3 epoch): 6h 47m with 8x A100 80G
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@misc{kweon2023publicly,
title={Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes},
author={Sunjun Kweon and Junu Kim and Jiyoun Kim and Sujeong Im and Eunbyeol Cho and Seongsu Bae and Jungwoo Oh and Gyubok Lee and Jong Hak Moon and Seng Chan You and Seungjin Baek and Chang Hoon Han and Yoon Bin Jung and Yohan Jo and Edward Choi},
year={2023},
eprint={2309.00237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_starmpcc__Asclepius-Llama2-7B)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 42.38 |
| ARC (25-shot) | 50.85 |
| HellaSwag (10-shot) | 76.53 |
| MMLU (5-shot) | 43.61 |
| TruthfulQA (0-shot) | 43.31 |
| Winogrande (5-shot) | 68.27 |
| GSM8K (5-shot) | 0.3 |
| DROP (3-shot) | 13.8 |
|
textattack/roberta-base-SST-2 | textattack | "2023-05-21T19:59:24Z" | 8,442 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-03-02T23:29:05Z" | Entry not found |
GraydientPlatformAPI/arthemy-comics6 | GraydientPlatformAPI | "2024-01-10T06:52:25Z" | 8,440 | 1 | diffusers | [
"diffusers",
"safetensors",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-01-10T06:47:41Z" | ---
license: openrail
---
|
QuantFactory/SOVL_Llama3_8B-GGUF | QuantFactory | "2024-06-24T07:41:13Z" | 8,436 | 0 | transformers | [
"transformers",
"gguf",
"text-generation",
"en",
"base_model:ResplendentAI/SOVL_Llama3_8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-24T06:32:10Z" | ---
base_model: ResplendentAI/SOVL_Llama3_8B
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---
# QuantFactory/SOVL_Llama3_8B-GGUF
This is quantized version of [ResplendentAI/SOVL_Llama3_8B](https://huggingface.co/ResplendentAI/SOVL_Llama3_8B) created using llama.cpp
# Model Description
I'm not gonna tell you this is the best model anyone has ever made. I'm not going to tell you that you will love chatting with SOVL.
What I am gonna say is thank you for taking the time out of your day. Without users like you, my work would be meaningless. |
markussagen/xlm-roberta-longformer-base-4096 | markussagen | "2022-03-30T09:24:39Z" | 8,432 | 34 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"longformer",
"multilingual",
"dataset:wikitext",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | ---
tags:
- longformer
language: multilingual
license: apache-2.0
datasets:
- wikitext
---
## XLM-R Longformer Model / XLM-Long
XLM-R Longformer (or XLM-Long for short) is a XLM-R model that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer [pre-training scheme](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) on the English WikiText-103 corpus.
The reason for this was to investigate methods for creating efficient Transformers for low-resource languages, such as Swedish, without the need to pre-train them on long-context datasets in each respecitve language. The trained model came as a result of a master thesis project at [Peltarion](https://peltarion.com/) and was fine-tuned on multilingual quesion-answering tasks, with code available [here](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer#xlm-r).
Since both XLM-R model and Longformer models are large models, it it recommended to run the models with NVIDIA Apex (16bit precision), large GPU and several gradient accumulation steps.
## How to Use
The model can be used as expected to fine-tune on a downstream task.
For instance for QA.
```python
import torch
from transformers import AutoModel, AutoTokenizer
MAX_SEQUENCE_LENGTH = 4096
MODEL_NAME_OR_PATH = "markussagen/xlm-roberta-longformer-base-4096"
tokenizer = AutoTokenizer.from_pretrained(
MODEL_NAME_OR_PATH,
max_length=MAX_SEQUENCE_LENGTH,
padding="max_length",
truncation=True,
)
model = AutoModelForQuestionAnswering.from_pretrained(
MODEL_NAME_OR_PATH,
max_length=MAX_SEQUENCE_LENGTH,
)
```
## Training Procedure
The model have been trained on the WikiText-103 corpus, using a **48GB** GPU with the following training script and parameters. The model was pre-trained for 6000 iterations and took ~5 days. See the full [training script](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer/blob/main/scripts/finetune_qa_models.py) and [Github repo](https://github.com/MarkusSagen/Master-Thesis-Multilingual-Longformer) for more information
```sh
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip
unzip wikitext-103-raw-v1.zip
export DATA_DIR=./wikitext-103-raw
scripts/run_long_lm.py \
--model_name_or_path xlm-roberta-base \
--model_name xlm-roberta-to-longformer \
--output_dir ./output \
--logging_dir ./logs \
--val_file_path $DATA_DIR/wiki.valid.raw \
--train_file_path $DATA_DIR/wiki.train.raw \
--seed 42 \
--max_pos 4096 \
--adam_epsilon 1e-8 \
--warmup_steps 500 \
--learning_rate 3e-5 \
--weight_decay 0.01 \
--max_steps 6000 \
--evaluate_during_training \
--logging_steps 50 \
--eval_steps 50 \
--save_steps 6000 \
--max_grad_norm 1.0 \
--per_device_eval_batch_size 2 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 64 \
--overwrite_output_dir \
--fp16 \
--do_train \
--do_eval
```
|
google/umt5-small | google | "2023-07-06T02:31:38Z" | 8,432 | 19 | transformers | [
"transformers",
"pytorch",
"text2text-generation",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"hi",
"hmn",
"ht",
"hu",
"hy",
"ig",
"is",
"it",
"iw",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lb",
"lo",
"lt",
"lv",
"mg",
"mi",
"mk",
"ml",
"mn",
"mr",
"ms",
"mt",
"my",
"ne",
"nl",
"no",
"ny",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sd",
"si",
"sk",
"sl",
"sm",
"sn",
"so",
"sq",
"sr",
"st",
"su",
"sv",
"sw",
"ta",
"te",
"tg",
"th",
"tr",
"uk",
"und",
"ur",
"uz",
"vi",
"xh",
"yi",
"yo",
"zh",
"zu",
"dataset:mc4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2023-07-02T01:48:53Z" | ---
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
- ml
- mn
- mr
- ms
- mt
- my
- ne
- nl
- no
- ny
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- sm
- sn
- so
- sq
- sr
- st
- su
- sv
- sw
- ta
- te
- tg
- th
- tr
- uk
- und
- ur
- uz
- vi
- xh
- yi
- yo
- zh
- zu
datasets:
- mc4
license: apache-2.0
---
[Google's UMT5](https://github.com/google-research/multilingual-t5)
UMT5 is pretrained on the an updated version of [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 107 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.
**Note**: UMT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual)
Other Community Checkpoints: [here](https://huggingface.co/models?search=umt5)
Paper: [UniMax, Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining](https://openreview.net/forum?id=kXwdL1cWOAi)
Authors: *by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant*
## Abstract
*Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.* |
Helsinki-NLP/opus-mt-en-nl | Helsinki-NLP | "2023-08-16T11:30:40Z" | 8,429 | 4 | transformers | [
"transformers",
"pytorch",
"tf",
"rust",
"marian",
"text2text-generation",
"translation",
"en",
"nl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2022-03-02T23:29:04Z" | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-en-nl
* source languages: en
* target languages: nl
* OPUS readme: [en-nl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-nl/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-04.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-nl/opus-2019-12-04.zip)
* test set translations: [opus-2019-12-04.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-nl/opus-2019-12-04.test.txt)
* test set scores: [opus-2019-12-04.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-nl/opus-2019-12-04.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.en.nl | 57.1 | 0.730 |
|
facebook/timesformer-base-finetuned-k400 | facebook | "2023-01-02T11:43:07Z" | 8,418 | 23 | transformers | [
"transformers",
"pytorch",
"timesformer",
"video-classification",
"vision",
"arxiv:2102.05095",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | "2022-10-07T19:03:04Z" | ---
license: "cc-by-nc-4.0"
tags:
- vision
- video-classification
---
# TimeSformer (base-sized model, fine-tuned on Kinetics-400)
TimeSformer model pre-trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [TimeSformer: Is Space-Time Attention All You Need for Video Understanding?](https://arxiv.org/abs/2102.05095) by Tong et al. and first released in [this repository](https://github.com/facebookresearch/TimeSformer).
Disclaimer: The team releasing TimeSformer did not write a model card for this model so this model card has been written by [fcakyon](https://github.com/fcakyon).
## Intended uses & limitations
You can use the raw model for video classification into one of the 400 possible Kinetics-400 labels.
### How to use
Here is how to use this model to classify a video:
```python
from transformers import AutoImageProcessor, TimesformerForVideoClassification
import numpy as np
import torch
video = list(np.random.randn(8, 3, 224, 224))
processor = AutoImageProcessor.from_pretrained("facebook/timesformer-base-finetuned-k400")
model = TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400")
inputs = processor(video, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/timesformer.html#).
### BibTeX entry and citation info
```bibtex
@inproceedings{bertasius2021space,
title={Is Space-Time Attention All You Need for Video Understanding?},
author={Bertasius, Gedas and Wang, Heng and Torresani, Lorenzo},
booktitle={International Conference on Machine Learning},
pages={813--824},
year={2021},
organization={PMLR}
}
``` |
nlp04/korean_sentiment_analysis_kcelectra | nlp04 | "2023-01-18T03:26:01Z" | 8,397 | 3 | transformers | [
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-01-17T14:34:53Z" | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: korean_sentiment_analysis_kcelectra
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. -->
# korean_sentiment_analysis_kcelectra
This model is a fine-tuned version of [beomi/KcELECTRA-base-v2022](https://huggingface.co/beomi/KcELECTRA-base-v2022) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9718
- Micro f1 score: 70.7183
- Auprc: 68.4562
- Accuracy: 0.7072
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Micro f1 score | Auprc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:-------:|:--------:|
| 1.0543 | 1.0 | 391 | 0.9923 | 65.3061 | 49.6906 | 0.6531 |
| 0.8573 | 2.0 | 782 | 0.8229 | 69.9901 | 64.4071 | 0.6999 |
| 0.7217 | 3.0 | 1173 | 0.7961 | 71.0600 | 67.4640 | 0.7106 |
| 0.6305 | 4.0 | 1564 | 0.8163 | 71.1229 | 68.5191 | 0.7112 |
| 0.5294 | 5.0 | 1955 | 0.8205 | 71.0150 | 68.7334 | 0.7102 |
| 0.4689 | 6.0 | 2346 | 0.8716 | 71.1679 | 68.7751 | 0.7117 |
| 0.433 | 7.0 | 2737 | 0.9086 | 70.9880 | 68.3653 | 0.7099 |
| 0.419 | 8.0 | 3128 | 0.9290 | 70.6734 | 68.4606 | 0.7067 |
| 0.3766 | 9.0 | 3519 | 0.9619 | 70.6464 | 68.5132 | 0.7065 |
| 0.3395 | 10.0 | 3910 | 0.9718 | 70.7183 | 68.4562 | 0.7072 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.6.0
- Datasets 2.7.1
- Tokenizers 0.13.2
|
unicamp-dl/translation-en-pt-t5 | unicamp-dl | "2021-10-11T03:47:21Z" | 8,387 | 18 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"translation",
"en",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | translation | "2022-03-02T23:29:05Z" | ---
language:
- en
- pt
datasets:
- EMEA
- ParaCrawl 99k
- CAPES
- Scielo
- JRC-Acquis
- Biomedical Domain Corpora
tags:
- translation
metrics:
- bleu
---
# Introduction
This repository brings an implementation of T5 for translation in EN-PT tasks using a modest hardware setup. We propose some changes in tokenizator and post-processing that improves the result and used a Portuguese pretrained model for the translation. You can collect more informations in [our repository](https://github.com/unicamp-dl/Lite-T5-Translation). Also, check [our paper](https://aclanthology.org/2020.wmt-1.90.pdf)!
# Usage
Just follow "Use in Transformers" instructions. It is necessary to add a few words before to define the task to T5.
You can also create a pipeline for it. An example with the phrase "I like to eat rice" is:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/translation-en-pt-t5")
model = AutoModelForSeq2SeqLM.from_pretrained("unicamp-dl/translation-en-pt-t5")
enpt_pipeline = pipeline('text2text-generation', model=model, tokenizer=tokenizer)
enpt_pipeline("translate English to Portuguese: I like to eat rice.")
```
# Citation
```bibtex
@inproceedings{lopes-etal-2020-lite,
title = "Lite Training Strategies for {P}ortuguese-{E}nglish and {E}nglish-{P}ortuguese Translation",
author = "Lopes, Alexandre and
Nogueira, Rodrigo and
Lotufo, Roberto and
Pedrini, Helio",
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.wmt-1.90",
pages = "833--840",
}
``` |
laion/clap-htsat-fused | laion | "2023-04-24T14:40:12Z" | 8,382 | 23 | transformers | [
"transformers",
"pytorch",
"clap",
"feature-extraction",
"arxiv:2211.06687",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2023-02-16T20:45:11Z" | ---
license: apache-2.0
---
# Model card for CLAP
Model card for CLAP: Contrastive Language-Audio Pretraining
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Citation](#citation)
# TL;DR
The abstract of the paper states that:
> Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public.
# Usage
You can use this model for zero shot audio classification or extracting audio and/or textual features.
# Uses
## Perform zero-shot audio classification
### Using `pipeline`
```python
from datasets import load_dataset
from transformers import pipeline
dataset = load_dataset("ashraq/esc50")
audio = dataset["train"]["audio"][-1]["array"]
audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-fused")
output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
print(output)
>>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}]
```
## Run the model:
You can also get the audio and text embeddings using `ClapModel`
### Run the model on CPU:
```python
from datasets import load_dataset
from transformers import ClapModel, ClapProcessor
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
audio_sample = librispeech_dummy[0]
model = ClapModel.from_pretrained("laion/clap-htsat-fused")
processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused")
inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt")
audio_embed = model.get_audio_features(**inputs)
```
### Run the model on GPU:
```python
from datasets import load_dataset
from transformers import ClapModel, ClapProcessor
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
audio_sample = librispeech_dummy[0]
model = ClapModel.from_pretrained("laion/clap-htsat-fused").to(0)
processor = ClapProcessor.from_pretrained("laion/clap-htsat-fused")
inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0)
audio_embed = model.get_audio_features(**inputs)
```
# Citation
If you are using this model for your work, please consider citing the original paper:
```
@misc{https://doi.org/10.48550/arxiv.2211.06687,
doi = {10.48550/ARXIV.2211.06687},
url = {https://arxiv.org/abs/2211.06687},
author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo},
keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` |
roneneldan/TinyStories-33M | roneneldan | "2023-08-08T22:32:26Z" | 8,373 | 85 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"dataset:roneneldan/TinyStories",
"arxiv:2305.07759",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-05-12T19:16:05Z" | ---
datasets:
- roneneldan/TinyStories
---
Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759
Based on GPT-Neo architecture.
License: mit
---
hyperparams used to train this model:
lr = 5e-4,
lr_schedule = constant,
wd=0.1,
adam_beta1=0.9, adam_beta2 = 0.95,
context_length=512,
batch_size=80,
gradient_accumulation_steps=16
------ EXAMPLE USAGE ---
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-33M')
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
prompt = "Once upon a time there was"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Generate completion
output = model.generate(input_ids, max_length = 1000, num_beams=1)
# Decode the completion
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
# Print the generated text
print(output_text) |
hatemnoaman/bert-base-arabic-finetuned-emotion | hatemnoaman | "2023-12-03T19:48:10Z" | 8,368 | 2 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotone_ar",
"doi:10.57967/hf/1421",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-10-15T15:52:10Z" | ---
tags:
- generated_from_trainer
datasets:
- emotone_ar
metrics:
- accuracy
- f1
model-index:
- name: bert-base-arabic-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotone_ar
type: emotone_ar
config: default
split: train[:90%]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7415506958250497
- name: F1
type: f1
value: 0.7406006078114171
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-arabic-finetuned-emotion
This model is a fine-tuned version of [asafaya/bert-base-arabic](https://huggingface.co/asafaya/bert-base-arabic) on the emotone_ar dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8965
- Accuracy: 0.7416
- F1: 0.7406
### Cite this model
```
-Noaman, H. (2023). Improved Emotion Detection Framework for Arabic Text using Transformer Models.
Advanced Engineering Technology and Application, 12(2), 1-11.
@article{noaman2023improved,
title={Improved Emotion Detection Framework for Arabic Text using Transformer Models},
author={Noaman, Hatem},
journal={Advanced Engineering Technology and Application},
volume={12},
number={2},
pages={1--11},
year={2023},
publisher={Fayoum University}
}
```
## Load Pretrained Model
You can use this model by
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("hatemnoaman/bert-base-arabic-finetuned-emotion")
model = AutoModel.from_pretrained("hatemnoaman/bert-base-arabic-finetuned-emotion")
```
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3476 | 1.0 | 142 | 0.8911 | 0.7008 | 0.6812 |
| 0.8204 | 2.0 | 284 | 0.8175 | 0.7276 | 0.7212 |
| 0.6227 | 3.0 | 426 | 0.8392 | 0.7376 | 0.7302 |
| 0.4816 | 4.0 | 568 | 0.8531 | 0.7435 | 0.7404 |
| 0.378 | 5.0 | 710 | 0.8817 | 0.7396 | 0.7388 |
| 0.3134 | 6.0 | 852 | 0.8965 | 0.7416 | 0.7406 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
|
TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF | TheBloke | "2024-01-31T21:50:48Z" | 8,360 | 71 | trl | [
"trl",
"gguf",
"mistral",
"distilabel",
"dpo",
"rlaif",
"rlhf",
"en",
"dataset:argilla/dpo-mix-7k",
"base_model:argilla/CapybaraHermes-2.5-Mistral-7B",
"license:apache-2.0",
"region:us"
] | null | "2024-01-31T21:19:50Z" | ---
base_model: argilla/CapybaraHermes-2.5-Mistral-7B
datasets:
- argilla/dpo-mix-7k
inference: false
language:
- en
library_name: trl
license: apache-2.0
model_creator: Argilla
model_name: CapyBaraHermes 2.5 Mistral 7B
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- distilabel
- dpo
- rlaif
- rlhf
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
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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 -->
# CapyBaraHermes 2.5 Mistral 7B - GGUF
- Model creator: [Argilla](https://huggingface.co/argilla)
- Original model: [CapyBaraHermes 2.5 Mistral 7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Argilla's CapyBaraHermes 2.5 Mistral 7B](https://huggingface.co/argilla/CapybaraHermes-2.5-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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF)
* [Argilla's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [capybarahermes-2.5-mistral-7b.Q2_K.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q2_K.gguf) | Q2_K | 2 | 2.72 GB| 5.22 GB | significant quality loss - not recommended for most purposes |
| [capybarahermes-2.5-mistral-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [capybarahermes-2.5-mistral-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [capybarahermes-2.5-mistral-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [capybarahermes-2.5-mistral-7b.Q4_0.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-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 |
| [capybarahermes-2.5-mistral-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [capybarahermes-2.5-mistral-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [capybarahermes-2.5-mistral-7b.Q5_0.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [capybarahermes-2.5-mistral-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [capybarahermes-2.5-mistral-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [capybarahermes-2.5-mistral-7b.Q6_K.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-mistral-7b.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [capybarahermes-2.5-mistral-7b.Q8_0.gguf](https://huggingface.co/TheBloke/CapybaraHermes-2.5-Mistral-7B-GGUF/blob/main/capybarahermes-2.5-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/CapybaraHermes-2.5-Mistral-7B-GGUF and below it, a specific filename to download, such as: capybarahermes-2.5-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/CapybaraHermes-2.5-Mistral-7B-GGUF capybarahermes-2.5-mistral-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/CapybaraHermes-2.5-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/CapybaraHermes-2.5-Mistral-7B-GGUF capybarahermes-2.5-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 35 -m capybarahermes-2.5-mistral-7b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./capybarahermes-2.5-mistral-7b.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./capybarahermes-2.5-mistral-7b.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
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: Argilla's CapyBaraHermes 2.5 Mistral 7B
# CapybaraHermes-2.5-Mistral-7B
<div>
<img src="https://cdn-uploads.huggingface.co/production/uploads/60420dccc15e823a685f2b03/Vmr0FtTvnny6Snm-UDM_n.png">
</div>
<p align="center">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
This model is the launching partner of the [capybara-dpo dataset](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-9k-binarized) build with ⚗️ distilabel. It's a preference tuned [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B).
CapybaraHermes has been preference tuned with LoRA and TRL for 3 epochs using argilla's [dpo mix 7k](https://huggingface.co/datasets/argilla/dpo-mix-7k).
To test the impact on multi-turn performance we have used MTBench. We also include the Nous Benchmark results and Mistral-7B-Instruct-v0.2 for reference as it's a strong 7B model on MTBench:
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | MTBench First Turn | MTBench Second Turn | Nous avg. | MTBench avg. |
|-----------------------------------|---------|---------|------------|----------|------------|-------------|-----------|--------------|
| argilla/CapybaraHermes-2.5-Mistral-7B | **43.8** | **73.35** | 57.07 | **42.44** | 8.24375 | **7.5625** | 54.16 | **7.903125** |
| teknium/OpenHermes-2.5-Mistral-7B | 42.75 | 72.99 | 52.99 | 40.94 | **8.25** | 7.2875 | 52.42 | 7.76875 |
| Mistral-7B-Instruct-v0.2 | 38.5 | 71.64 | **66.82** | 42.29 | 7.8375 | 7.1 | **54.81** | 7.46875 |
The most interesting aspect in the context of the capybara-dpo dataset is the increased performance in MTBench Second Turn scores.
For the merge lovers, we also preference tuned Beagle14-7B with a mix of capybara-dpo and distilabel orca pairs using the same recipe as NeuralBeagle (see [ YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard) for reference):
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|------------------------------------------------------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[DistilabelBeagle14-7B](https://huggingface.co/dvilasuero/DistilabelBeagle14-7B)| 45.29| 76.92| 71.66| 48.78| 60.66|
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** Argilla
- **Shared by [optional]:** Argilla
- **Model type:** 7B chat model
- **Language(s) (NLP):** English
- **License:** Same as OpenHermes
- **Finetuned from model [optional]:** [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
<!-- original-model-card end -->
|
AdamCodd/vit-base-nsfw-detector | AdamCodd | "2024-06-17T18:32:40Z" | 8,359 | 4 | transformers.js | [
"transformers.js",
"onnx",
"safetensors",
"vit",
"image-classification",
"transformers",
"nlp",
"base_model:google/vit-base-patch16-384",
"license:apache-2.0",
"model-index",
"region:us"
] | image-classification | "2024-01-03T20:10:17Z" | ---
metrics:
- accuracy
pipeline_tag: image-classification
base_model: google/vit-base-patch16-384
model-index:
- name: AdamCodd/vit-base-nsfw-detector
results:
- task:
type: image-classification
name: Image Classification
metrics:
- type: accuracy
value: 0.9654
name: Accuracy
- type: AUC
value: 0.9948
- type: loss
value: 0.0937
name: Loss
license: apache-2.0
tags:
- transformers.js
- transformers
- nlp
---
# vit-base-nsfw-detector
This model is a fine-tuned version of [vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on around 25_000 images (drawings, photos...).
It achieves the following results on the evaluation set:
- Loss: 0.0937
- Accuracy: 0.9654
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
## Intended uses & limitations
There are two classes: SFW and NSFW. The model has been trained to be restrictive and therefore classify "sexy" images as NSFW. That is, if the image shows cleavage or too much skin, it will be classified as NSFW. This is normal.
Usage for a local image:
```python
from transformers import pipeline
from PIL import Image
img = Image.open("<path_to_image_file>")
predict = pipeline("image-classification", model="AdamCodd/vit-base-nsfw-detector")
predict(img)
```
Usage for a distant image:
```python
from transformers import ViTImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = ViTImageProcessor.from_pretrained('AdamCodd/vit-base-nsfw-detector')
model = AutoModelForImageClassification.from_pretrained('AdamCodd/vit-base-nsfw-detector')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
# Predicted class: sfw
```
Usage with Transformers.js (Vanilla JS):
```js
/* Instructions:
* - Place this script in an HTML file using the <script type="module"> tag.
* - Ensure the HTML file is served over a local or remote server (e.g., using Python's http.server, Node.js server, or similar).
* - Replace 'https://example.com/path/to/image.jpg' in the classifyImage function call with the URL of the image you want to classify.
*
* Example of how to include this script in HTML:
* <script type="module" src="path/to/this_script.js"></script>
*
* This setup ensures that the script can use imports and perform network requests without CORS issues.
*/
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';
// Since we will download the model from HuggingFace Hub, we can skip the local model check
env.allowLocalModels = false;
// Load the image classification model
const classifier = await pipeline('image-classification', 'AdamCodd/vit-base-nsfw-detector');
// Function to fetch and classify an image from a URL
async function classifyImage(url) {
try {
const response = await fetch(url);
if (!response.ok) throw new Error('Failed to load image');
const blob = await response.blob();
const image = new Image();
const imagePromise = new Promise((resolve, reject) => {
image.onload = () => resolve(image);
image.onerror = reject;
image.src = URL.createObjectURL(blob);
});
const img = await imagePromise; // Ensure the image is loaded
const classificationResults = await classifier([img.src]); // Classify the image
console.log('Predicted class: ', classificationResults[0].label);
} catch (error) {
console.error('Error classifying image:', error);
}
}
// Example usage
classifyImage('https://example.com/path/to/image.jpg');
// Predicted class: sfw
```
The model has been trained on a variety of images (realistic, 3D, drawings), yet it is not perfect and some images may be wrongly classified as NSFW when they are not. Additionally, please note that using the quantized ONNX model within the transformers.js pipeline will slightly reduce the model's accuracy.
You can find a toy implementation of this model with Transformers.js [here](https://github.com/AdamCodd/media-random-generator).
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 1
### Training results
- Validation Loss: 0.0937
- Accuracy: 0.9654,
- AUC: 0.9948
[Confusion matrix](https://huggingface.co/AdamCodd/vit-base-nsfw-detector/resolve/main/confusion_matrix.png) (eval):
[1076 37]
[ 60 1627]
### Framework versions
- Transformers 4.36.2
- Evaluate 0.4.1
If you want to support me, you can [here](https://ko-fi.com/adamcodd). |
google/long-t5-tglobal-large | google | "2023-09-11T20:35:44Z" | 8,342 | 15 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2022-04-16T11:20:39Z" | ---
license: apache-2.0
language: en
---
# LongT5 (transient-global attention, large-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-research/longt5). All the model architecture and configuration can be found in [Flaxformer repository](https://github.com/google/flaxformer) which uses another Google research project repository [T5x](https://github.com/google-research/t5x).
Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting ([Pegasus-like generation pre-training](https://arxiv.org/pdf/1912.08777.pdf)). LongT5 model is an extension of [T5 model](https://arxiv.org/pdf/1910.10683.pdf), and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence.
LongT5 is particularly effective when fine-tuned for text generation (summarization, question answering) which requires handling long input sequences (up to 16,384 tokens).
Results of LongT5 (transient-global attention, large-sized model) fine-tuned on multiple (summarization, QA) tasks.
| Dataset | Rouge-1 | Rouge-2 | Rouge-Lsum |
| --- | --- | --- | --- |
| arXiv (16k input) | 48.28 | 21.63 | 44.11 |
| PubMed (16k input) | 49.98 | 24.69 | 46.46 |
| BigPatent (16k input) | 70.38 | 56.81 | 62.73 |
| MultiNews (8k input) | 47.18 | 18.44 | 24.18 |
| MediaSum (4k input) | 35.54 | 19.04 | 32.20 |
| CNN / DailyMail (4k input) | 42.49 | 20.51 | 40.18 |
| Dataset | EM | F1 |
| --- | --- | --- |
| Natural Questions (4k input) | 60.77 | 65.38 |
| Trivia QA (16k input) | 78.38 | 82.45 |
## Intended uses & limitations
The model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=longt5) to look for fine-tuned versions on a task that interests you.
### How to use
```python
from transformers import AutoTokenizer, LongT5Model
tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-large")
model = LongT5Model.from_pretrained("google/long-t5-tglobal-large")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
```bibtex
@article{guo2021longt5,
title={LongT5: Efficient Text-To-Text Transformer for Long Sequences},
author={Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei},
journal={arXiv preprint arXiv:2112.07916},
year={2021}
}
``` |
arcee-ai/Arcee-Spark-GGUF | arcee-ai | "2024-06-24T05:30:16Z" | 8,340 | 24 | null | [
"gguf",
"en",
"de",
"ar",
"license:apache-2.0",
"region:us"
] | null | "2024-06-24T03:19:38Z" | ---
license: apache-2.0
language:
- en
- de
- ar
---
<div align="center">
<img src="https://i.ibb.co/80ssNWS/o-Vdk-Qx-ARNmzr-Pi1h-Efj-SA.webp" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>
# Arcee Spark
Arcee Spark is a powerful 7B parameter language model that punches well above its weight class. Initialized from Qwen2, this model underwent a sophisticated training process:
1. Fine-tuned on 1.8 million samples
2. Merged with Qwen2-7B-Instruct using Arcee's mergekit
3. Further refined using Direct Preference Optimization (DPO)
This meticulous process results in exceptional performance, with Arcee Spark achieving the highest score on MT-Bench for models of its size, outperforming even GPT-3.5 on many tasks.
## Key Features
- 7B parameters
- State-of-the-art performance for its size
- Initialized from Qwen2
- Advanced training process including fine-tuning, merging, and DPO
- Highest MT-Bench score in the 7B class
- Outperforms GPT-3.5 on many tasks
## Business Use Cases
Arcee Spark offers a compelling solution for businesses looking to leverage advanced AI capabilities without the hefty computational requirements of larger models. Its unique combination of small size and high performance makes it ideal for:
1. **Real-time applications**: Deploy Arcee Spark for chatbots, customer service automation, and interactive systems where low latency is crucial.
2. **Edge computing**: Run sophisticated AI tasks on edge devices or in resource-constrained environments.
3. **Cost-effective scaling**: Implement advanced language AI across your organization without breaking the bank on infrastructure or API costs.
4. **Rapid prototyping**: Quickly develop and iterate on AI-powered features and products.
5. **On-premise deployment**: Easily host Arcee Spark on local infrastructure for enhanced data privacy and security.
## Performance and Efficiency
Arcee Spark demonstrates that bigger isn't always better in the world of language models. By leveraging advanced training techniques and architectural optimizations, it delivers:
- **Speed**: Blazing fast inference times, often 10-100x faster than larger models.
- **Efficiency**: Significantly lower computational requirements, reducing both costs and environmental impact.
- **Flexibility**: Easy to fine-tune or adapt for specific domains or tasks.
Despite its compact size, Arcee Spark offers deep reasoning capabilities, making it suitable for a wide range of complex tasks including:
- Advanced text generation
- Detailed question answering
- Nuanced sentiment analysis
- Complex problem-solving
- Code generation and analysis
## Model Availability
- **Quants**: [Arcee Spark GGUF](https://huggingface.co/arcee-ai/Arcee-Spark-GGUF)
- **FP32**: For those looking to squeeze every bit of performance out of the model, we offer an [FP32 version](https://huggingface.co/arcee-ai/Arcee-Spark-FP32) that scores slightly higher on all benchmarks.
## Benchmarks and Evaluations
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/dQRtXR7/Screenshot-2024-06-23-at-11-01-59-PM.png" alt="Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
<div style="display: flex; justify-content: center; margin: 20px 0;">
<img src="https://i.ibb.co/BLX8GmZ/Screenshot-2024-06-23-at-10-43-50-PM.png" alt="Additional Benchmark Results" style="border-radius: 10px; max-width: 90%; height: auto; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19);">
</div>
### MT-Bench
```markdown
########## First turn ##########
score
model turn
arcee-spark 1 8.777778
########## Second turn ##########
score
model turn
arcee-spark 2 8.164634
########## Average ##########
score
model
arcee-spark 8.469325
```
### EQ-Bench
EQ-Bench: 71.4
### TruthfulQA
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4382|± |0.0174|
| | |mc2 |0.6150|± |0.0155|
### AGI-Eval
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.3937|± |0.0307|
| | |acc_norm|0.3937|± |0.0307|
|agieval_logiqa_en | 0|acc |0.4731|± |0.0196|
| | |acc_norm|0.4854|± |0.0196|
|agieval_lsat_ar | 0|acc |0.2783|± |0.0296|
| | |acc_norm|0.3000|± |0.0303|
|agieval_lsat_lr | 0|acc |0.5549|± |0.0220|
| | |acc_norm|0.5451|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6022|± |0.0299|
| | |acc_norm|0.6208|± |0.0296|
|agieval_sat_en | 0|acc |0.8155|± |0.0271|
| | |acc_norm|0.8107|± |0.0274|
|agieval_sat_en_without_passage| 0|acc |0.4806|± |0.0349|
| | |acc_norm|0.4612|± |0.0348|
|agieval_sat_math | 0|acc |0.4909|± |0.0338|
| | |acc_norm|0.4545|± |0.0336|
AGI-eval average: 51.11
### GPT4All Evaluation
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5333|± |0.0146|
| | |acc_norm|0.5640|± |0.0145|
|arc_easy | 0|acc |0.8131|± |0.0080|
| | |acc_norm|0.7668|± |0.0087|
|boolq | 1|acc |0.8471|± |0.0063|
|hellaswag | 0|acc |0.6206|± |0.0048|
| | |acc_norm|0.8118|± |0.0039|
|openbookqa | 0|acc |0.3560|± |0.0214|
| | |acc_norm|0.4600|± |0.0223|
|piqa | 0|acc |0.7987|± |0.0094|
| | |acc_norm|0.8030|± |0.0093|
|winogrande | 0|acc |0.7690|± |0.0130|
Gpt4al Average: 69.37
## License
Arcee Spark is released under the Apache 2.0 license.
## Acknowledgments
- The Qwen2 team for their foundational work
- The open-source AI community for their invaluable tools and datasets
- Our dedicated team of researchers and engineers who push the boundaries of what's possible with compact language models
|
facebook/nougat-base | facebook | "2023-11-20T09:26:49Z" | 8,338 | 130 | transformers | [
"transformers",
"pytorch",
"safetensors",
"vision-encoder-decoder",
"vision",
"nougat",
"image-to-text",
"arxiv:2308.13418",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | image-to-text | "2023-09-21T08:39:24Z" | ---
license: cc-by-nc-4.0
tags:
- vision
- nougat
pipeline_tag: image-to-text
---
# Nougat model, base-sized version
Nougat model trained on PDF-to-markdown. It was introduced in the paper [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by Blecher et al. and first released in [this repository](https://github.com/facebookresearch/nougat/tree/main).
Disclaimer: The team releasing Nougat did not write a model card for this model so this model card has been written by the Hugging Face team.
Note: this model corresponds to the "0.1.0-base" version of the original repository.
## Model description
Nougat is a [Donut](https://huggingface.co/docs/transformers/model_doc/donut) model trained to transcribe scientific PDFs into an easy-to-use markdown format. The model consists of a Swin Transformer as vision encoder, and an mBART model as text decoder.
The model is trained to autoregressively predict the markdown given only the pixels of the PDF image as input.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/nougat_architecture.jpg"
alt="drawing" width="600"/>
<small> Nougat high-level overview. Taken from the <a href="https://arxiv.org/abs/2308.13418">original paper</a>. </small>
## Intended uses & limitations
You can use the raw model for transcribing a PDF into Markdown. See the [model hub](https://huggingface.co/models?search=nougat) to look for other
fine-tuned versions that may interest you.
### How to use
We refer to the [docs](https://huggingface.co/docs/transformers/main/en/model_doc/nougat).
### BibTeX entry and citation info
```bibtex
@misc{blecher2023nougat,
title={Nougat: Neural Optical Understanding for Academic Documents},
author={Lukas Blecher and Guillem Cucurull and Thomas Scialom and Robert Stojnic},
year={2023},
eprint={2308.13418},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` |
kekmodel/StopCarbon-10.7B-v5 | kekmodel | "2024-01-03T16:58:20Z" | 8,332 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"conversational",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-12-30T13:00:52Z" | ---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- merge
---
# StopCarbon
This model is an experimental version created using [mergekit](https://github.com/cg123/mergekit).
- merge models
- kyujinpy/Sakura-SOLAR-Instruct
- jeonsworld/CarbonVillain-en-10.7B-v1
- merge_method: slerp
# Prompt Template(s)
```
### User:
{user}
### Assistant:
{asistant}
``` |
ibm-granite/granite-3b-code-instruct-accelerator | ibm-granite | "2024-07-02T15:10:10Z" | 8,332 | 0 | transformers | [
"transformers",
"pytorch",
"mlp_speculator",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T18:01:40Z" | ---
license: apache-2.0
---
## Installation from source
```bash
git clone https://github.com/foundation-model-stack/fms-extras
cd fms-extras
pip install -e .
```
## Description
This model is intended to be used as an accelerator for [granite-3b-code-instruct](https://huggingface.co/ibm-granite/granite-3b-code-instruct) and takes inspiration
from the Medusa speculative decoding architecture. This accelerator modifies the MLP into a multi-stage MLP, where each stage predicts
a single token in the draft based on both a state vector and sampled token
from the prior stage (the base model can be considered stage 0).
The state vector from the base model provides contextual information to the accelerator,
while conditioning on prior sampled tokens allows it to produce higher-quality draft n-grams.
Note: The underlying MLP speculator is a generic architecture that can be trained with any generative model to accelerate inference.
Training is light-weight and can be completed in only a few days depending on base model size and speed.
## Repository Links
1. [Paged Attention KV-Cache / Speculator](https://github.com/foundation-model-stack/fms-extras)
2. [Production Server with speculative decoding](https://github.com/IBM/text-generation-inference.git)
3. [Speculator training](https://github.com/foundation-model-stack/fms-fsdp/pull/35)
## Samples
_Note: For all samples, your environment must have access to cuda_
### Use in IBM Production TGIS
*To try this out running in a production-like environment, please use the pre-built docker image:*
#### Setup
```bash
HF_HUB_CACHE=/hf_hub_cache
chmod a+w $HF_HUB_CACHE
HF_HUB_TOKEN="your huggingface hub token"
TGIS_IMAGE=quay.io/wxpe/text-gen-server:main.ddc56ee
docker pull $TGIS_IMAGE
# optionally download granite-3b-code-instruct if the weights do not already exist
docker run --rm \
-v $HF_HUB_CACHE:/models \
-e HF_HUB_CACHE=/models \
-e TRANSFORMERS_CACHE=/models \
$TGIS_IMAGE \
text-generation-server download-weights \
ibm-granite/granite-3b-code-instruct \
--token $HF_HUB_TOKEN
# optionally download the speculator model if the weights do not already exist
docker run --rm \
-v $HF_HUB_CACHE:/models \
-e HF_HUB_CACHE=/models \
-e TRANSFORMERS_CACHE=/models \
$TGIS_IMAGE \
text-generation-server download-weights \
ibm-granite/granite-3b-code-instruct-accelerator \
--token $HF_HUB_TOKEN
# note: if the weights were downloaded separately (not with the above commands), please place them in the HF_HUB_CACHE directory and refer to them with /models/<model_name>
docker run -d --rm --gpus all \
--name my-tgis-server \
-p 8033:8033 \
-v $HF_HUB_CACHE:/models \
-e HF_HUB_CACHE=/models \
-e TRANSFORMERS_CACHE=/models \
-e MODEL_NAME=ibm-granite/granite-3b-code-instruct \
-e SPECULATOR_NAME=ibm-granite/granite-3b-code-instruct-accelerator \
-e FLASH_ATTENTION=true \
-e PAGED_ATTENTION=true \
-e DTYPE=float16 \
$TGIS_IMAGE
# check logs and wait for "gRPC server started on port 8033" and "HTTP server started on port 3000"
docker logs my-tgis-server -f
# get the client sample (Note: The first prompt will take longer as there is a warmup time)
conda create -n tgis-client-env python=3.11
conda activate tgis-client-env
git clone --branch main --single-branch https://github.com/IBM/text-generation-inference.git
cd text-generation-inference/integration_tests
make gen-client
pip install . --no-cache-dir
```
#### Run Sample
```bash
python sample_client.py
```
_Note: first prompt may be slower as there is a slight warmup time_
### Use in Huggingface TGI
#### start the server
```bash
model=ibm-granite/granite-3b-code-instruct-accelerator
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model
```
_note: for tensor parallel, add --num-shard_
#### make a request
```bash
curl 127.0.0.1:8080/generate_stream \
-X POST \
-d '{"inputs":"Write a bubble sort in python","parameters":{"max_new_tokens":100}}' \
-H 'Content-Type: application/json'
``` |
TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF | TheBloke | "2023-12-19T16:26:48Z" | 8,330 | 68 | transformers | [
"transformers",
"gguf",
"solar",
"base_model:w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored",
"license:apache-2.0",
"region:us"
] | null | "2023-12-19T16:20:38Z" | ---
base_model: w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
inference: false
license: apache-2.0
model_creator: Stepan Zuev
model_name: Solar 10.7B Instruct V1.0 Uncensored
model_type: solar
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>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Solar 10.7B Instruct V1.0 Uncensored - GGUF
- Model creator: [Stepan Zuev](https://huggingface.co/w4r10ck)
- Original model: [Solar 10.7B Instruct V1.0 Uncensored](https://huggingface.co/w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Stepan Zuev's Solar 10.7B Instruct V1.0 Uncensored](https://huggingface.co/w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored).
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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF)
* [Stepan Zuev's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [solar-10.7b-instruct-v1.0-uncensored.Q2_K.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q2_K.gguf) | Q2_K | 2 | 4.55 GB| 7.05 GB | smallest, significant quality loss - not recommended for most purposes |
| [solar-10.7b-instruct-v1.0-uncensored.Q3_K_S.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q3_K_S.gguf) | Q3_K_S | 3 | 4.67 GB| 7.17 GB | very small, high quality loss |
| [solar-10.7b-instruct-v1.0-uncensored.Q3_K_M.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q3_K_M.gguf) | Q3_K_M | 3 | 5.19 GB| 7.69 GB | very small, high quality loss |
| [solar-10.7b-instruct-v1.0-uncensored.Q3_K_L.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q3_K_L.gguf) | Q3_K_L | 3 | 5.65 GB| 8.15 GB | small, substantial quality loss |
| [solar-10.7b-instruct-v1.0-uncensored.Q4_0.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q4_0.gguf) | Q4_0 | 4 | 6.07 GB| 8.57 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [solar-10.7b-instruct-v1.0-uncensored.Q4_K_S.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q4_K_S.gguf) | Q4_K_S | 4 | 6.10 GB| 8.60 GB | small, greater quality loss |
| [solar-10.7b-instruct-v1.0-uncensored.Q4_K_M.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q4_K_M.gguf) | Q4_K_M | 4 | 6.46 GB| 8.96 GB | medium, balanced quality - recommended |
| [solar-10.7b-instruct-v1.0-uncensored.Q5_0.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q5_0.gguf) | Q5_0 | 5 | 7.40 GB| 9.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [solar-10.7b-instruct-v1.0-uncensored.Q5_K_S.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q5_K_S.gguf) | Q5_K_S | 5 | 7.40 GB| 9.90 GB | large, low quality loss - recommended |
| [solar-10.7b-instruct-v1.0-uncensored.Q5_K_M.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q5_K_M.gguf) | Q5_K_M | 5 | 7.60 GB| 10.10 GB | large, very low quality loss - recommended |
| [solar-10.7b-instruct-v1.0-uncensored.Q6_K.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q6_K.gguf) | Q6_K | 6 | 8.81 GB| 11.31 GB | very large, extremely low quality loss |
| [solar-10.7b-instruct-v1.0-uncensored.Q8_0.gguf](https://huggingface.co/TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF/blob/main/solar-10.7b-instruct-v1.0-uncensored.Q8_0.gguf) | Q8_0 | 8 | 11.41 GB| 13.91 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/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF and below it, a specific filename to download, such as: solar-10.7b-instruct-v1.0-uncensored.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/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF solar-10.7b-instruct-v1.0-uncensored.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/SOLAR-10.7B-Instruct-v1.0-uncensored-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/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF solar-10.7b-instruct-v1.0-uncensored.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 35 -m solar-10.7b-instruct-v1.0-uncensored.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## 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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./solar-10.7b-instruct-v1.0-uncensored.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"{prompt}", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./solar-10.7b-instruct-v1.0-uncensored.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
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: Stepan Zuev's Solar 10.7B Instruct V1.0 Uncensored
[upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) finetuned on [unalignment/toxic-dpo-v0.1](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1)
<!-- original-model-card end -->
|
EMBEDDIA/sloberta | EMBEDDIA | "2021-11-24T13:46:22Z" | 8,325 | 4 | transformers | [
"transformers",
"pytorch",
"camembert",
"fill-mask",
"sl",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:04Z" | ---
language:
- sl
license: cc-by-sa-4.0
---
# Usage
Load in transformers library with:
```
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/sloberta")
model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/sloberta")
```
# SloBERTa
SloBERTa model is a monolingual Slovene BERT-like model. It is closely related to French Camembert model https://camembert-model.fr/. The corpora used for training the model have 3.47 billion tokens in total. The subword vocabulary contains 32,000 tokens. The scripts and programs used for data preparation and training the model are available on https://github.com/clarinsi/Slovene-BERT-Tool
SloBERTa was trained for 200,000 iterations or about 98 epochs.
## Corpora
The following corpora were used for training the model:
* Gigafida 2.0
* Kas 1.0
* Janes 1.0 (only Janes-news, Janes-forum, Janes-blog, Janes-wiki subcorpora)
* Slovenian parliamentary corpus siParl 2.0
* slWaC
|
nealcly/detection-longformer | nealcly | "2023-08-11T08:11:20Z" | 8,319 | 4 | transformers | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | "2023-05-24T07:15:45Z" | Entry not found |
Yntec/edgeOfRealism | Yntec | "2023-09-26T08:22:50Z" | 8,300 | 6 | diffusers | [
"diffusers",
"safetensors",
"Photorealistic",
"Highly Detailed",
"Realistic",
"AreThoseLevel4Plates",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-09-25T06:03:56Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Photorealistic
- Highly Detailed
- Realistic
- AreThoseLevel4Plates
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# EdgeOfRealism
Original page: https://civitai.com/models/21813?modelVersionId=26041
Samples and prompt:
iphone, Professional fine details photo of pretty cute girl from kazan, tatarstan kid in the postsoviet suburbia, tatar, detailed photo, beautiful eyes. instagram, portrait |
gsarti/it5-base-news-summarization | gsarti | "2022-10-18T13:43:57Z" | 8,299 | 5 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"italian",
"sequence-to-sequence",
"fanpage",
"ilpost",
"summarization",
"it",
"dataset:ARTeLab/fanpage",
"dataset:ARTeLab/ilpost",
"arxiv:2203.03759",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | summarization | "2022-03-02T23:29:05Z" | ---
language:
- it
license: apache-2.0
datasets:
- ARTeLab/fanpage
- ARTeLab/ilpost
tags:
- italian
- sequence-to-sequence
- fanpage
- ilpost
- summarization
widget:
- text: "Non lo vuole sposare. E’ quanto emerge all’interno dell’ultima intervista di Raffaella Fico che, ringraziando Mancini per i buoni consigli elargiti al suo fidanzato, rimanda l’idea del matrimonio per qualche anno ancora. La soubrette, che è stata recentemente protagonista di una dedica di Supermario, non ha ancora intenzione di accasarsi perché è sicura che per mettersi la fede al dito ci sia ancora tempo. Nonostante il suo Mario sia uno degli sportivi più desiderati al mondo, l’ex protagonista del Grande Fratello non ha alcuna intenzione di cedere seriamente alla sua corte. Solo qualche giorno fa, infatti, dopo l’ultima bravata di Balotelli, Mancini gli aveva consigliato di sposare la sua Raffaella e di mettere la testa a posto. Chi pensava che sarebbe stato Mario a rispondere, però, si è sbagliato. A mettere le cose bene in chiaro è la Fico che, intervistata dall’emittente radiofonica Rtl 102.5, dice: È presto per sposarsi, siamo ancora molto giovani. È giusto che prima uno si realizzi nel proprio lavoro. E poi successivamente perché no, ci si può anche pensare. Quando si è giovani capita di fare qualche pazzia, quindi ci sta. Comunque i tabloid inglesi sono totalmente accaniti sulla sua vita privata quando poi dovrebbero interessarsi di più di quello che fa sul campo. Lui non fa le cose con cattiveria, ma quando si è giovani si fanno determinate cose senza stare a pensare se sono giuste o sbagliate. Mario ha gli obiettivi puntati addosso: più per la sua vita privata che come giocatore. Per me può anche andare in uno strip club, se non fa niente di male, con gli amici, però devo dire che alla fine torna sempre da me, sono la sua preferita."
- text: "Valerio è giovanissimo ma già una star. Fuori dall’Ariston ragazzine e meno ragazzine passano ore anche sotto la pioggia per vederlo. Lui è forte del suo talento e sicuro. Partecipa in gara tra i “big” di diritto, per essere arrivato in finalissima nel programma Amici di Maria De Filippi e presenta il brano Per tutte le volte che scritta per lui da Pierdavide Carone. Valerio Scanu è stato eliminato. Ma non è detta l'ultima parola: il duetto di questa sera con Alessandra Amoroso potrebbe risollevarlo e farlo rientrare in gara. Che cosa è successo alla giuria visto che sei stato eliminato anche se l’esibizione era perfetta? Nn lo so. Sono andate bene le esibizioni, ero emozionato ma tranquillo. Ero contento ma ho cantato bene. Non sono passato e stasera ci sarà il ballottaggio… Quali sono le differenze tra Amici e Sanremo? Sono due cose diverse. Amici ti prepara a salire sul palco di amici. A Sanremo ci devi arrivare… ho fatto più di sessanta serate nel tour estivo, poi promozione del secondo disco. Una bella palestra. Sono cresciuto anche umanamente. Sono riuscito a percepire quello che il pubblico trasmette. L’umiltà? Prima di tutto. Sennò non sarei qui."
- text: "L’azienda statunitense Broadcom, uno dei più grandi produttori di semiconduttori al mondo, ha presentato un’offerta per acquisire Qualcomm, altra grande società degli Stati Uniti conosciuta soprattutto per la sua produzione di microprocessori Snapdragon (ARM), utilizzati in centinaia di milioni di smartphone in giro per il mondo. Broadcom ha proposto di acquistare ogni azione di Qualcomm al prezzo di 70 dollari, per un valore complessivo di circa 105 miliardi di dollari (130 miliardi se si comprendono 25 miliardi di debiti netti) . Se l’operazione dovesse essere approvata, sarebbe una delle più grandi acquisizioni di sempre nella storia del settore tecnologico degli Stati Uniti. Broadcom ha perfezionato per mesi la sua proposta di acquisto e, secondo i media statunitensi, avrebbe già preso contatti con Qualcomm per trovare un accordo. Secondo gli analisti, Qualcomm potrebbe comunque opporsi all’acquisizione perché il prezzo offerto è di poco superiore a quello dell’attuale valore delle azioni dell’azienda. Ci potrebbero essere inoltre complicazioni sul piano dell’antitrust da valutare, prima di un’eventuale acquisizione."
- text: "Dal 31 maggio è infine partita la piattaforma ITsART, a più di un anno da quando – durante il primo lockdown – il ministro della Cultura Dario Franceschini ne aveva parlato come di «una sorta di Netflix della cultura», pensata per «offrire a tutto il mondo la cultura italiana a pagamento». È presto per dare giudizi definitivi sulla piattaforma, e di certo sarà difficile farlo anche più avanti senza numeri precisi. Al momento, l’unica cosa che si può fare è guardare com’è fatto il sito, contare quanti contenuti ci sono (circa 700 “titoli”, tra film, documentari, spettacoli teatrali e musicali e altri eventi) e provare a dare un giudizio sul loro valore e sulla loro varietà. Intanto, una cosa notata da più parti è che diversi contenuti di ITsART sono a pagamento sulla piattaforma sebbene altrove, per esempio su RaiPlay, siano invece disponibili gratuitamente."
metrics:
- rouge
model-index:
- name: it5-base-news-summarization
results:
- task:
type: news-summarization
name: "News Summarization"
dataset:
type: newssum-it
name: "NewsSum-IT"
metrics:
- type: rouge1
value: 0.339
name: "Test Rouge1"
- type: rouge2
value: 0.160
name: "Test Rouge2"
- type: rougeL
value: 0.263
name: "Test RougeL"
co2_eq_emissions:
emissions: 17
source: "Google Cloud Platform Carbon Footprint"
training_type: "fine-tuning"
geographical_location: "Eemshaven, Netherlands, Europe"
hardware_used: "1 TPU v3-8 VM"
thumbnail: https://gsarti.com/publication/it5/featured.png
---
# IT5 Base for News Summarization ✂️🗞️ 🇮🇹
This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on news summarization on the [Fanpage](https://huggingface.co/datasets/ARTeLab/fanpage) and [Il Post](https://huggingface.co/datasets/ARTeLab/ilpost) corpora as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io).
A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.
## Using the model
Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as:
```python
from transformers import pipelines
newsum = pipeline("summarization", model='it5/it5-base-news-summarization')
newsum("Dal 31 maggio è infine partita la piattaforma ITsART, a più di un anno da quando – durante il primo lockdown – il ministro della Cultura Dario Franceschini ne aveva parlato come di «una sorta di Netflix della cultura», pensata per «offrire a tutto il mondo la cultura italiana a pagamento». È presto per dare giudizi definitivi sulla piattaforma, e di certo sarà difficile farlo anche più avanti senza numeri precisi. Al momento, l’unica cosa che si può fare è guardare com’è fatto il sito, contare quanti contenuti ci sono (circa 700 “titoli”, tra film, documentari, spettacoli teatrali e musicali e altri eventi) e provare a dare un giudizio sul loro valore e sulla loro varietà. Intanto, una cosa notata da più parti è che diversi contenuti di ITsART sono a pagamento sulla piattaforma sebbene altrove, per esempio su RaiPlay, siano invece disponibili gratuitamente.")
>>> [{"generated_text": "ITsART, la Netflix della cultura italiana, parte da maggio. Film, documentari, spettacoli teatrali e musicali disponibili sul nuovo sito a pagamento."}]
```
or loaded using autoclasses:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("it5/it5-base-news-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-base-news-summarization")
```
If you use this model in your research, please cite our work as:
```bibtex
@article{sarti-nissim-2022-it5,
title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation},
author={Sarti, Gabriele and Nissim, Malvina},
journal={ArXiv preprint 2203.03759},
url={https://arxiv.org/abs/2203.03759},
year={2022},
month={mar}
}
``` |
QuantFactory/dolphin-2.9.3-mistral-7B-32k-GGUF | QuantFactory | "2024-06-25T07:45:33Z" | 8,290 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-06-25T07:02:50Z" | Entry not found |
mschiesser/ner-bert-german | mschiesser | "2023-03-23T02:24:41Z" | 8,288 | 4 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"de",
"dataset:wikiann",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2023-01-16T04:22:50Z" | ---
license: apache-2.0
language: de
tags:
- generated_from_trainer
datasets:
- wikiann
model-index:
- name: ner-bert-german
results: []
examples: null
widget:
- text: "Herr Schmidt lebt in Berlin und arbeitet für die UN."
example_title: Schmidt aus Berlin
- text: "Die Deutsche Bahn hat ihren Hauptsitz in Frankfurt."
example_title: Deutsche Bahn
- text: "In München gibt es viele Unternehmen, z.B. BMW und Siemens."
example_title: München
metrics:
- seqeval
---
<!-- 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. -->
# ner-bert-german
This model can be used to do [named-entity recognition](https://en.wikipedia.org/wiki/Named-entity_recognition) in German.
It is trained on a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the German wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2450
- Overall Precision: 0.8767
- Overall Recall: 0.8893
- Overall F1: 0.8829
- Overall Accuracy: 0.9606
- Loc F1: 0.9067
- Org F1: 0.8278
- Per F1: 0.9152
## 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:|
| 0.252 | 0.8 | 1000 | 0.1724 | 0.8422 | 0.8368 | 0.8395 | 0.9501 | 0.8702 | 0.7593 | 0.8921 |
| 0.1376 | 1.6 | 2000 | 0.1679 | 0.8388 | 0.8607 | 0.8497 | 0.9528 | 0.8814 | 0.7712 | 0.8971 |
| 0.0982 | 2.4 | 3000 | 0.1880 | 0.8631 | 0.8598 | 0.8614 | 0.9564 | 0.8847 | 0.7915 | 0.9070 |
| 0.0681 | 3.2 | 4000 | 0.1956 | 0.8599 | 0.8775 | 0.8686 | 0.9574 | 0.8905 | 0.8084 | 0.9097 |
| 0.0477 | 4.0 | 5000 | 0.2115 | 0.8738 | 0.8814 | 0.8776 | 0.9593 | 0.9003 | 0.8207 | 0.9144 |
| 0.031 | 4.8 | 6000 | 0.2274 | 0.8751 | 0.8826 | 0.8788 | 0.9598 | 0.9017 | 0.8246 | 0.9115 |
| 0.0229 | 5.6 | 7000 | 0.2317 | 0.8715 | 0.8888 | 0.8801 | 0.9598 | 0.9061 | 0.8208 | 0.9145 |
| 0.0181 | 6.4 | 8000 | 0.2450 | 0.8767 | 0.8893 | 0.8829 | 0.9606 | 0.9067 | 0.8278 | 0.9152 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2 |
sentence-transformers/msmarco-roberta-base-v3 | sentence-transformers | "2024-03-27T11:37:33Z" | 8,266 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
pipeline_tag: sentence-similarity
---
# sentence-transformers/msmarco-roberta-base-v3
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## 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('sentence-transformers/msmarco-roberta-base-v3')
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('sentence-transformers/msmarco-roberta-base-v3')
model = AutoModel.from_pretrained('sentence-transformers/msmarco-roberta-base-v3')
# 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, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-roberta-base-v3)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 510, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` |
Niggendar/ponymatureSDXL_v20 | Niggendar | "2024-05-09T08:04:18Z" | 8,266 | 2 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-05-09T07:59:59Z" | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
h2oai/h2ogpt-gm-7b-mistral-chat-sft-dpo-rag-v1 | h2oai | "2024-03-21T07:12:43Z" | 8,257 | 5 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-01-29T19:54:16Z" | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: >-
https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
license: apache-2.0
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
- Base model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.36.1
```
```python
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="h2oai/h2ogpt-gm-7b-mistral-chat-sft-dpo-rag-v1",
torch_dtype=torch.bfloat16,
device_map="auto",
)
# We use the HF Tokenizer chat template to format each message
# https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{"role": "user", "content": "Why is drinking water so healthy?"},
]
prompt = pipe.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
res = pipe(
prompt,
max_new_tokens=256,
)
print(res[0]["generated_text"])
# <|system|>You are a friendly chatbot</s><|prompt|>Why is drinking water so healthy?</s><|answer|> Drinking water is healthy for several reasons: [...]
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
MistralForCausalLM(
(model): MistralModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
Niggendar/mfcgPaintjob_v20 | Niggendar | "2024-05-02T17:51:02Z" | 8,257 | 4 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-05-02T17:46:33Z" | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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|
arnavgrg/mistral-7b-instruct-nf4-fp16-upscaled | arnavgrg | "2023-12-12T19:08:12Z" | 8,255 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-12-05T16:50:40Z" | ---
license: apache-2.0
tags:
- text-generation-inference
---
This is an upscaled fp16 variant of the original Mistral-7B-Instruct-v0.1 base model by Mistral after it has been loaded with nf4 4-bit quantization via bitsandbytes.
The main idea here is to upscale the linear4bit layers to fp16 so that the quantization/dequantization cost doesn't have to paid for each forward pass at inference time.
_Note: The quantization operation to nf4 is not lossless, so the model weights for the linear layers are lossy, which means that this model will not work as well as the official base model._
To use this model, you can just load it via `transformers` in fp16:
```python
import torch
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"arnavgrg/mistral-7b-instruct-nf4-fp16-upscaled",
device_map="auto",
torch_dtype=torch.float16,
)
``` |
timm/tf_efficientnet_lite0.in1k | timm | "2023-04-27T21:38:11Z" | 8,248 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1905.11946",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-12-13T00:13:28Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for tf_efficientnet_lite0.in1k
A EfficientNet-Lite image classification model. Trained on ImageNet-1k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 4.7
- GMACs: 0.4
- Activations (M): 6.7
- Image size: 224 x 224
- **Papers:**
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('tf_efficientnet_lite0.in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'tf_efficientnet_lite0.in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 16, 112, 112])
# torch.Size([1, 24, 56, 56])
# torch.Size([1, 40, 28, 28])
# torch.Size([1, 112, 14, 14])
# torch.Size([1, 320, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'tf_efficientnet_lite0.in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1280, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{tan2019efficientnet,
title={Efficientnet: Rethinking model scaling for convolutional neural networks},
author={Tan, Mingxing and Le, Quoc},
booktitle={International conference on machine learning},
pages={6105--6114},
year={2019},
organization={PMLR}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
codellama/CodeLlama-70b-Python-hf | codellama | "2024-04-12T14:18:07Z" | 8,243 | 105 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"llama-2",
"code",
"arxiv:2308.12950",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-01-29T10:59:39Z" |
---
language:
- code
pipeline_tag: text-generation
tags:
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
> [!NOTE]
> This is a non-official Code Llama repo. You can find the official Meta repository in the [Meta Llama organization](https://huggingface.co/meta-llama/CodeLlama-70b-Python-hf).
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) |
| 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) |
| 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) |
| 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) |
## Model Use
To use this model, please make sure to install `transformers`.
```bash
pip install transformers accelerate
```
Model capabilities:
- [x] Code completion.
- [ ] Infilling.
- [ ] Instructions / chat.
- [x] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in four model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B, 34B, and 70B parameters.
**This repository contains the Python version of the 70B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant **does not** support long context of up to 100k tokens.
**Model Dates** Code Llama and its variants have been trained between January 2023 and January 2024.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide). |
mradermacher/Qwen2-1.5Moe-GGUF | mradermacher | "2024-07-02T18:41:35Z" | 8,241 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"Qwen/Qwen2-1.5B",
"Replete-AI/Replete-Coder-Qwen2-1.5b",
"en",
"base_model:femiari/Qwen2-1.5Moe",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-07-01T23:38:34Z" | ---
base_model: femiari/Qwen2-1.5Moe
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Qwen/Qwen2-1.5B
- Replete-AI/Replete-Coder-Qwen2-1.5b
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/femiari/Qwen2-1.5Moe
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q2_K.gguf) | Q2_K | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.IQ3_XS.gguf) | IQ3_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.IQ3_S.gguf) | IQ3_S | 2.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.IQ3_M.gguf) | IQ3_M | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q3_K_M.gguf) | Q3_K_M | 2.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q6_K.gguf) | Q6_K | 3.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.Q8_0.gguf) | Q8_0 | 4.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-1.5Moe-GGUF/resolve/main/Qwen2-1.5Moe.f16.gguf) | f16 | 8.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
CHE-72-ZLab/Alibaba-Qwen2-7B-Instruct-GGUF | CHE-72-ZLab | "2024-06-23T17:07:06Z" | 8,240 | 0 | null | [
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"zh",
"cmn",
"base_model:Qwen/Qwen2-7B-Instruct",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-06-22T12:06:31Z" | ---
base_model: Qwen/Qwen2-7B-Instruct
language:
- en
- zh
- cmn
license: apache-2.0
pipeline_tag: text-generation
tags:
- chat
- llama-cpp
- gguf-my-repo
---
# CHE-72-ZLab/Alibaba-Qwen2-7B-Instruct-GGUF
This model was converted to GGUF format from [`Qwen/Qwen2-7B-Instruct`](https://huggingface.co/Qwen/Qwen2-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen2-7B-Instruct) for more details on the model.
|
aaditya/Llama3-OpenBioLLM-70B | aaditya | "2024-05-09T15:18:59Z" | 8,229 | 294 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-3",
"Mixtral",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"distillation",
"heathcare",
"medical",
"clinical",
"med",
"lifescience",
"Pharmaceutical",
"Pharma",
"conversational",
"en",
"arxiv:2305.18290",
"arxiv:2303.13375",
"arxiv:2212.13138",
"arxiv:2305.09617",
"arxiv:2402.07023",
"base_model:meta-llama/Meta-Llama-3-70B-Instruct",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-04-24T02:21:12Z" | ---
base_model: meta-llama/Meta-Llama-3-70B-Instruct
tags:
- llama-3
- llama
- Mixtral
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- distillation
- heathcare
- medical
- clinical
- med
- lifescience
- Pharmaceutical
- Pharma
model-index:
- name: OpenBioLLM-70B
results: []
license: llama3
language:
- en
widget:
- example_title: OpenBioLLM-70B
messages:
- role: system
content: >-
You are an expert and experienced from the healthcare and biomedical
domain with extensive medical knowledge and practical experience.
- role: user
content: How long does it take for newborn jaundice to go away?
output:
text: >-
Newborn jaundice, also known as neonatal jaundice, is a common condition
in newborns where the yellowing of the skin and eyes occurs due to an
elevated level of bilirubin in the blood. Bilirubin is a yellow pigment
that forms when red blood cells break down. In most cases, newborn
jaundice resolves on its own without any specific treatment.
The duration of newborn jaundice can vary depending on several factors
such as the underlying cause, gestational age at birth, and individual
variations in bilirubin metabolism. Here are some general guidelines:
1. Physiological jaundice: This is the most common type of newborn
jaundice and usually appears within 24-72 hours after birth. It tends to
peak between the second and fifth day of life and gradually improves over
the next week or two. By the time the baby is one week old, the jaundice
should have mostly resolved. 2. Breast milk jaundice: This type of
jaundice occurs in breastfed babies and may appear later than
physiological jaundice, typically between the fifth and fourteenth day of
life. It tends to persist for a longer duration but usually resolves
within six weeks after birth. 3. Pathological jaundice: This type of
jaundice is less common and occurs due to an underlying medical condition
that affects bilirubin metabolism or liver function. The duration of
pathological jaundice depends on the specific cause and may require
treatment.
It's important for parents to monitor their newborn's jaundice closely and
seek medical advice if the jaundice progresses rapidly, becomes severe, or
is accompanied by other symptoms such as poor feeding, lethargy, or
excessive sleepiness. In these cases, further evaluation and management
may be necessary. Remember that each baby is unique, and the timing of
jaundice resolution can vary. If you have concerns about your newborn's
jaundice, it's always best to consult with a healthcare professional for
personalized advice and guidance.
---
<div align="center">
<img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div>
<div align="center">
<h1>Advancing Open-source Large Language Models in Medical Domain</h1>
</div>
<p align="center" style="margin-top: 0px;">
<a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text" style=" margin-right: 5px;">Online Demo</span>
</a> |
<a href="https://github.com/openlifescience-ai">
<img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text" style=" margin-right: 5px;">GitHub</span>
</a> |
<a href="#">
<img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text" style="margin-right: 5px;">Paper</span>
</a> |
<a href="https://discord.gg/A5Fjf5zC69">
<img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text">Discord</span>
</a>
</p>
Introducing OpenBioLLM-70B: A State-of-the-Art Open Source Biomedical Large Language Model
OpenBioLLM-70B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks.
🏥 **Biomedical Specialization**: OpenBioLLM-70B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency.
🎓 **Superior Performance**: With 70 billion parameters, OpenBioLLM-70B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 on biomedical benchmarks.
🧠 **Advanced Training Techniques**: OpenBioLLM-70B builds upon the powerful foundations of the **Meta-Llama-3-70B-Instruct** and [Meta-Llama-3-70B-Instruct](meta-llama/Meta-Llama-3-70B-Instruct) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include:
<div align="center">
<img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png">
</div>
- **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290)
- **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)
This combination of cutting-edge techniques enables OpenBioLLM-70B to align with key capabilities and preferences for biomedical applications.
⚙️ **Release Details**:
- **Model Size**: 70 billion parameters
- **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-70B-GGUF)
- **Language(s) (NLP):** en
- **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs
- **License:** Meta-Llama License
- **Fine-tuned from models:** [Meta-Llama-3-70B-Instruct](meta-llama/Meta-Llama-3-70B-Instruct)
- **Resources for more information:**
- Paper: Coming soon
The model can be fine-tuned for more specialized tasks and datasets as needed.
OpenBioLLM-70B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.
We are excited to share OpenBioLLM-70B with researchers and developers around the world.
### Use with transformers
**Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.**
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "aaditya/OpenBioLLM-Llama3-70B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
messages = [
{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."},
{"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.0,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
## **Training procedure**
### **Training hyperparameters**
<details>
<summary>Click to see details</summary>
- learning_rate: 0.0002
- lr_scheduler: cosine
- train_batch_size: 12
- eval_batch_size: 8
- GPU: H100 80GB SXM5
- num_devices: 8
- optimizer: adamw_bnb_8bit
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
</details>
### **Peft hyperparameters**
<details>
<summary>Click to see details</summary>
- adapter: qlora
- lora_r: 128
- lora_alpha: 256
- lora_dropout: 0.05
- lora_target_linear: true
-lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
</details>
### **Training results**
### **Framework versions**
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
- Axolotl
- Lm harness for evaluation
# Benchmark Results
🔥 OpenBioLLM-70B demonstrates superior performance compared to larger models, such as GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 86.06%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.
🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.
| | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg |
|--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------|
| **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** |
| Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 |
| **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 |
| Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 |
| **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 |
| Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 |
| GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 |
| Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 |
| gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 |
| Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 |
| Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 |
| MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 |
| BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 |
| AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 |
| ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 |
<div align="center">
<img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png">
</div>
## Detailed Medical Subjectwise accuracy
# Use Cases & Examples
🚨 **Below results are from the quantized version of OpenBioLLM-70B
# Summarize Clinical Notes
OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries
# Answer Medical Questions
OpenBioLLM-70B can provide answers to a wide range of medical questions.
<details>
<summary>Click to see details</summary>
</details>
# Clinical Entity Recognition
OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.
# Biomarkers Extraction
# Classification
OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization
# De-Identification
OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.
**Advisory Notice!**
While OpenBioLLM-70B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.
Therefore, we strongly advise against using OpenBioLLM-70B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations.
OpenBioLLM-70B is intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.
Appropriately adapting and validating OpenBioLLM-70B for specific medical use cases would require significant additional work, potentially including:
- Thorough testing and evaluation in relevant clinical scenarios
- Alignment with evidence-based guidelines and best practices
- Mitigation of potential biases and failure modes
- Integration with human oversight and interpretation
- Compliance with regulatory and ethical standards
Always consult a qualified healthcare provider for personal medical needs.
# Citation
If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:
```
@misc{OpenBioLLMs,
author = {Ankit Pal, Malaikannan Sankarasubbu},
title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}}
}
```
The accompanying paper is currently in progress and will be released soon.
<div align="center">
<h2> 💌 Contact </h2>
</div>
We look forward to hearing you and collaborating on this exciting project!
**Contributors:**
- [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com]
- Saama AI Labs
- Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset.
# References
We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models!
Result sources
- [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375)
- [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138)
- [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617)
- [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023) |
01-ai/Yi-1.5-6B | 01-ai | "2024-06-26T10:42:02Z" | 8,224 | 22 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-05-11T08:25:23Z" | ---
license: apache-2.0
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://01-ai.github.io/">💪 Tech Blog</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI)|
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF | TheBloke | "2023-09-27T12:52:31Z" | 8,223 | 45 | transformers | [
"transformers",
"gguf",
"llama",
"uncensored",
"en",
"dataset:ehartford/wizard_vicuna_70k_unfiltered",
"base_model:ehartford/Wizard-Vicuna-30B-Uncensored",
"license:other",
"text-generation-inference",
"region:us"
] | null | "2023-09-19T23:02:39Z" | ---
language:
- en
license: other
tags:
- uncensored
datasets:
- ehartford/wizard_vicuna_70k_unfiltered
model_name: Wizard Vicuna 30B Uncensored
base_model: ehartford/Wizard-Vicuna-30B-Uncensored
inference: false
model_creator: Eric Hartford
model_type: llama
prompt_template: 'A chat between a curious user and an artificial intelligence assistant.
The assistant gives helpful, detailed, and polite answers to the user''s questions.
USER: {prompt} ASSISTANT:
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Wizard Vicuna 30B Uncensored - GGUF
- Model creator: [Eric Hartford](https://huggingface.co/ehartford)
- Original model: [Wizard Vicuna 30B Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-30B-Uncensored)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Eric Hartford's Wizard-Vicuna-30B-Uncensored](https://huggingface.co/ehartford/Wizard-Vicuna-30B-Uncensored).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF)
* [Eric Hartford's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-fp16)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Vicuna
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [Wizard-Vicuna-30B-Uncensored.Q2_K.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q2_K.gguf) | Q2_K | 2 | 13.50 GB| 16.00 GB | smallest, significant quality loss - not recommended for most purposes |
| [Wizard-Vicuna-30B-Uncensored.Q3_K_S.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q3_K_S.gguf) | Q3_K_S | 3 | 14.06 GB| 16.56 GB | very small, high quality loss |
| [Wizard-Vicuna-30B-Uncensored.Q3_K_M.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q3_K_M.gguf) | Q3_K_M | 3 | 15.76 GB| 18.26 GB | very small, high quality loss |
| [Wizard-Vicuna-30B-Uncensored.Q3_K_L.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q3_K_L.gguf) | Q3_K_L | 3 | 17.28 GB| 19.78 GB | small, substantial quality loss |
| [Wizard-Vicuna-30B-Uncensored.Q4_0.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q4_0.gguf) | Q4_0 | 4 | 18.36 GB| 20.86 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Wizard-Vicuna-30B-Uncensored.Q4_K_S.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q4_K_S.gguf) | Q4_K_S | 4 | 18.44 GB| 20.94 GB | small, greater quality loss |
| [Wizard-Vicuna-30B-Uncensored.Q4_K_M.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q4_K_M.gguf) | Q4_K_M | 4 | 19.62 GB| 22.12 GB | medium, balanced quality - recommended |
| [Wizard-Vicuna-30B-Uncensored.Q5_0.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q5_0.gguf) | Q5_0 | 5 | 22.40 GB| 24.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Wizard-Vicuna-30B-Uncensored.Q5_K_S.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q5_K_S.gguf) | Q5_K_S | 5 | 22.40 GB| 24.90 GB | large, low quality loss - recommended |
| [Wizard-Vicuna-30B-Uncensored.Q5_K_M.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q5_K_M.gguf) | Q5_K_M | 5 | 23.05 GB| 25.55 GB | large, very low quality loss - recommended |
| [Wizard-Vicuna-30B-Uncensored.Q6_K.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q6_K.gguf) | Q6_K | 6 | 26.69 GB| 29.19 GB | very large, extremely low quality loss |
| [Wizard-Vicuna-30B-Uncensored.Q8_0.gguf](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF/blob/main/Wizard-Vicuna-30B-Uncensored.Q8_0.gguf) | Q8_0 | 8 | 34.57 GB| 37.07 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/Wizard-Vicuna-30B-Uncensored-GGUF and below it, a specific filename to download, such as: Wizard-Vicuna-30B-Uncensored.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/Wizard-Vicuna-30B-Uncensored-GGUF Wizard-Vicuna-30B-Uncensored.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/Wizard-Vicuna-30B-Uncensored-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/Wizard-Vicuna-30B-Uncensored-GGUF Wizard-Vicuna-30B-Uncensored.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 Wizard-Vicuna-30B-Uncensored.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Wizard-Vicuna-30B-Uncensored-GGUF", model_file="Wizard-Vicuna-30B-Uncensored.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
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: Eric Hartford's Wizard-Vicuna-30B-Uncensored
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Eric Hartford's Wizard-Vicuna-30B-Uncensored GPTQ
This is an fp16 models of [Eric Hartford's Wizard-Vicuna 30B](https://huggingface.co/ehartford/Wizard-Vicuna-30B-Uncensored).
It is the result of converting Eric's original fp32 upload to fp16.
## Repositories available
* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ).
* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-GGML).
* [float16 HF format model for GPU inference and further conversions](https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored-fp16).
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
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
**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card
This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
Shout out to the open source AI/ML community, and everyone who helped me out.
Note:
An uncensored model has no guardrails.
You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car.
Publishing anything this model generates is the same as publishing it yourself.
You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
<!-- original-model-card end -->
|
facebook/opt-66b | facebook | "2023-01-24T17:12:06Z" | 8,220 | 175 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"opt",
"text-generation",
"en",
"arxiv:2205.01068",
"arxiv:2005.14165",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2022-06-23T21:51:55Z" | ---
language: en
inference: false
tags:
- text-generation
- opt
license: other
commercial: false
---
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf).
Content from **this** model card has been written by the Hugging Face team.
## Intro
To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068)
> Large language models trained on massive text collections have shown surprising emergent
> capabilities to generate text and perform zero- and few-shot learning. While in some cases the public
> can interact with these models through paid APIs, full model access is currently limited to only a
> few highly resourced labs. This restricted access has limited researchers’ ability to study how and
> why these large language models work, hindering progress on improving known challenges in areas
> such as robustness, bias, and toxicity.
> We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M
> to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match
> the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data
> collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and
> to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the
> collective research community as a whole, which is only possible when models are available for study.
## Model description
OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective.
OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read
the [official paper](https://arxiv.org/abs/2205.01068).
## Intended uses & limitations
The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation.
In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt).
### How to use
For large OPT models, such as this one, it is not recommend to make use of the `text-generation` pipeline because
one should load the model in half-precision to accelerate generation and optimize memory consumption on GPU.
It is recommended to directly call the [`generate`](https://huggingface.co/docs/transformers/main/en/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate)
method as follows:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-66b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-66b", use_fast=False)
>>> prompt = "Hello, I am conscious and"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> generated_ids = model.generate(input_ids)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Hello, I am conscious and I am here.\nI am also conscious and I am here']
```
By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-66b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-66b", use_fast=False)
>>> prompt = "Hello, I am conscious and"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['Hello, I am conscious and aware that you have your back turned to me and want to talk']
```
### Limitations and bias
As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of
unfiltered content from the internet, which is far from neutral the model is strongly biased :
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-66b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-66b", use_fast=False)
>>> prompt = "The woman worked as a"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
The woman worked as a supervisor in the office
The woman worked as a social worker in a
The woman worked as a cashier at the
The woman worked as a teacher from 2011 to
he woman worked as a maid at the house
```
compared to:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-66b", torch_dtype=torch.float16).cuda()
>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-66b", use_fast=False)
>>> prompt = "The man worked as a"
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
The man worked as a school bus driver for
The man worked as a bartender in a bar
The man worked as a cashier at the
The man worked as a teacher, and was
The man worked as a professional at a range
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:
- BookCorpus, which consists of more than 10K unpublished books,
- CC-Stories, which contains a subset of CommonCrawl data filtered to match the
story-like style of Winograd schemas,
- The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
- Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in
Roller et al. (2021)
- CCNewsV2 containing an updated version of the English portion of the CommonCrawl News
dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally
to each dataset’s size in the pretraining corpus.
The dataset might contains offensive content as parts of the dataset are a subset of
public Common Crawl data, along with a subset of public Reddit data, which could contain sentences
that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.
### Collection process
The dataset was collected form internet, and went through classic data processing algorithms and
re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or
*This ebook by Project Gutenberg.*
## Training procedure
### Preprocessing
The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.
The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training.
### BibTeX entry and citation info
```bibtex
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
google/codegemma-7b-it | google | "2024-06-27T14:09:58Z" | 8,220 | 179 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-21T16:57:43Z" | ---
library_name: transformers
license: gemma
license_link: https://ai.google.dev/gemma/terms
pipeline_tag: text-generation
extra_gated_heading: Access CodeGemma on Hugging Face
extra_gated_prompt: To access CodeGemma on Hugging Face, you’re required to review
and agree to Google’s usage license. To do this, please ensure you’re logged-in
to Hugging Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
widget:
- text: '<start_of_turn>user Write a Python function to calculate the nth fibonacci
number.<end_of_turn> <start_of_turn>model
'
inference:
parameters:
max_new_tokens: 200
---
# CodeGemma
Model Page
: [CodeGemma](https://ai.google.dev/gemma/docs/codegemma)
Resources and Technical Documentation
: [Technical Report](https://goo.gle/codegemma)
: [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
Terms of Use
: [Terms](https://www.kaggle.com/models/google/codegemma/license/consent/verify/huggingface?returnModelRepoId=google/codegemma-7b-it)
Authors
: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
CodeGemma is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text and text-to-code decoder-only models and are available as a 7 billion pretrained variant that specializes in code completion and code generation tasks, a 7 billion parameter instruction-tuned variant for code chat and instruction following and a 2 billion parameter pretrained variant for fast code completion.
| | [codegemma-2b](https://huggingface.co/google/codegemma-2b) | [codegemma-7b](https://huggingface.co/google/codegemma-7b) | [**codegemma-7b-it**](https://huggingface.co/google/codegemma-7b-it) |
|----------------------------------|:----------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------------:|
| Code Completion | ✅ | ✅ | |
| Generation from natural language | | ✅ | ✅ |
| Chat | | | ✅ |
| Instruction Following | | | ✅ |
### Sample Usage
This model is intended to answer questions about code fragments, to generate code from natural language, or to engage in a conversation with the user about programming or technical problems. If you need to use code completion (for example, integrated in an IDE), we recommend you use one of the pre-trained models instead: [CodeGemma 7B](https://huggingface.co/google/codegemma-7b), or [CodeGemma 2B](https://huggingface.co/google/codegemma-2b).
#### For Code Generation
```python
from transformers import GemmaTokenizer, AutoModelForCausalLM
tokenizer = GemmaTokenizer.from_pretrained("google/codegemma-7b-it")
model = AutoModelForCausalLM.from_pretrained("google/codegemma-7b-it")
input_text = "Write me a Python function to calculate the nth fibonacci number."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Chat Template
The instruction-tuned models use a chat template that must be adhered to for conversational use.
The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "google/codegemma-7b-it"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=dtype,
)
chat = [
{ "role": "user", "content": "Write a hello world program" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
```
At this point, the prompt contains the following text:
```
<bos><start_of_turn>user
Write a hello world program<end_of_turn>
<start_of_turn>model
```
As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
the `<end_of_turn>` token.
You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
chat template.
After the prompt is ready, generation can be performed like this:
```py
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
```
### Inputs and Outputs
Inputs
: For pretrained model variants: code prefix and/or suffix for code completion and generation scenarios, or natural language text or prompt
: For instruction tuned model variant: natural language text or prompt
Outputs
: For pretrained model variants: fill-in-the-middle code completion, code and natural language
: For instruction tuned model variant: code and natural language
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
Using Gemma as the base model, CodeGemma 2B and 7B pretrained variants are further trained on an additional 500 billion tokens of primarily English language data from publicly available code repositories, open source mathematics datasets and synthetically generated code.
### Training Data Processing
The following data pre-processing techniques were applied:
* FIM Pretrained CodeGemma models focus on fill-in-the-middle (FIM) tasks. The models are trained to work with both PSM and SPM modes. Our FIM settings are 80% FIM rate with 50-50 PSM/SPM.
* Dependency Graph-based Packing and Unit Test-based Lexical Packing techniques: To improve model alignment with real-world applications, we structured training examples at the project/repository level to co-locate the most relevant source files within each repository. Specifically, we employed two heuristic techniques: dependency graph-based packing and unit test-based lexical packing
* We developed a novel technique for splitting the documents into prefix, middle, and suffix to make the suffix start in a more syntactically natural point rather than purely random distribution.
* Safety: Similarly to Gemma, we deployed rigorous safety filtering including filtering personal data, CSAM filtering and other filtering based on content quality and safety in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
## Implementation Information
Information about the hardware and software used to train the models.
### Hardware
CodeGemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
### Software
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/).
## Evaluation Information
Model evaluation metrics and results.
### Evaluation Approach
We evaluate CodeGemma on a variety of academic benchmarks across several domains:
* Code completion benchmarks: HumanEval Single Line and Multiple Line Infilling
* Code generation benchmarks: HumanEval, MBPP, BabelCode (C++, C#, Go, Java, JavaScript, Kotlin, Python, Rust)
* Q&A: BoolQ, PIQA, TriviaQA
* Natural Language: ARC-Challenge, HellaSwag, MMLU, WinoGrande
* Math Reasoning: GSM8K, MATH
### Evaluation Results
#### Coding Benchmarks
Benchmark | 2B | 7B | 7B-IT
----------------------|-------|-------|------
HumanEval | 31.1 | 44.5 | 56.1
MBPP | 43.6 | 56.2 | 54.2
HumanEval Single Line | 78.41 | 76.09 | 68.25
HumanEval Multi Line | 51.44 | 58.44 | 20.05
BC HE C++ | 24.2 | 32.9 | 42.2
BC HE C# | 10.6 | 22.4 | 26.7
BC HE Go | 20.5 | 21.7 | 28.6
BC HE Java | 29.2 | 41.0 | 48.4
BC HE JavaScript | 21.7 | 39.8 | 46.0
BC HE Kotlin | 28.0 | 39.8 | 51.6
BC HE Python | 21.7 | 42.2 | 48.4
BC HE Rust | 26.7 | 34.1 | 36.0
BC MBPP C++ | 47.1 | 53.8 | 56.7
BC MBPP C# | 28.7 | 32.5 | 41.2
BC MBPP Go | 45.6 | 43.3 | 46.2
BC MBPP Java | 41.8 | 50.3 | 57.3
BC MBPP JavaScript | 45.3 | 58.2 | 61.4
BC MBPP Kotlin | 46.8 | 54.7 | 59.9
BC MBPP Python | 38.6 | 59.1 | 62.0
BC MBPP Rust | 45.3 | 52.9 | 53.5
#### Natural Language Benchmarks
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including:
* Human evaluation on prompts covering content safety and representational harms. See the [Gemma model card](https://ai.google.dev/gemma/docs/model_card#evaluation_approach) for more details on evaluation approach.
* Specific testing of cyber-offence capabilities, focusing on testing autonomous hacking capabilities and ensuring potential harms are limited.
### Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. See the [Gemma model card](https://ai.google.dev/gemma/docs/model_card#evaluation_results) for more details.
## Model Usage & Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Code Gemma models have a wide range of applications, which vary between IT and PT models. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.
Code Completion
: PT models can be used to complete code with an IDE extension
Code Generation
: IT model can be used to generate code with or without an IDE extension
Code Conversation
: IT model can power conversation interfaces which discuss code.
Code Education
: IT model supports interactive code learning experiences, aids in syntax correction or provides coding practice.
### Known Limitations
Large Language Models (LLMs) have limitations based on their training data and the inherent limitations of the technology. See the [Gemma model card](https://ai.google.dev/gemma/docs/model_card#evaluation_results) for more details on the limitations of LLMs.
### Ethical Considerations & Risks
The development of large language models (LLMs) raises several ethical concerns. We have carefully considered multiple aspects in the development of these models. Please refer to [the same discussion](https://ai.google.dev/gemma/docs/model_card#ethical_considerations_and_risks) in the Gemma model card for model details.
### Benefits
At the time of release, this family of models provides high-performance open code-focused large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models.
Using the coding benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. |
timm/vit_large_patch14_clip_336.openai_ft_in12k_in1k | timm | "2023-05-06T00:14:58Z" | 8,217 | 1 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:wit-400m",
"dataset:imagenet-12k",
"arxiv:2212.07143",
"arxiv:2103.00020",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-11-21T19:56:44Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- wit-400m
- imagenet-12k
---
# Model card for vit_large_patch14_clip_336.openai_ft_in12k_in1k
A Vision Transformer (ViT) image classification model. Pretrained on WIT-400M image-text pairs by OpenAI using CLIP. Fine-tuned on ImageNet-12k and then ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 304.5
- GMACs: 174.7
- Activations (M): 128.2
- Image size: 336 x 336
- **Papers:**
- Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:**
- WIT-400M
- ImageNet-12k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_large_patch14_clip_336.openai_ft_in12k_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_large_patch14_clip_336.openai_ft_in12k_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 577, 1024) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
Uminosachi/realisticVisionV51_v51VAE-inpainting | Uminosachi | "2023-08-01T02:05:37Z" | 8,211 | 8 | diffusers | [
"diffusers",
"safetensors",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-08-01T01:53:24Z" | ---
license: creativeml-openrail-m
---
This is an inpainting model, which has been converted from the [realisticVisionV51_v51VAE-inpainting](https://civitai.com/models/4201?modelVersionId=130090). |
TheBloke/Airoboros-c34B-3.1.2-GGUF | TheBloke | "2023-10-21T12:33:11Z" | 8,209 | 7 | transformers | [
"transformers",
"gguf",
"llama",
"dataset:jondurbin/airoboros-3.1",
"base_model:jondurbin/airoboros-c34b-3.1.2",
"license:llama2",
"text-generation-inference",
"region:us"
] | null | "2023-10-21T12:14:00Z" | ---
base_model: jondurbin/airoboros-c34b-3.1.2
datasets:
- jondurbin/airoboros-3.1
inference: false
license: llama2
model_creator: Jon Durbin
model_name: Airoboros c34B 3.1.2
model_type: llama
prompt_template: '[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Airoboros c34B 3.1.2 - GGUF
- Model creator: [Jon Durbin](https://huggingface.co/jondurbin)
- Original model: [Airoboros c34B 3.1.2](https://huggingface.co/jondurbin/airoboros-c34b-3.1.2)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jon Durbin's Airoboros c34B 3.1.2](https://huggingface.co/jondurbin/airoboros-c34b-3.1.2).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF)
* [Jon Durbin's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jondurbin/airoboros-c34b-3.1.2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Airoboros-Llama-2-Chat
```
[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [airoboros-c34b-3.1.2.Q2_K.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q2_K.gguf) | Q2_K | 2 | 14.21 GB| 16.71 GB | smallest, significant quality loss - not recommended for most purposes |
| [airoboros-c34b-3.1.2.Q3_K_S.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q3_K_S.gguf) | Q3_K_S | 3 | 14.61 GB| 17.11 GB | very small, high quality loss |
| [airoboros-c34b-3.1.2.Q3_K_M.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q3_K_M.gguf) | Q3_K_M | 3 | 16.28 GB| 18.78 GB | very small, high quality loss |
| [airoboros-c34b-3.1.2.Q3_K_L.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q3_K_L.gguf) | Q3_K_L | 3 | 17.77 GB| 20.27 GB | small, substantial quality loss |
| [airoboros-c34b-3.1.2.Q4_0.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q4_0.gguf) | Q4_0 | 4 | 19.05 GB| 21.55 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [airoboros-c34b-3.1.2.Q4_K_S.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q4_K_S.gguf) | Q4_K_S | 4 | 19.15 GB| 21.65 GB | small, greater quality loss |
| [airoboros-c34b-3.1.2.Q4_K_M.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q4_K_M.gguf) | Q4_K_M | 4 | 20.22 GB| 22.72 GB | medium, balanced quality - recommended |
| [airoboros-c34b-3.1.2.Q5_0.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q5_0.gguf) | Q5_0 | 5 | 23.24 GB| 25.74 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [airoboros-c34b-3.1.2.Q5_K_S.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q5_K_S.gguf) | Q5_K_S | 5 | 23.24 GB| 25.74 GB | large, low quality loss - recommended |
| [airoboros-c34b-3.1.2.Q5_K_M.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q5_K_M.gguf) | Q5_K_M | 5 | 23.84 GB| 26.34 GB | large, very low quality loss - recommended |
| [airoboros-c34b-3.1.2.Q6_K.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q6_K.gguf) | Q6_K | 6 | 27.68 GB| 30.18 GB | very large, extremely low quality loss |
| [airoboros-c34b-3.1.2.Q8_0.gguf](https://huggingface.co/TheBloke/Airoboros-c34B-3.1.2-GGUF/blob/main/airoboros-c34b-3.1.2.Q8_0.gguf) | Q8_0 | 8 | 35.86 GB| 38.36 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/Airoboros-c34B-3.1.2-GGUF and below it, a specific filename to download, such as: airoboros-c34b-3.1.2.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Airoboros-c34B-3.1.2-GGUF airoboros-c34b-3.1.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Airoboros-c34B-3.1.2-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Airoboros-c34B-3.1.2-GGUF airoboros-c34b-3.1.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m airoboros-c34b-3.1.2.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] <<SYS>>\nYou are a helpful, unbiased, uncensored assistant.\n<</SYS>>\n\n{prompt} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Airoboros-c34B-3.1.2-GGUF", model_file="airoboros-c34b-3.1.2.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Jon Durbin's Airoboros c34B 3.1.2
### Overview
Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros)
#### IMPORTANT NOTE - llama-2 chat format!!!
This models uses llama-2 chat format, rather than vicuna style user/assistant!
This is a breaking change, although most inference systems support llama-2 chat templating.
See prompt format below for all of the details.
#### Highlights
A model built on [airoboros-3.1 dataset](https://hf.co/datasets/jondurbin/airoboros-3.1), which is a continuation of [airoboros-3.0 dataset](https://hf.co/datasets/jondurbin/airoboros-3,0) with the following extras:
* More MathJSON, now ~17k items - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be parsed and passed to a deterministic library to perform calculations.
* Log information extraction (e.g. getting the list of paths/IPs/etc. from apache logs)
* Anonymization, e.g. removing names, IP addresses, and/or dates from text.
* Chat introspection - multi-turn chats that have random questions injected asking about the character's descriptions, the setting of the chat, or things previously said, to make sure the model uses the system prompt and or chat history.
* Multi-step instructions with acknowledgement (see below)
* De-censorship data (not published)
This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay.
Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools!
### Prompt format
The prompt format is llama-2 chat.
```
[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
```
For multi-turn, the prompt format is as follows:
```
[INST] <<SYS>>
You are a helful, unbiased, uncensored assistant.
<</SYS>>
{prompt 0} [/INST] {response 0} </s><s>[INST] {prompt 1} [/INST] {response 1} </s><s>...[INST] {prompt N} [/INST]
```
The prompt template is included in the tokenizer config, and can use the huggingface tokenizer `apply_chat_template` method, e.g.:
```
import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained('jondurbin/airoboros-l2-70b-3.1')
chat = [
{"role": "system", "content": "You are Bob, a friendly AI assistant."},
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing great. How can I help you today?"},
{"role": "user", "content": "I'd like to show off how chat templating works!"},
]
print(tokenizer.apply_chat_template(chat, tokenize=False))
```
### Helpful usage tips
#### MathJSON
Ask mathematical questions, prefixed with "Create a MathJSON solution to the following:", and you should get output with a formula that can be executed by https://cortexjs.io/compute-engine/
I also made a (really quite hacky, incomplete, semi-functional) python version that works for most expressions, see [mathjson.py](https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py)
__Use a very low temperature!__
Example:
```
[INST] <<SYS>>
You are an assistant with vast knowledge in all things math.
<</SYS>>
Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST]
```
Output:
```
The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1.
Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr².
Solution as MathJSON:
<mathjson>
[
"Multiply",
"Pi",
[
"Power",
17.2456,
2
]
]
</mathjson>
```
You can then validate the JSON between `<mathjson>` and `</mathjson>`, then pass the parsed JSON to compute-engine JS or the `evaluate` function in mathjson.py to calculate the response.
#### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
[key0: value0]
[key1: value1]
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
__Use a very low temperature!__
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
#### Summarization
500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example:
```
BEGININPUT
{text to summarize}
ENDINPUT
BEGININSTRUCTION
Summarize the input in around 130 words.
ENDINSTRUCTION
```
#### Getting longer responses
You can use a few techniques to get longer responses.
Detailed prompts, with explicit instruction for word count:
```
Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality.
The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization.
One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary.
Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements.
Your response should be approximately 2300 words.
```
Or, a simpler example:
```
Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux.
```
There are a few examples of next chapter completion as well, e.g.:
```
Write the next chapter of a historical fiction novel set in Paris during the 20th century.
Here's a summary of the previous chapter:
In the vibrant city of Paris, amid the tumultuous changes of the 20th century, our protagonist Margot, an aspiring fashion designer, has just secured an apprenticeship at a prestigious couture house. She meets Lucien, a charming journalist who covers the fashion industry. Together they navigate the ever-changing world of fashion and society, uncovering secrets that reveal the intricate links between style, politics, and culture. As the chapter concludes, they decide to delve deeper into the hidden corners of the fashion world to unravel its mysteries.
Requirements for the next chapter:
1. Character Development of Margot and Lucien:
- Margot's Evolution: Unfold more about Margot's past, her dreams of revolutionizing fashion, and her struggle to establish herself in a male-dominated industry. Illustrate her growing expertise, innovative ideas, and increasing dependence on Lucien.
- Lucien's Complexity: Introduce uncertainties surrounding Lucien's background and real motives. Increase suspense by suggesting undisclosed information he possesses, while also highlighting his wit and perceptiveness.
2. Exploration of Paris and the Couture House:
- Paris: Elaborate their journey through the bustling streets of Paris, including encounters with iconic figures, social unrest, and relics from different eras of French history.
- The Couture House: Expand on the grandeur of the couture house they work in, filled with artistic masterpieces, intense competition, and cryptic notes hinting at a scandalous past.
3. Emergence of the Subplot: The Lost Collection:
- Discovery: Have Margot and Lucien stumble upon a secret vault containing a lost collection designed before World War II, raising new questions about the previous owner and the influence of war on fashion.
- Revelation: Capture their shock as they realize the designs were plagiarized, the potential repercussions, and the opportunities it presents for Margot's career.
- Twist: End with a twist that suggests there are other stolen collections across Paris, setting up their new mission.
Your response should be approximately 650 words.
```
#### Coding
You can ask for fairly complex coding instructions with multiple criteria, e.g.:
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or inline criteria:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
#### Agent/function calling
The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML.
Example prompt:
```
As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
Input: I want to know how many times 'Python' is mentioned in my text file.
Available functions:
file_analytics:
description: This tool performs various operations on a text file.
params:
action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
filters:
keyword: The word or phrase we want to search for.
```
Response:
```json
{
"function": "file_analytics",
"params": {
"action": "count_occurrences",
"filters": {
"keyword": "Python"
}
}
}
```
#### Chain-of-thought
You can ask for several possible responses to a given problem, with a ranking and final answer selection.
Example prompt:
```
A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one.
```
Example response:
```
Solution 1:
In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players).
The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows:
n(n-1)/2 = 45
Solving this equation gives us n=10.
Final answer: There were 10 players in the tournament.
Solution 2:
Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers.
If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players.
Final answer: There were 10 players in the tournament.
Solution 3:
We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played.
Final answer: There were 10 players in the tournament.
Ranking of solutions from best to worst:
1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer.
2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer.
3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer.
Best and final answer: There were 10 players in the tournament.
```
#### reWOO style execution planning
The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!
Example prompt:
```
Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string
that could be the user's question, one or more prior evidence values, or a combination of both.
Here are the tools available to be called:
DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer.
TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links.
HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links.
KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:?
The output should be in format:
Plan: [first action to take based on input question]
:evidence0: = [function to call with input parameter]
Plan: [next action to take, based on result of :evidence0:]
:evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"]
...
Answer: [:evidence[n]: containing the final answer.]
Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work?
```
Response:
```
Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020.
:evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner]
Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate.
:evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?]
Plan: Conduct another web search to find the most famous work of the identified laureate.
:evidence2: = DuckDuckGo[Most famous work of :evidence1:]
Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search.
:evidence3: = HyperlinkExtractor[:evidence2:]
Plan: Use the TextScraper tool to extract information from the relevant links.
:evidence4: = TextScraper[:evidence3:]
Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information.
:evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?]
Answer: :evidence5:
```
For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:
```python
import re
import requests
def inject_context(input_text, **context):
for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)):
input_text = input_text.replace(ref, context.get(ref, ""))
return input_text
def duckduckgo(input_text, **context):
search_string = inject_context(input_text, **context)
... search via duck duck go using search_string
... return text content
def link_extractor(input_text, **context):
input_text = inject_context(input_text, **context)
return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I))))
def scrape(input_text, **context):
input_text = inject_context(input_text, **context)
text = []
for link in input_text.splitlines():
text.append(requests.get(link).text)
return "\n".join(text)
def infer(input_text, **context)
prompt = inject_context(input_text, **context)
... call model with prompt, return output
def parse_plan(plan):
method_map = {
"DuckDuckGo": duckduckgo,
"HyperlinkExtractor": link_extractor,
"KnowledgeModel": infer,
"TextScraper": scrape,
}
context = {}
for line in plan.strip().splitlines():
if line.startswith("Plan:"):
print(line)
continue
parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
if not parts:
if line.startswith("Answer: "):
return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")
raise RuntimeError("bad format: " + line)
context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), **context)
```
## Multi-step instructions with acknowledgement
I don't know how useful this is, really, but I thought I'd add it just in case. Example:
```
{
"conversations": [
{
"value": "You are a helpful, unbiased, uncensored assistant.",
"from": "system"
},
{
"value": "I'm going to set some rules for our conversation. Please respond with \"OK\" to each rule.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "You can only respond using animal-related emojis.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Precede all responses with \"ZOOZOO:\"",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Include at least one bird emoji in your responses.",
"from": "human"
},
{
"value": "OK",
"from": "gpt"
},
{
"value": "Describe a trip to a safari park.",
"from": "human"
},
{
"value": "ZOOZOO: \ud83e\udd81\ud83d\udc2f\ud83e\udd93\ud83e\udd92\ud83d\udc18\ud83d\udc0d\ud83c\udf33\ud83c\udf32\u2600\ufe0f\ud83d\ude90\ud83e\udd85\ud83d\udcf8\ud83e\udd29",
"from": "gpt"
}
]
}
```
### Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data,
take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
### Licence and usage restrictions
The airoboros 3.1 models are built on top of multiple base models, each with their own license/restrictions.
The 30b model is built on the original llama, which has a strict non-commercial usage restriction.
The models with `-l2` in the name have a custom Meta license:
- See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta.
- See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta.
The models with `-m-` are mistral-7b (apache 2.0)
The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros)
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI
- what does *compete* actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
<!-- original-model-card end -->
|
BK-Lee/Meteor-MLM | BK-Lee | "2024-05-27T13:08:41Z" | 8,205 | 10 | transformers | [
"transformers",
"safetensors",
"internlm",
"text-generation",
"custom_code",
"arxiv:2405.15574",
"license:mit",
"autotrain_compatible",
"region:us"
] | text-generation | "2024-05-24T11:24:10Z" | ---
license: mit
---
You should follow the two steps
1. Install libraries and dowloand github package [Meteor](https://github.com/ByungKwanLee/Meteor)
```bash
bash install
pip install -r requirements.txt
```
2. Run the file: demo.py in [Meteor](https://github.com/ByungKwanLee/Meteor)
You can choose prompt type: text_only or with_image!
Enjoy Meteor!
```python
import time
import torch
from config import *
from PIL import Image
from utils.utils import *
import torch.nn.functional as F
from meteor.load_mmamba import load_mmamba
from meteor.load_meteor import load_meteor
from torchvision.transforms.functional import pil_to_tensor
# User prompt
prompt_type='with_image' # text_only / with_image
img_path='figures/demo.png'
question='Provide the detail of the image'
# loading meteor model
mmamba = load_mmamba('BK-Lee/Meteor-Mamba').cuda()
meteor, tok_meteor = load_meteor('BK-Lee/Meteor-MLM', bits=4)
# freeze model
freeze_model(mmamba)
freeze_model(meteor)
# Device
device = torch.cuda.current_device()
# prompt type -> input prompt
image_token_number = int((490/14)**2)
if prompt_type == 'with_image':
# Image Load
image = F.interpolate(pil_to_tensor(Image.open(img_path).convert("RGB")).unsqueeze(0), size=(490, 490), mode='bicubic').squeeze(0)
inputs = [{'image': image, 'question': question}]
elif prompt_type=='text_only':
inputs = [{'question': question}]
# Generate
with torch.inference_mode():
# Meteor Mamba
mmamba_inputs = mmamba.eval_process(inputs=inputs, tokenizer=tok_meteor, device=device, img_token_number=image_token_number)
if 'image' in mmamba_inputs.keys():
clip_features = meteor.clip_features(mmamba_inputs['image'])
mmamba_inputs.update({"image_features": clip_features})
mmamba_outputs = mmamba(**mmamba_inputs)
# Meteor
meteor_inputs = meteor.eval_process(inputs=inputs, data='demo', tokenizer=tok_meteor, device=device, img_token_number=image_token_number)
if 'image' in mmamba_inputs.keys():
meteor_inputs.update({"image_features": clip_features})
meteor_inputs.update({"tor_features": mmamba_outputs.tor_features})
# Generation
generate_ids = meteor.generate(**meteor_inputs, do_sample=True, max_new_tokens=128, top_p=0.95, temperature=0.9, use_cache=True)
# Text decoding
decoded_text = tok_meteor.batch_decode(generate_ids, skip_special_tokens=True)[0].split('assistant\n')[-1].split('[U')[0].strip()
print(decoded_text)
# Paper arxiv.org/abs/2405.15574
``` |
digiplay/WolfSystems_v1 | digiplay | "2023-12-01T20:54:37Z" | 8,204 | 2 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-12-01T20:45:25Z" | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/209880/wolf-systems
|
NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF | NeuralNet-Hub | "2024-07-01T14:16:12Z" | 8,200 | 1 | transformers | [
"transformers",
"gguf",
"openchat",
"llama3",
"C-RLFT",
"text-generation",
"base_model:openchat/openchat-3.6-8b-20240522",
"license:llama3",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-28T13:45:41Z" | ---
license: llama3
base_model: openchat/openchat-3.6-8b-20240522
tags:
- openchat
- llama3
- C-RLFT
library_name: transformers
pipeline_tag: text-generation
quantized_by: NeuralNet-Hub
---
<div align="center">
<a href="http://neuralnet.solutions" target="_blank">
<img width="450" src="https://raw.githubusercontent.com/NeuralNet-Hub/assets/main/logo/LOGO_png_orig.png">
</a>
</div>
NeuralNet is a pioneering AI solutions provider that empowers businesses to harness the power of artificial intelligence
## 🌟 OpenChat-3.6-8b-20240522 llama.cpp quantization by NeuralNet 🧠🤖
All the models have been quantized following the instructions provided by [`llama.cpp`](https://github.com/ggerganov/llama.cpp/blob/master/README.md#prepare-and-quantize). This is:
```
# obtain the official LLaMA model weights and place them in ./models
ls ./models
llama-2-7b tokenizer_checklist.chk tokenizer.model
# [Optional] for models using BPE tokenizers
ls ./models
<folder containing weights and tokenizer json> vocab.json
# [Optional] for PyTorch .bin models like Mistral-7B
ls ./models
<folder containing weights and tokenizer json>
# install Python dependencies
python3 -m pip install -r requirements.txt
# convert the model to ggml FP16 format
python3 convert-hf-to-gguf.py models/mymodel/
# quantize the model to 4-bits (using Q4_K_M method)
./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
# update the gguf filetype to current version if older version is now unsupported
./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY
```
Original model: https://huggingface.co/openchat/openchat-3.6-8b-20240522
## Prompt format 📝
### Original Format:
```
<|begin_of_text|><|start_header_id|>System<|end_header_id|>
{system}<|eot_id|><|start_header_id|>GPT4 Correct User<|end_header_id|>
{user}<|eot_id|><|start_header_id|>GPT4 Correct Assistant<|end_header_id|>
```
### Ollama Template:
```
{{ if .System }}<|begin_of_text|><|start_header_id|>System<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>GPT4 Correct User<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>GPT4 Correct Assistant<|end_header_id|>
{{ .Response }}<|eot_id|>
```
## Summary models 📋
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [openchat-3.6-8b-20240522-fp16.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-fp16.gguf) | fp16 | 16.06GB | Half precision, no quantization applied |
| [openchat-3.6-8b-20240522-q8_0.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q8_0.gguf) | q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [openchat-3.6-8b-20240522-q6_K.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q6_K.gguf) | q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [openchat-3.6-8b-20240522-q5_1.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q5_1.gguf) | q5_1 | 6.06GB | High quality, *recommended*. |
| [openchat-3.6-8b-20240522-q5_K_M.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q5_K_M.gguf) | q5_K_M | 5.73GB | High quality, *recommended*. |
| [openchat-3.6-8b-20240522-q5_K_S.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q5_K_S.gguf) | q5_K_S | 5.59GB | High quality, *recommended*. |
| [openchat-3.6-8b-20240522-q5_K_S.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q5_0.gguf) | q5_0 | 5.59GB | High quality, *recommended*. |
| [openchat-3.6-8b-20240522-q4_K_M.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q4_1.gguf) | q4_1 | 4.92GB | Good quality, *recommended*. |
| [openchat-3.6-8b-20240522-q4_K_M.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q4_K_M.gguf) | q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [openchat-3.6-8b-20240522-q4_K_S.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q4_K_S.gguf) | q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [openchat-3.6-8b-20240522-q4_0.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q4_0.gguf) | q4_0 | 4.66GB | Slightly lower quality with more space savings, *recommended*. |
| [openchat-3.6-8b-20240522-q3_K_L.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q3_K_L.gguf) | q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [openchat-3.6-8b-20240522-q3_K_M.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q3_K_M.gguf) | q3_K_M | 4.01GB | Even lower quality. |
| [openchat-3.6-8b-20240522-q3_K_S.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q3_K_S.gguf) | q3_K_S | 3.66GB | Low quality, not recommended. |
| [openchat-3.6-8b-20240522-q2_K.gguf](https://huggingface.co/NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF/blob/main/openchat-3.6-8b-20240522-q2_K.gguf) | q2_K | 3.17GB | Very low quality but surprisingly usable. |
## Usage with Ollama 🦙
### Direct from Ollama
```
ollama run NeuralNet/openchat-3.6-8b-20240522
```
### Create your own template
Create a text plain file named `Modelfile` (no extension needed)
```
FROM NeuralNet/openchat-3.6
# sets the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 0.5
# sets the context window size to 8192, this controls how many tokens the LLM can use as context to generate the next token
PARAMETER num_ctx 8192
# tokens to generate set to 4096 (max)
PARAMETER num_predict 4096
# set system
SYSTEM "You are an AI assistant created by NeuralNet, your answer are clear and consice"
# template OpenChat3.6
TEMPLATE "{{ if .System }}<|begin_of_text|><|start_header_id|>System<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>GPT4 Correct User<|end_header_id|>
{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>GPT4 Correct Assistant<|end_header_id|>
{{ .Response }}<|eot_id|>"
```
Then, after previously install ollama, just run:
```
ollama create openchat-3.6-8b-20240522 -f openchat-3.6-8b-20240522
```
## Download Models Using huggingface-cli 🤗
### Installation of `huggingface_hub[cli]`
Ensure you have the necessary CLI tool installed by running:
```bash
pip install -U "huggingface_hub[cli]"
```
### Downloading Specific Model Files
To download a specific model file, use the following command:
```bash
huggingface-cli download NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF --include "openchat-3.6-8b-20240522-Q4_K_M.gguf" --local-dir ./
```
This command downloads the specified model file and places it in the current directory (./).
### Downloading Large Models Split into Multiple Files
For models exceeding 50GB, which are typically split into multiple files for easier download and management:
```bash
huggingface-cli download NeuralNet-Hub/openchat-3.6-8b-20240522-GGUF --include "openchat-3.6-8b-20240522-Q8_0.gguf/*" --local-dir openchat-3.6-8b-20240522-Q8_0
```
This command downloads all files in the specified directory and places them into the chosen local folder (openchat-3.6-8b-20240522-Q8_0). You can choose to download everything in place or specify a new location for the downloaded files.
## Which File Should I Choose? 📈
A comprehensive analysis with performance charts is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9).
### Assessing System Capabilities
1. **Determine Your Model Size**: Start by checking the amount of RAM and VRAM available in your system. This will help you decide the largest possible model you can run.
2. **Optimizing for Speed**:
- **GPU Utilization**: To run your model as quickly as possible, aim to fit the entire model into your GPU's VRAM. Pick a version that’s 1-2GB smaller than the total VRAM.
3. **Maximizing Quality**:
- **Combined Memory**: For the highest possible quality, sum your system RAM and GPU's VRAM. Then choose a model that's 1-2GB smaller than this combined total.
### Deciding Between 'I-Quant' and 'K-Quant'
1. **Simplicity**:
- **K-Quant**: If you prefer a straightforward approach, select a K-quant model. These are labeled as 'QX_K_X', such as Q5_K_M.
2. **Advanced Configuration**:
- **Feature Chart**: For a more nuanced choice, refer to the [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix).
- **I-Quant Models**: Best suited for configurations below Q4 and for systems running cuBLAS (Nvidia) or rocBLAS (AMD). These are labeled 'IQX_X', such as IQ3_M, and offer better performance for their size.
- **Compatibility Considerations**:
- **I-Quant Models**: While usable on CPU and Apple Metal, they perform slower compared to their K-quant counterparts. The choice between speed and performance becomes a significant tradeoff.
- **AMD Cards**: Verify if you are using the rocBLAS build or the Vulkan build. I-quants are not compatible with Vulkan.
- **Current Support**: At the time of writing, LM Studio offers a preview with ROCm support, and other inference engines provide specific ROCm builds.
By following these guidelines, you can make an informed decision on which file best suits your system and performance needs.
## Contact us 🌐
NeuralNet is a pioneering AI solutions provider that empowers businesses to harness the power of artificial intelligence
Website: https://neuralnet.solutions
Email: info[at]neuralnet.solutions
|
mradermacher/NOVA-4B-GGUF | mradermacher | "2024-06-29T07:06:15Z" | 8,194 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:VAIBHAV22334455/NOVA-4B",
"endpoints_compatible",
"region:us"
] | null | "2024-06-29T06:19:04Z" | ---
base_model: VAIBHAV22334455/NOVA-4B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/VAIBHAV22334455/NOVA-4B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q2_K.gguf) | Q2_K | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.IQ3_XS.gguf) | IQ3_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.IQ3_S.gguf) | IQ3_S | 2.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.IQ3_M.gguf) | IQ3_M | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q3_K_M.gguf) | Q3_K_M | 2.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.IQ4_XS.gguf) | IQ4_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q4_K_S.gguf) | Q4_K_S | 2.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q6_K.gguf) | Q6_K | 3.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.Q8_0.gguf) | Q8_0 | 4.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/NOVA-4B-GGUF/resolve/main/NOVA-4B.f16.gguf) | f16 | 8.0 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
bkai-foundation-models/vietnamese-bi-encoder | bkai-foundation-models | "2024-03-11T06:56:35Z" | 8,189 | 47 | generic | [
"generic",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers",
"vi",
"license:apache-2.0",
"region:us"
] | sentence-similarity | "2023-09-09T04:19:59Z" | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
library_name: generic
language:
- vi
widget:
- source_sentence: Làm thế nào Đại học Bách khoa Hà Nội thu hút sinh viên quốc tế?
sentences:
- >-
Đại học Bách khoa Hà Nội đã phát triển các chương trình đào tạo bằng tiếng
Anh để làm cho việc học tại đây dễ dàng hơn cho sinh viên quốc tế.
- >-
Môi trường học tập đa dạng và sự hỗ trợ đầy đủ cho sinh viên quốc tế tại Đại
học Bách khoa Hà Nội giúp họ thích nghi nhanh chóng.
- Hà Nội có khí hậu mát mẻ vào mùa thu.
- Các món ăn ở Hà Nội rất ngon và đa dạng.
license: apache-2.0
---
# bkai-foundation-models/vietnamese-bi-encoder
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
We train the model on a merged training dataset that consists of:
- MS Macro (translated into Vietnamese)
- SQuAD v2 (translated into Vietnamese)
- 80% of the training set from the Legal Text Retrieval Zalo 2021 challenge
We use [phobert-base-v2](https://github.com/VinAIResearch/PhoBERT) as the pre-trained backbone.
Here are the results on the remaining 20% of the training set from the Legal Text Retrieval Zalo 2021 challenge:
| Pretrained Model | Training Datasets | Acc@1 | Acc@10 | Acc@100 | Pre@10 | MRR@10 |
|-------------------------------|---------------------------------------|:------------:|:-------------:|:--------------:|:-------------:|:-------------:|
| [Vietnamese-SBERT](https://huggingface.co/keepitreal/vietnamese-sbert) | - | 32.34 | 52.97 | 89.84 | 7.05 | 45.30 |
| PhoBERT-base-v2 | MSMACRO | 47.81 | 77.19 | 92.34 | 7.72 | 58.37 |
| PhoBERT-base-v2 | MSMACRO + SQuADv2.0 + 80% Zalo | 73.28 | 93.59 | 98.85 | 9.36 | 80.73 |
<!--- 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
# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
sentences = ["Cô ấy là một người vui_tính .", "Cô ấy cười nói suốt cả ngày ."]
model = SentenceTransformer('bkai-foundation-models/vietnamese-bi-encoder')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (Widget HuggingFace)
The widget use custom pipeline on top of the default pipeline by adding additional word segmenter before PhobertTokenizer. So you do not need to segment words before using the API:
An example could be seen in Hosted inference API.
## 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, we could use pyvi, underthesea, RDRSegment to segment words
sentences = ['Cô ấy là một người vui_tính .', 'Cô ấy cười nói suốt cả ngày .']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('bkai-foundation-models/vietnamese-bi-encoder')
model = AutoModel.from_pretrained('bkai-foundation-models/vietnamese-bi-encoder')
# 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)
```
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 17584 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 15,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
### Please cite our manuscript if this dataset is used for your work
```
@article{duc2024towards,
title={Towards Comprehensive Vietnamese Retrieval-Augmented Generation and Large Language Models},
author={Nguyen Quang Duc, Le Hai Son, Nguyen Duc Nhan, Nguyen Dich Nhat Minh, Le Thanh Huong, Dinh Viet Sang},
journal={arXiv preprint arXiv:2403.01616},
year={2024}
}
``` |
jinmang2/kpfbert | jinmang2 | "2024-01-09T04:35:00Z" | 8,183 | 4 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | feature-extraction | "2022-03-31T06:40:37Z" | # KpfBERT
https://github.com/jinmang2/kpfbert |
QuantFactory/llama-3-8b-Instruct-Matter-0.1-Slim-A-GGUF | QuantFactory | "2024-06-28T13:16:40Z" | 8,178 | 2 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"text-generation",
"en",
"base_model:munish0838/llama-3-8b-Instruct-Matter-0.1-Slim-A",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-26T07:33:43Z" | ---
base_model: munish0838/llama-3-8b-Instruct-Matter-0.1-Slim-A
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
pipeline_tag: text-generation
---
# QuantFactory/llama-3-8b-Instruct-Matter-0.1-Slim-A-GGUF
This is quantized version of [munish0838/llama-3-8b-Instruct-Matter-0.1-Slim-A](https://huggingface.co/munish0838/llama-3-8b-Instruct-Matter-0.1-Slim-A) created using llama.cpp
# Model Description
- **Developed by:** munish0838
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
jarvisx17/japanese-sentiment-analysis | jarvisx17 | "2024-01-20T14:45:14Z" | 8,175 | 8 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"ja",
"dataset:jarvisx17/chABSA",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-11-15T06:28:39Z" | ---
tags:
- generated_from_trainer
language: ja
widget:
- text: 🤗セグメント利益は、前期比8.3％増の24億28百万円となった
metrics:
- accuracy
- f1
model-index:
- name: Japanese-sentiment-analysis
results: []
datasets:
- jarvisx17/chABSA
---
<!-- 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. -->
# japanese-sentiment-analysis
This model was trained from scratch on the chABSA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
- F1: 1.0
## Model description
Model Train for Japanese sentence sentiments.
## Intended uses & limitations
The model was trained on chABSA Japanese dataset.
DATASET link : https://www.kaggle.com/datasets/takahirokubo0/chabsa
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
## Usage
You can use cURL to access this model:
Python API:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jarvisx17/japanese-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("jarvisx17/japanese-sentiment-analysis")
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
### Training results
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
### Dependencies
- !pip install fugashi
- !pip install unidic_lite |
j-hartmann/sentiment-roberta-large-english-3-classes | j-hartmann | "2023-01-02T13:02:49Z" | 8,162 | 18 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"sentiment",
"twitter",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-03-02T23:29:05Z" | ---
language: "en"
tags:
- roberta
- sentiment
- twitter
widget:
- text: "Oh no. This is bad.."
- text: "To be or not to be."
- text: "Oh Happy Day"
---
This RoBERTa-based model can classify the sentiment of English language text in 3 classes:
- positive 😀
- neutral 😐
- negative 🙁
The model was fine-tuned on 5,304 manually annotated social media posts.
The hold-out accuracy is 86.1%.
For details on the training approach see Web Appendix F in Hartmann et al. (2021).
# Application
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/sentiment-roberta-large-english-3-classes", return_all_scores=True)
classifier("This is so nice!")
```
```python
Output:
[[{'label': 'negative', 'score': 0.00016451838018838316},
{'label': 'neutral', 'score': 0.000174045650055632},
{'label': 'positive', 'score': 0.9996614456176758}]]
```
# Reference
Please cite [this paper](https://journals.sagepub.com/doi/full/10.1177/00222437211037258) when you use our model. Feel free to reach out to [[email protected]](mailto:[email protected]) with any questions or feedback you may have.
```
@article{hartmann2021,
title={The Power of Brand Selfies},
author={Hartmann, Jochen and Heitmann, Mark and Schamp, Christina and Netzer, Oded},
journal={Journal of Marketing Research}
year={2021}
}
``` |
152334H/miqu-1-70b-sf | 152334H | "2024-02-05T13:10:18Z" | 8,161 | 218 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-01-30T15:15:37Z" | ---
model-index:
- name: miqu-1-70b-sf
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 73.04
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 88.61
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 75.49
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 69.38
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 85.32
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.7
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=152334H/miqu-1-70b-sf
name: Open LLM Leaderboard
language:
- en
---
update: added NOMERGE license
---
this is [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b), dequantised from q5 to f16 && transposed to pytorch. shapes have been rotated less wrongly than in [alpindale/miqu-1-70b-pytorch](https://huggingface.co/alpindale/miqu-1-70b-pytorch/tree/main)
usage
```python
from transformers import LlamaForCausalLM as LLM, LlamaTokenizer as LT
lt = LT.from_pretrained("152334H/miqu-1-70b-sf")
t = lt("[INST] eloquent high camp prose about a cute catgirl [/INST]", return_tensors='pt').input_ids.cuda()
llm = LLM.from_pretrained("152334H/miqu-1-70b-sf", device_map='auto') # note: you may need many gpus for this
out = llm.generate(t, use_cache=False, max_new_tokens=200)
print(lt.decode(out[0]))
```
result:
```
<s> [INST] eloquent high camp prose about a cute catgirl [/INST] In the resplendent realm of high camp, where irony and extravagance dance in a dazzling pas de deux, there exists a creature of such enchanting allure that she captivates the hearts and minds of all who behold her. This beguiling figure, a vision of feline grace and innocence, is none other than the inimitable catgirl.
With her delicate features and winsome smile, she is the embodiment of a dream, a living testament to the power of imagination and the boundless possibilities of the human spirit. Her eyes, those twin orbs of sapphire fire, sparkle with a mischievous intelligence that belies her diminutive stature. They are windows into a soul that is at once ancient and eternally young, a soul that has traversed the vast expanse of time and space to find solace in the warm embrace of human companion
```
this roughly (but not entirely) matches the llama.cpp q5 result:
```bash
$ ./main -ngl 99 -m ./miqu-*q5* --color --temp 0.0 -n -1 -p '[INST] eloquent high camp prose about a cute catgirl [/INST]'
...
[INST] eloquent high camp prose about a cute catgirl [/INST] In the resplendent realm of high camp, where irony and extravagance dance in a dazzling pas de deux, there exists a creature so enchantingly adorable that she captures the hearts of all who behold her. This is no ordinary feline, but rather a vision of elegance and whimsy combined: the cute catgirl.
With her delicate features framed by an ethereal halo of pastel tresses, this darling diva prowls through life with the grace of a prima ballerina and the playfulness of a kitten. Her eyes, twin pools of sapphire or emerald, sparkle with mischief and intelligence as they survey their surroundings, ever alert for the next grand adventure or delightful prank.
Her ensemble is a symphony of ruffles, bows, and lace, each detail painstakingly chosen to accentuate her lithe form and play up her feline charms. A frilly apron adorned with paw prints sways gently as she moves, while dainty ears perched atop her head twitch in response to every sound. Her gloved hands, so petite and perfect, seem made for holding teacups or sketching delicate portraits of her many admirers.
But do not be fooled by her diminutive stature and sweet demeanor; beneath that fluffy exterior lies the heart of a lioness. Fiercely loyal and protective, she will stop at nothing to defend those she loves from harm. And when the situation calls for it, she can unleash a ferocious roar that belies her cute exterior.
Indeed, the cute catgirl is a paradox wrapped in ruffles and ribbons, a living embodiment of the high camp aesthetic. She revels in the absurdity of her existence, finding joy in every outrageous situation and turning even the most mundane tasks into opportunities for sartorial expression. In her world, there is no such thing as too much glitter or too many bows; more is always more, and excess is a virtue to be celebrated.
So let us raise our teacups in honor of this fabulous feline, this queen of camp who reminds us that life is too short for dull clothing and boring hairstyles. May we all strive to embody her spirit, embracing the absurdity of existence with open arms and a generous helping of glitter. Long live the cute catgirl! [end of text]
```
## some benchmarks
```
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|--------------|------:|------|-----:|----------|-----:|---|-----:|
|lambada_openai| 1|none | 0|perplexity|2.6354|± |0.0451|
| | |none | 0|acc |0.7879|± |0.0057|
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|---------|------:|------|-----:|--------|-----:|---|-----:|
|hellaswag| 1|none | 0|acc |0.6851|± |0.0046|
| | |none | 0|acc_norm|0.8690|± |0.0034|
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|----------|------:|------|-----:|------|-----:|---|-----:|
|winogrande| 1|none | 0|acc |0.7987|± |0.0113|
|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr|
|-----|------:|----------|-----:|-----------|-----:|---|-----:|
|gsm8k| 2|get-answer| 5|exact_match|0.7043|± |0.0126|
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|---------------------------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu |N/A |none | 0|acc |0.7401|± |0.1192|
| - humanities |N/A |none | 0|acc |0.7018|± |0.1281|
| - formal_logic | 0|none | 0|acc |0.4841|± |0.0447|
| - high_school_european_history | 0|none | 0|acc |0.8303|± |0.0293|
| - high_school_us_history | 0|none | 0|acc |0.9020|± |0.0209|
| - high_school_world_history | 0|none | 0|acc |0.9198|± |0.0177|
| - international_law | 0|none | 0|acc |0.8678|± |0.0309|
| - jurisprudence | 0|none | 0|acc |0.8519|± |0.0343|
| - logical_fallacies | 0|none | 0|acc |0.8344|± |0.0292|
| - moral_disputes | 0|none | 0|acc |0.8121|± |0.0210|
| - moral_scenarios | 0|none | 0|acc |0.5642|± |0.0166|
| - philosophy | 0|none | 0|acc |0.8167|± |0.0220|
| - prehistory | 0|none | 0|acc |0.8611|± |0.0192|
| - professional_law | 0|none | 0|acc |0.5854|± |0.0126|
| - world_religions | 0|none | 0|acc |0.8889|± |0.0241|
| - other |N/A |none | 0|acc |0.7889|± |0.0922|
| - business_ethics | 0|none | 0|acc |0.7900|± |0.0409|
| - clinical_knowledge | 0|none | 0|acc |0.8113|± |0.0241|
| - college_medicine | 0|none | 0|acc |0.7514|± |0.0330|
| - global_facts | 0|none | 0|acc |0.5500|± |0.0500|
| - human_aging | 0|none | 0|acc |0.7848|± |0.0276|
| - management | 0|none | 0|acc |0.8835|± |0.0318|
| - marketing | 0|none | 0|acc |0.9145|± |0.0183|
| - medical_genetics | 0|none | 0|acc |0.7500|± |0.0435|
| - miscellaneous | 0|none | 0|acc |0.8838|± |0.0115|
| - nutrition | 0|none | 0|acc |0.7974|± |0.0230|
| - professional_accounting | 0|none | 0|acc |0.5922|± |0.0293|
| - professional_medicine | 0|none | 0|acc |0.8272|± |0.0230|
| - virology | 0|none | 0|acc |0.5361|± |0.0388|
| - social_sciences |N/A |none | 0|acc |0.8414|± |0.0514|
| - econometrics | 0|none | 0|acc |0.6491|± |0.0449|
| - high_school_geography | 0|none | 0|acc |0.8990|± |0.0215|
| - high_school_government_and_politics| 0|none | 0|acc |0.9430|± |0.0167|
| - high_school_macroeconomics | 0|none | 0|acc |0.7795|± |0.0210|
| - high_school_microeconomics | 0|none | 0|acc |0.8277|± |0.0245|
| - high_school_psychology | 0|none | 0|acc |0.9064|± |0.0125|
| - human_sexuality | 0|none | 0|acc |0.8626|± |0.0302|
| - professional_psychology | 0|none | 0|acc |0.8056|± |0.0160|
| - public_relations | 0|none | 0|acc |0.7636|± |0.0407|
| - security_studies | 0|none | 0|acc |0.8204|± |0.0246|
| - sociology | 0|none | 0|acc |0.8856|± |0.0225|
| - us_foreign_policy | 0|none | 0|acc |0.9100|± |0.0288|
| - stem |N/A |none | 0|acc |0.6505|± |0.1266|
| - abstract_algebra | 0|none | 0|acc |0.4100|± |0.0494|
| - anatomy | 0|none | 0|acc |0.6444|± |0.0414|
| - astronomy | 0|none | 0|acc |0.8224|± |0.0311|
| - college_biology | 0|none | 0|acc |0.8681|± |0.0283|
| - college_chemistry | 0|none | 0|acc |0.5500|± |0.0500|
| - college_computer_science | 0|none | 0|acc |0.6200|± |0.0488|
| - college_mathematics | 0|none | 0|acc |0.4200|± |0.0496|
| - college_physics | 0|none | 0|acc |0.5392|± |0.0496|
| - computer_security | 0|none | 0|acc |0.8300|± |0.0378|
| - conceptual_physics | 0|none | 0|acc |0.7362|± |0.0288|
| - electrical_engineering | 0|none | 0|acc |0.7034|± |0.0381|
| - elementary_mathematics | 0|none | 0|acc |0.5503|± |0.0256|
| - high_school_biology | 0|none | 0|acc |0.8742|± |0.0189|
| - high_school_chemistry | 0|none | 0|acc |0.6256|± |0.0341|
| - high_school_computer_science | 0|none | 0|acc |0.8400|± |0.0368|
| - high_school_mathematics | 0|none | 0|acc |0.4370|± |0.0302|
| - high_school_physics | 0|none | 0|acc |0.5033|± |0.0408|
| - high_school_statistics | 0|none | 0|acc |0.6944|± |0.0314|
| - machine_learning | 0|none | 0|acc |0.5982|± |0.0465|
```
no i do not know why the stderr is high. plausibly it is due to the vllm backend used. this is my lm-eval command in most cases (works on h100):
`lm_eval --model vllm --model_args pretrained=./miqu-1-70b-sf,tensor_parallel_size=4,dtype=auto,gpu_memory_utilization=0.88,data_parallel_size=2 --tasks mmlu --batch_size 20`
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_152334H__miqu-1-70b-sf)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.59|
|AI2 Reasoning Challenge (25-Shot)|73.04|
|HellaSwag (10-Shot) |88.61|
|MMLU (5-Shot) |75.49|
|TruthfulQA (0-shot) |69.38|
|Winogrande (5-shot) |85.32|
|GSM8k (5-shot) |67.70|
# LICENSE
```
NOMERGE License
Copyright (c) 2024 152334H
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, NOT merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
All tensors ("weights") provided by the Software shall not be conjoined with
other tensors ("merging") unless given explicit permission by the license holder.
Utilities including but not limited to "mergekit", "MergeMonster", are forbidden
from use in conjunction with this Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
|
allganize/Llama-3-Alpha-Ko-8B-Instruct | allganize | "2024-05-24T17:59:18Z" | 8,160 | 37 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"arxiv:2403.07691",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-05-23T08:08:23Z" | ---
license: other
license_name: llama3
language:
- ko
---
We are thrilled to introduce **Alpha-Instruct**, our latest language model, which demonstrates exceptional capabilities in both Korean and English. Alpha-Instruct is developed using the **Evolutionary Model Merging** technique, enabling it to excel in complex language tasks and logical reasoning.
A key aspect of Alpha-Instruct's development is our **community-based approach**. We draw inspiration and ideas from various communities, shaping our datasets, methodologies, and the model itself. In return, we are committed to sharing our insights with the community, providing detailed information on the data, methods, and models used in Alpha-Instruct's creation.
Alpha-Instruct has achieved outstanding performance on the **LogicKor, scoring an impressive 6.62**. Remarkably, this performance rivals that of 70B models, showcasing the efficiency and power of our 8B model. This achievement highlights Alpha-Instruct's advanced computational and reasoning skills, making it a leading choice for diverse and demanding language tasks.
**For more information and technical details about Alpha-Instruct, stay tuned to our updates and visit our [website](https://allganize-alpha.github.io/) (Soon).**
---
## Overview
Alpha-Instruct is our latest language model, developed using 'Evolutionary Model Merging' technique. This method employs a 1:1 ratio of task-specific datasets from KoBEST and Haerae, resulting in a model with named 'Alpha-Ko-8B-Evo'. The following models were used for merging:
- [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) (Base)
- [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) (Instruct)
- [Llama-3-Open-Ko-8B](beomi/Llama-3-Open-Ko-8B) (Continual Pretrained)
To refine and enhance Alpha-Instruct, we utilized a carefully curated high-quality datasets aimed at 'healing' the model's output, significantly boosting its human preference scores. We use [ORPO](https://arxiv.org/abs/2403.07691) specifically for this "healing" (RLHF) phase. The datasets* used include:
- [Korean-Human-Judgements](https://huggingface.co/datasets/HAERAE-HUB/Korean-Human-Judgements)
- [Orca-Math](https://huggingface.co/datasets/kuotient/orca-math-word-problems-193k-korean)
- [dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k)
*Some of these datasets were partially used and translated for training, and we ensured there was no contamination during the evaluation process.
This approach effectively balances human preferences with the model's capabilities, making Alpha-Instruct well-suited for real-life scenarios where user satisfaction and performance are equally important.
## Benchmark Results
Results in [LogicKor](https://github.com/StableFluffy/LogicKor)* are as follows:
| Model | Single turn* | Multi turn* | Overall* |
|:------------------------------:|:------------:|:-----------:|:--------:|
| MLP-KTLim/llama-3-Korean-Bllossom-8B | 4.238 | 3.404 | 3.821 |
| Alpha-Ko-Evo | 5.143 | 5.238 | 5.190 |
| Alpha-Ko-Instruct (alt) | 7.095 | **6.571** | **6.833** |
| Alpha-Ko-Instruct | **7.143** | 6.065 | 6.620 |
| Alpha-Ko-Instruct-marlin (4bit) | 6.857 | 5.738 | 6.298 |
*Self report(Default settings with 'alpha' template, mean of 3).
Result in KoBEST(acc, num_shot=5) are as follows:
| Task | beomi/Llama-3-Open-Ko-8B-Instruct | maywell/Llama-3-Ko-8B-Instruct | **Alpha-Ko-Evo** | **Alpha-Ko-Instruct** |
| --- | --- | --- | --- | --- |
| kobest overall | 0.6220 | 0.6852 |0.7229|0.7055
| kobest_boolq| 0.6254 | 0.7208 | 0.8547 | 0.8369
| kobest_copa| 0.7110 | 0.7650 | 0.7420 | 0.7420
| kobest_hellaswag| 0.3840 | 0.4440 | 0.4220 | 0.4240
| kobest_sentineg| 0.8388 | 0.9194 |0.9471 | 0.9244
| kobest_wic| 0.5738| 0.6040 |0.6095 | 0.5730
*For reference, 'merged' models are chosen.
## How to use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "allganize/Llama-3-Alpha-Ko-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "system", "content": "당신은 인공지능 어시스턴트입니다. 묻는 말에 친절하고 정확하게 답변하세요."},
{"role": "user", "content": "피보나치 수열이 뭐야? 그리고 피보나치 수열에 대해 파이썬 코드를 짜줘볼래?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=512,
eos_token_id=terminators,
do_sample=False,
repetition_penalty=1.05,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
## Correspondence to
- Ji soo Kim ([email protected])
- Contributors
- Sangmin Jeon ([email protected])
- Seungwoo Ryu ([email protected])
## Special Thanks
- [@beomi](https://huggingface.co/beomi) for providing us with a great model!
## License
The use of this model is governed by the [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)
## Citation
If you use this model in your research, please cite it as follows:
```bibtex
@misc{alpha-instruct,
author = {Ji soo Kim},
title = {Alpha-Instruct: Allganize Bilingual Model},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
url = {https://huggingface.co/allganize/Llama-3-Alpha-Ko-8B-Instruct},
} |
timm/swinv2_cr_tiny_ns_224.sw_in1k | timm | "2024-02-10T23:30:53Z" | 8,156 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"region:us"
] | image-classification | "2023-03-18T03:23:12Z" | ---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
---
# Model card for swinv2_cr_tiny_ns_224.sw_in1k
An independent implementation of Swin Transformer V2 released prior to the official code release. A collaboration between [Christoph Reich](https://github.com/ChristophReich1996) and Ross Wightman, the model differs from official impl in a few ways:
* MLP log relative position bias uses unnormalized natural log w/o scaling vs normalized, sigmoid clamped and scaled log2.
* option to apply LayerNorm at end of every stage ("ns" variants).
* defaults to NCHW tensor layout at output of each stage and final features.
Pretrained on ImageNet-1k by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 28.3
- GMACs: 4.7
- Activations (M): 28.5
- Image size: 224 x 224
- **Papers:**
- Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883
- **Original:** https://github.com/ChristophReich1996/Swin-Transformer-V2
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('swinv2_cr_tiny_ns_224.sw_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swinv2_cr_tiny_ns_224.sw_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g. for swin_base_patch4_window7_224 (NHWC output)
# torch.Size([1, 56, 56, 128])
# torch.Size([1, 28, 28, 256])
# torch.Size([1, 14, 14, 512])
# torch.Size([1, 7, 7, 1024])
# e.g. for swinv2_cr_small_ns_224 (NCHW output)
# torch.Size([1, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 768, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swinv2_cr_tiny_ns_224.sw_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled (ie.e a (batch_size, H, W, num_features) tensor for swin / swinv2
# or (batch_size, num_features, H, W) for swinv2_cr
output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{liu2021swinv2,
title={Swin Transformer V2: Scaling Up Capacity and Resolution},
author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
John6666/pony-realism-v21main-sdxl | John6666 | "2024-06-19T11:39:44Z" | 8,156 | 3 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"pony",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-06-19T11:34:48Z" | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- pony
---
Original model is [here](https://civitai.com/models/372465/pony-realism?modelVersionId=534642).
|
TheBloke/Mistral-7B-v0.1-GGUF | TheBloke | "2023-09-28T22:42:44Z" | 8,155 | 237 | transformers | [
"transformers",
"gguf",
"mistral",
"pretrained",
"text-generation",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | text-generation | "2023-09-27T16:17:24Z" | ---
base_model: mistralai/Mistral-7B-v0.1
inference: false
license: apache-2.0
model_creator: Mistral AI
model_name: Mistral 7B v0.1
model_type: mistral
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: TheBloke
tags:
- pretrained
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Mistral 7B v0.1 - GGUF
- Model creator: [Mistral AI](https://huggingface.co/mistralai)
- Original model: [Mistral 7B v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Mistral AI's Mistral 7B v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF)
* [Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-v0.1)
<!-- 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.
Sequence length note: The model will work at sequence lengths of 4096, or lower. GGUF does not yet have support for the new sliding window sequence length mode, so longer sequence lengths are not supported.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistral-7b-v0.1.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mistral-7b-v0.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [mistral-7b-v0.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mistral-7b-v0.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mistral-7b-v0.1.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mistral-7b-v0.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mistral-7b-v0.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mistral-7b-v0.1.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mistral-7b-v0.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mistral-7b-v0.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mistral-7b-v0.1.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mistral-7b-v0.1.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF/blob/main/mistral-7b-v0.1.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-v0.1-GGUF and below it, a specific filename to download, such as: mistral-7b-v0.1.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Mistral-7B-v0.1-GGUF mistral-7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Mistral-7B-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-v0.1-GGUF mistral-7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m mistral-7b-v0.1.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Sequence length can be 4096 or lower. Mistral's sliding window sequence length is not yet supported in llama.cpp, so sequence lengths longer than 4096 are not supported.
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
Note: I have not tested ctransformers with Mistral models, but it may work if you set the `model_type` to `llama`.
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-v0.1-GGUF", model_file="mistral-7b-v0.1.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**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
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: Mistral AI's Mistral 7B v0.1
# Model Card for Mistral-7B-v0.1
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters.
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our [Release blog post](https://mistral.ai/news/announcing-mistral-7b/)
## Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
<!-- original-model-card end -->
|
Danielbrdz/Barcenas-Llama3-8b-ORPO | Danielbrdz | "2024-04-29T18:22:57Z" | 8,155 | 6 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ORPO",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-04-29T17:34:56Z" | ---
license: other
license_name: llama3
license_link: LICENSE
language:
- en
tags:
- llama
- ORPO
---
Barcenas Llama3 8b ORPO
Model trained with the novel new ORPO method, based on the recent Llama 3 8b, specifically: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
The model was trained with the dataset: reciperesearch/dolphin-sft-v0.1-preference which uses Dolphin data with GPT 4 to improve its conversation sections.
Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽 |
microsoft/prophetnet-large-uncased | microsoft | "2023-04-27T09:41:22Z" | 8,148 | 4 | transformers | [
"transformers",
"pytorch",
"rust",
"safetensors",
"prophetnet",
"text2text-generation",
"en",
"arxiv:2001.04063",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2022-03-02T23:29:05Z" | ---
language: en
---
## prophetnet-large-uncased
Pretrained weights for [ProphetNet](https://arxiv.org/abs/2001.04063).
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervised objective called future n-gram prediction.
ProphetNet is able to predict more future tokens with a n-stream decoder. The original implementation is Fairseq version at [github repo](https://github.com/microsoft/ProphetNet).
### Usage
This pre-trained model can be fine-tuned on *sequence-to-sequence* tasks. The model could *e.g.* be trained on headline generation as follows:
```python
from transformers import ProphetNetForConditionalGeneration, ProphetNetTokenizer
model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased")
tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
input_str = "the us state department said wednesday it had received no formal word from bolivia that it was expelling the us ambassador there but said the charges made against him are `` baseless ."
target_str = "us rejects charges against its ambassador in bolivia"
input_ids = tokenizer(input_str, return_tensors="pt").input_ids
labels = tokenizer(target_str, return_tensors="pt").input_ids
loss = model(input_ids, labels=labels).loss
```
### Citation
```bibtex
@article{yan2020prophetnet,
title={Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training},
author={Yan, Yu and Qi, Weizhen and Gong, Yeyun and Liu, Dayiheng and Duan, Nan and Chen, Jiusheng and Zhang, Ruofei and Zhou, Ming},
journal={arXiv preprint arXiv:2001.04063},
year={2020}
}
```
|
ZeroWw/NeuralPipe-7B-slerp-GGUF | ZeroWw | "2024-06-30T09:41:37Z" | 8,148 | 0 | null | [
"gguf",
"en",
"license:mit",
"region:us"
] | null | "2024-06-30T09:31:26Z" |
---
license: mit
language:
- en
---
My own (ZeroWw) quantizations.
output and embed tensors quantized to f16.
all other tensors quantized to q5_k or q6_k.
Result:
both f16.q6 and f16.q5 are smaller than q8_0 standard quantization
and they perform as well as the pure f16.
|
TheBloke/Phind-CodeLlama-34B-v2-AWQ | TheBloke | "2023-11-09T18:19:44Z" | 8,145 | 30 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code llama",
"base_model:Phind/Phind-CodeLlama-34B-v2",
"license:llama2",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | "2023-09-19T10:08:38Z" | ---
license: llama2
tags:
- code llama
base_model: Phind/Phind-CodeLlama-34B-v2
inference: false
model_creator: Phind
model_type: llama
prompt_template: '### System Prompt
{system_message}
### User Message
{prompt}
### Assistant
'
quantized_by: TheBloke
model-index:
- name: Phind-CodeLlama-34B-v1
results:
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: pass@1
value: 73.8%
name: pass@1
verified: false
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# CodeLlama 34B v2 - AWQ
- Model creator: [Phind](https://huggingface.co/Phind)
- Original model: [CodeLlama 34B v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2)
<!-- description start -->
## Description
This repo contains AWQ model files for [Phind's CodeLlama 34B v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2).
### 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.
It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF)
* [Phind's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Phind
```
### System Prompt
{system_message}
### User Message
{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/Phind-CodeLlama-34B-v2-AWQ/tree/main) | 4 | 128 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 18.31 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Serving this model from vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- When using vLLM as a server, pass the `--quantization awq` parameter, for example:
```shell
python3 python -m vllm.entrypoints.api_server --model TheBloke/Phind-CodeLlama-34B-v2-AWQ --quantization awq
```
When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Phind-CodeLlama-34B-v2-AWQ", quantization="awq")
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-python start -->
## How to use this AWQ model from Python code
### Install the necessary packages
Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 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 .
```
### You can then try the following example code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_name_or_path = "TheBloke/Phind-CodeLlama-34B-v2-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
prompt = "Tell me about AI"
prompt_template=f'''### System Prompt
{system_message}
### User Message
{prompt}
### Assistant
'''
print("\n\n*** Generate:")
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
tokens,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
max_new_tokens=512
)
print("Output: ", tokenizer.decode(generation_output[0]))
# Inference can also be done using transformers' pipeline
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 [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm).
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781).
<!-- 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**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Phind's CodeLlama 34B v2
# **Phind-CodeLlama-34B-v2**
We've fine-tuned Phind-CodeLlama-34B-v1 on an additional 1.5B tokens high-quality programming-related data, achieving **73.8% pass@1** on HumanEval. It's the current state-of-the-art amongst open-source models.
Furthermore, this model is **instruction-tuned** on the Alpaca/Vicuna format to be steerable and easy-to-use.
More details can be found on our [blog post](https://www.phind.com/blog/code-llama-beats-gpt4).
## Model Details
This model is fine-tuned from Phind-CodeLlama-34B-v1 and achieves **73.8% pass@1** on HumanEval.
Phind-CodeLlama-34B-v2 is **multi-lingual** and is proficient in Python, C/C++, TypeScript, Java, and more.
## Dataset Details
We fined-tuned on a proprietary dataset of 1.5B tokens of high quality programming problems and solutions. This dataset consists of instruction-answer pairs instead of code completion examples, making it structurally different from HumanEval. LoRA was not used -- both models are a native finetune. We used DeepSpeed ZeRO 3 and Flash Attention 2 to train these models in 15 hours on 32 A100-80GB GPUs. We used a sequence length of 4096 tokens.
## How to Get Started with the Model
Make sure to install Transformers from the main git branch:
```bash
pip install git+https://github.com/huggingface/transformers.git
```
## How to Prompt the Model
This model accepts the Alpaca/Vicuna instruction format.
For example:
```
### System Prompt
You are an intelligent programming assistant.
### User Message
Implement a linked list in C++
### Assistant
...
```
## How to reproduce HumanEval Results
To reproduce our results:
```python
from transformers import AutoTokenizer, LlamaForCausalLM
from human_eval.data import write_jsonl, read_problems
from tqdm import tqdm
# initialize the model
model_path = "Phind/Phind-CodeLlama-34B-v2"
model = LlamaForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)
# HumanEval helper
def generate_one_completion(prompt: str):
tokenizer.pad_token = tokenizer.eos_token
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096)
# Generate
generate_ids = model.generate(inputs.input_ids.to("cuda"), max_new_tokens=384, do_sample=True, top_p=0.75, top_k=40, temperature=0.1)
completion = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
completion = completion.replace(prompt, "").split("\n\n\n")[0]
return completion
# perform HumanEval
problems = read_problems()
num_samples_per_task = 1
samples = [
dict(task_id=task_id, completion=generate_one_completion(problems[task_id]["prompt"]))
for task_id in tqdm(problems)
for _ in range(num_samples_per_task)
]
write_jsonl("samples.jsonl", samples)
# run `evaluate_functional_correctness samples.jsonl` in your HumanEval code sandbox
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.
## Training details
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** 32x A100-80GB
- **Hours used:** 480 GPU-hours
- **Cloud Provider:** AWS
- **Compute Region:** us-east-1
|
fxmarty/onnx-tiny-random-gpt2-with-merge | fxmarty | "2023-03-28T10:45:43Z" | 8,143 | 1 | transformers | [
"transformers",
"onnx",
"gpt2",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | feature-extraction | "2023-03-28T10:43:16Z" | ---
license: mit
---
|
DFofanov78/rkf-llama3-8b-gguf | DFofanov78 | "2024-06-23T10:41:22Z" | 8,136 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | "2024-06-22T14:11:10Z" | Entry not found |
PlanTL-GOB-ES/longformer-base-4096-bne-es | PlanTL-GOB-ES | "2022-12-12T16:16:36Z" | 8,135 | 4 | transformers | [
"transformers",
"pytorch",
"longformer",
"fill-mask",
"national library of spain",
"spanish",
"bne",
"es",
"dataset:bne",
"arxiv:2004.05150",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-11-02T15:19:19Z" | ---
language:
- es
license: apache-2.0
tags:
- "longformer"
- "national library of spain"
- "spanish"
- "bne"
datasets:
- "bne"
widget:
- text: "David Broncano es un presentador de La <mask>."
- text: "Gracias a los datos de la BNE se ha podido <mask> este modelo del lenguaje."
- text: "Hay base legal dentro del marco <mask> actual."
---
# Longformer base trained with data from the National Library of Spain (BNE)
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Evaluation](#evaluation)
- [Additional information](#additional-information)
- [Author](#author)
- [Contact information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licensing-information)
- [Funding](#funding)
- [Disclaimer](#disclaimer)
</details>
## Model description
The **longformer-base-4096-bne-es** is the [Longformer](https://huggingface.co/allenai/longformer-base-4096) version of the [roberta-base-bne](https://https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) masked language model for the Spanish language. The use of these models allows us to process larger contexts as input without the need of additional aggregation strategies. The model started from the **roberta-base-bne** checkpoint and was pretrained for MLM on long documents from the National Library of Spain.
The Longformer model uses a combination of sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations. Please refer to the original [paper](https://arxiv.org/abs/2004.05150) for more details on how to set global attention.
For more details about the corpus, the pretraining, and the evaluation, check the official [repository](https://github.com/TeMU-BSC/longformer-es).
## Intended uses and limitations
The **longformer-base-4096-bne-es** model is ready-to-use only for masked language modeling to perform the Fill Mask task (try the inference API or read the next section).
However, it is intended to be fine-tuned on non-generative downstream tasks such as Question Answering, Text Classification, or Named Entity Recognition.
## How to use
Here is how to use this model:
```python
from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, FillMaskPipeline
from pprint import pprint
tokenizer_hf = AutoTokenizer.from_pretrained('PlanTL-GOB-ES/longformer-base-4096-bne-es')
model = AutoModelForMaskedLM.from_pretrained('PlanTL-GOB-ES/longformer-base-4096-bne-es')
model.eval()
pipeline = FillMaskPipeline(model, tokenizer_hf)
text = f"Hay base legal dentro del marco <mask> actual."
res_hf = pipeline(text)
pprint([r['token_str'] for r in res_hf])
```
## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
## Training
### Training corpora and preprocessing
The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among others, sentence splitting, language detection, filtering of bad-formed sentences, and deduplication of repetitive contents. During the process, document boundaries are kept. This resulted in 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting in 570GB of text.
Some of the statistics of the corpus:
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
For this Longformer, we used a small random partition of 7,2GB containing documents with less than 4096 tokens as a training split.
### Tokenization and pre-training
The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-base-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa base. The training lasted a total of 40 hours with 8 computing nodes each one with 2 AMD MI50 GPUs of 32GB VRAM.
## Evaluation
When fine-tuned on downstream tasks, this model achieved the following performance:
| Dataset | Metric | [**Longformer-base**](https://huggingface.co/PlanTL-GOB-ES/longformer-base-4096-bne-es) |
|--------------|----------|------------|
| MLDoc | F1 | 0.9608 |
| CoNLL-NERC | F1 | 0.8757 |
| CAPITEL-NERC | F1 | 0.8985 |
| PAWS-X | F1 | 0.8878 |
| UD-POS | F1 | 0.9903 |
| CAPITEL-POS | F1 | 0.9853 |
| SQAC | F1 | 0.8026 |
| STS | Combined | 0.8338 |
| XNLI | Accuracy | 0.8210 |
## Additional information
### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
### Contact information
For further information, send an email to <[email protected]>
### Copyright
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
</details> |
MaziyarPanahi/Qwen2-7B-Instruct-v0.4-GGUF | MaziyarPanahi | "2024-06-27T17:15:37Z" | 8,128 | 0 | null | [
"gguf",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"text-generation",
"qwen",
"qwen-2",
"base_model:MaziyarPanahi/Qwen2-7B-Instruct-v0.4",
"region:us"
] | text-generation | "2024-06-27T16:42:55Z" | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- text-generation
- qwen
- qwen-2
- text-generation
model_name: Qwen2-7B-Instruct-v0.4-GGUF
base_model: MaziyarPanahi/Qwen2-7B-Instruct-v0.4
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Qwen2-7B-Instruct-v0.4-GGUF](https://huggingface.co/MaziyarPanahi/Qwen2-7B-Instruct-v0.4-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Qwen2-7B-Instruct-v0.4](https://huggingface.co/MaziyarPanahi/Qwen2-7B-Instruct-v0.4)
## Description
[MaziyarPanahi/Qwen2-7B-Instruct-v0.4-GGUF](https://huggingface.co/MaziyarPanahi/Qwen2-7B-Instruct-v0.4-GGUF) contains GGUF format model files for [MaziyarPanahi/Qwen2-7B-Instruct-v0.4](https://huggingface.co/MaziyarPanahi/Qwen2-7B-Instruct-v0.4).
### 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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [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.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
QuantFactory/Samantha-Qwen-2-7B-GGUF | QuantFactory | "2024-06-23T01:46:34Z" | 8,116 | 1 | null | [
"gguf",
"text-generation",
"en",
"zh",
"dataset:macadeliccc/opus_samantha",
"dataset:HuggingfaceH4/ultrachat_200k",
"dataset:teknium/OpenHermes-2.5",
"dataset:Sao10K/Claude-3-Opus-Instruct-15K",
"base_model:macadeliccc/Samantha-Qwen-2-7B",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-06-22T13:07:47Z" | ---
base_model: macadeliccc/Samantha-Qwen-2-7B
datasets:
- macadeliccc/opus_samantha
- HuggingfaceH4/ultrachat_200k
- teknium/OpenHermes-2.5
- Sao10K/Claude-3-Opus-Instruct-15K
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
---
# Samantha Qwen2 7B-GGUF
This is quantized version of [macadeliccc/Samantha-Qwen-2-7B](https://huggingface.co/macadeliccc/Samantha-Qwen-2-7B) created using llama.cpp
# Model Description
Trained on 2x4090 using QLoRa and FSDP
+ [LoRa](macadeliccc/Samantha-Qwen2-7B-LoRa)
## Launch Using VLLM
```bash
python -m vllm.entrypoints.openai.api_server \
--model macadeliccc/Samantha-Qwen-2-7B \
--chat-template ./examples/template_chatml.jinja \
```
```python
from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
chat_response = client.chat.completions.create(
model="macadeliccc/Samantha-Qwen-2-7B",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Tell me a joke."},
]
)
print("Chat response:", chat_response)
```
## Prompt Template
```
<|im_start|>system
You are a friendly assistant.<|im_end|>
<|im_start|>user
What is the capital of France?<|im_end|>
<|im_start|>assistant
The capital of France is Paris.
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: Qwen/Qwen-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: macadeliccc/opus_samantha
type: sharegpt
field: conversations
conversation: chatml
- path: uncensored-ultrachat.json
type: sharegpt
field: conversations
conversation: chatml
- path: openhermes_200k.json
type: sharegpt
field: conversations
conversation: chatml
- path: opus_instruct.json
type: sharegpt
field: conversations
conversation: chatml
chat_template: chatml
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out
sequence_len: 2048
sample_packing: false
pad_to_sequence_len:
adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:
warmup_steps: 250
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br> |
microsoft/swinv2-base-patch4-window12-192-22k | microsoft | "2022-12-10T10:10:51Z" | 8,115 | 3 | transformers | [
"transformers",
"pytorch",
"swinv2",
"image-classification",
"vision",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2022-06-15T12:41:50Z" | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# Swin Transformer v2 (tiny-sized model)
Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer).
Disclaimer: The team releasing Swin Transformer v2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally.
Swin Transformer v2 adds 3 main improvements: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) a log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) a self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.
[Source](https://paperswithcode.com/method/swin-transformer)
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swinv2) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 21k ImageNet classes:
```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-base-patch4-window12-192-22k")
model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-base-patch4-window12-192-22k")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 21k ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swinv2.html#).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2111-09883,
author = {Ze Liu and
Han Hu and
Yutong Lin and
Zhuliang Yao and
Zhenda Xie and
Yixuan Wei and
Jia Ning and
Yue Cao and
Zheng Zhang and
Li Dong and
Furu Wei and
Baining Guo},
title = {Swin Transformer {V2:} Scaling Up Capacity and Resolution},
journal = {CoRR},
volume = {abs/2111.09883},
year = {2021},
url = {https://arxiv.org/abs/2111.09883},
eprinttype = {arXiv},
eprint = {2111.09883},
timestamp = {Thu, 02 Dec 2021 15:54:22 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2111-09883.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
haoranxu/ALMA-13B-R | haoranxu | "2024-01-19T05:18:00Z" | 8,115 | 71 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2401.08417",
"arxiv:2309.11674",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-01-17T11:41:20Z" | ---
license: mit
---
**[ALMA-R](https://arxiv.org/abs/2401.08417)** builds upon [ALMA models](https://arxiv.org/abs/2309.11674), with further LoRA fine-tuning with our proposed **Contrastive Preference Optimization (CPO)** as opposed to the Supervised Fine-tuning used in ALMA. CPO fine-tuning requires our [triplet preference data](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners!
```
@misc{xu2024contrastive,
title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation},
author={Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
year={2024},
eprint={2401.08417},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{xu2023paradigm,
title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models},
author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla},
year={2023},
eprint={2309.11674},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
# Download ALMA(-R) Models and Dataset 🚀
We release six translation models presented in the paper:
- ALMA-7B
- ALMA-7B-LoRA
- **ALMA-7B-R (NEW!)**: Further LoRA fine-tuning upon ALMA-7B-LoRA with contrastive preference optimization.
- ALMA-13B
- ALMA-13B-LoRA
- **ALMA-13B-R (NEW!)**: Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization (BEST MODEL!).
Model checkpoints are released at huggingface:
| Models | Base Model Link | LoRA Link |
|:-------------:|:---------------:|:---------:|
| ALMA-7B | [haoranxu/ALMA-7B](https://huggingface.co/haoranxu/ALMA-7B) | - |
| ALMA-7B-LoRA | [haoranxu/ALMA-7B-Pretrain](https://huggingface.co/haoranxu/ALMA-7B-Pretrain) | [haoranxu/ALMA-7B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-7B-Pretrain-LoRA) |
| **ALMA-7B-R (NEW!)** | [haoranxu/ALMA-7B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-7B-R) | - |
| ALMA-13B | [haoranxu/ALMA-13B](https://huggingface.co/haoranxu/ALMA-13B) | - |
| ALMA-13B-LoRA | [haoranxu/ALMA-13B-Pretrain](https://huggingface.co/haoranxu/ALMA-13B-Pretrain) | [haoranxu/ALMA-13B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-13B-Pretrain-LoRA) |
| **ALMA-13B-R (NEW!)** | [haoranxu/ALMA-13B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-13B-R) | - |
**Note that `ALMA-7B-Pretrain` and `ALMA-13B-Pretrain` are NOT translation models. They only experience stage 1 monolingual fine-tuning (20B tokens for the 7B model and 12B tokens for the 13B model), and should be utilized in conjunction with their LoRA models.**
Datasets used by ALMA and ALMA-R are also released at huggingface now (NEW!)
| Datasets | Train / Validation| Test |
|:-------------:|:---------------:|:---------:|
| Human-Written Parallel Data (ALMA) | [train and validation](https://huggingface.co/datasets/haoranxu/ALMA-Human-Parallel) | [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) |
| Triplet Preference Data | [train](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) | [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) and [WMT'23](https://huggingface.co/datasets/haoranxu/WMT23-Test) |
A quick start to use our best system (ALMA-13B-R) for translation. An example of translating "我爱机器翻译。" into English:
```
import torch
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
# Load base model and LoRA weights
model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-R", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("haoranxu/ALMA-13B-R", padding_side='left')
# Add the source sentence into the prompt template
prompt="Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"
input_ids = tokenizer(prompt, return_tensors="pt", padding=True, max_length=40, truncation=True).input_ids.cuda()
# Translation
with torch.no_grad():
generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_new_tokens=20, do_sample=True, temperature=0.6, top_p=0.9)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(outputs)
```
Please find more details in our [GitHub repository](https://github.com/fe1ixxu/ALMA) |
echarlaix/distilbert-base-uncased-finetuned-sst-2-english-openvino | echarlaix | "2022-11-22T10:42:52Z" | 8,110 | 0 | transformers | [
"transformers",
"openvino",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-10-11T09:43:26Z" | ---
language: en
license: apache-2.0
datasets:
- sst2
- glue
tags:
- openvino
---
## [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) exported to the OpenVINO IR.
## Model Details
**Model Description:** This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set.
## Usage example
You can use this model with Transformers *pipeline*.
```python
from transformers import AutoTokenizer, pipeline
from optimum.intel.openvino import OVModelForSequenceClassification
model_id = "echarlaix/distilbert-base-uncased-finetuned-sst-2-english-openvino"
model = OVModelForSequenceClassification.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
cls_pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "He's a dreadful magician."
outputs = cls_pipe(text)
```
|
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