modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-13 00:37:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
tomaarsen/span-marker-xlm-roberta-large-conll03-doc-context
|
tomaarsen
| 2023-09-12T19:49:09Z | 13 | 0 |
span-marker
|
[
"span-marker",
"pytorch",
"safetensors",
"token-classification",
"ner",
"named-entity-recognition",
"en",
"dataset:conll2003",
"dataset:tomaarsen/conll2003",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:apache-2.0",
"model-index",
"region:us"
] |
token-classification
| 2023-06-10T14:58:07Z |
---
language:
- en
license: apache-2.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
datasets:
- conll2003
- tomaarsen/conll2003
metrics:
- f1
- recall
- precision
pipeline_tag: token-classification
widget:
- text: Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic
to Paris.
example_title: Amelia Earhart
base_model: xlm-roberta-large
model-index:
- name: SpanMarker w. xlm-roberta-large on CoNLL03 with document-level context by
Tom Aarsen
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: CoNLL03 w. document context
type: conll2003
split: test
revision: 01ad4ad271976c5258b9ed9b910469a806ff3288
metrics:
- type: f1
value: 0.9442
name: F1
- type: precision
value: 0.9411
name: Precision
- type: recall
value: 0.9473
name: Recall
---
# SpanMarker for Named Entity Recognition
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) as the underlying encoder. See [train.py](train.py) for the training script.
Note that this model was trained with document-level context, i.e. it will primarily perform well when provided with enough context. It is recommended to call `model.predict` with a 🤗 Dataset with `tokens`, `document_id` and `sentence_id` columns.
See the [documentation](https://tomaarsen.github.io/SpanMarkerNER/api/span_marker.modeling.html#span_marker.modeling.SpanMarkerModel.predict) of the `model.predict` method for more information.
## Usage
To use this model for inference, first install the `span_marker` library:
```bash
pip install span_marker
```
You can then run inference with this model like so:
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-conll03-doc-context")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
```
### Limitations
**Warning**: This model works best when punctuation is separated from the prior words, so
```python
# ✅
model.predict("He plays J. Robert Oppenheimer , an American theoretical physicist .")
# ❌
model.predict("He plays J. Robert Oppenheimer, an American theoretical physicist.")
# You can also supply a list of words directly: ✅
model.predict(["He", "plays", "J.", "Robert", "Oppenheimer", ",", "an", "American", "theoretical", "physicist", "."])
```
The same may be beneficial for some languages, such as splitting `"l'ocean Atlantique"` into `"l' ocean Atlantique"`.
See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
|
guydebruyn/q-FrozenLake-v1-4x4-noSlippery
|
guydebruyn
| 2023-09-12T19:46:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T19:46:53Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="guydebruyn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
n7n7-music/n7n7-style
|
n7n7-music
| 2023-09-12T19:33:56Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-12T19:33:56Z |
---
license: creativeml-openrail-m
---
|
susnato/bert-base-uncased-issues-128
|
susnato
| 2023-09-12T19:28:18Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-12-16T15:07:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: bert-base-uncased
model-index:
- name: bert-base-uncased-issues-128
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0940
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1003 | 1.0 | 291 | 1.6578 |
| 1.6211 | 2.0 | 582 | 1.4140 |
| 1.4964 | 3.0 | 873 | 1.3040 |
| 1.41 | 4.0 | 1164 | 1.3011 |
| 1.336 | 5.0 | 1455 | 1.3095 |
| 1.2862 | 6.0 | 1746 | 1.3739 |
| 1.2271 | 7.0 | 2037 | 1.2743 |
| 1.2043 | 8.0 | 2328 | 1.2019 |
| 1.1701 | 9.0 | 2619 | 1.2696 |
| 1.1498 | 10.0 | 2910 | 1.2507 |
| 1.1194 | 11.0 | 3201 | 1.1398 |
| 1.1094 | 12.0 | 3492 | 1.1309 |
| 1.0913 | 13.0 | 3783 | 1.0740 |
| 1.0683 | 14.0 | 4074 | 1.1201 |
| 1.0607 | 15.0 | 4365 | 1.1690 |
| 1.0558 | 16.0 | 4656 | 1.0940 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.12.1
|
Jinheng-2002/dp-pokemon-1-9.11
|
Jinheng-2002
| 2023-09-12T19:24:49Z | 0 | 0 | null |
[
"tensorboard",
"stable-diffusion",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"region:us"
] | null | 2023-09-12T02:34:48Z |
---
base_model: CompVis/stable-diffusion-v1-4
tags:
- stable-diffusion
inference: true
---
|
federicochiarello/dqn-SpaceInvadersNoFrameskip-v4
|
federicochiarello
| 2023-09-12T19:08:40Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T19:08:06Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 532.00 +/- 120.23
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga federicochiarello -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga federicochiarello -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga federicochiarello
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia
|
nielsr
| 2023-09-12T18:35:03Z | 221 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-08-29T08:52:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
base_model: microsoft/swin-tiny-patch4-window7-224
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat-kornia
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.9829629629629629
name: Accuracy
---
<!-- 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-finetuned-eurosat-kornia
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0540
- Accuracy: 0.9830
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0859 | 1.0 | 190 | 0.0969 | 0.9685 |
| 0.0664 | 2.0 | 380 | 0.0627 | 0.9815 |
| 0.0359 | 3.0 | 570 | 0.0540 | 0.9830 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
MIT/ast-finetuned-audioset-16-16-0.442
|
MIT
| 2023-09-12T18:34:51Z | 412 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"audio-spectrogram-transformer",
"audio-classification",
"arxiv:2104.01778",
"license:bsd-3-clause",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-11-14T19:08:00Z |
---
license: bsd-3-clause
tags:
- audio-classification
---
# Audio Spectrogram Transformer (fine-tuned on AudioSet)
Audio Spectrogram Transformer (AST) model fine-tuned on AudioSet. It was introduced in the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Gong et al. and first released in [this repository](https://github.com/YuanGongND/ast).
Disclaimer: The team releasing Audio Spectrogram Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Audio Spectrogram Transformer is equivalent to [ViT](https://huggingface.co/docs/transformers/model_doc/vit), but applied on audio. Audio is first turned into an image (as a spectrogram), after which a Vision Transformer is applied. The model gets state-of-the-art results on several audio classification benchmarks.
## Usage
You can use the raw model for classifying audio into one of the AudioSet classes. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/audio-spectrogram-transformer) for more info.
|
Lethargus/Pyramids
|
Lethargus
| 2023-09-12T18:24:43Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-09-12T18:23:06Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Lethargus/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kamara3k/q-FrozenLake-v1-4x4-noSlippery
|
kamara3k
| 2023-09-12T18:19:55Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T18:19:52Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="kamara3k/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
shaowenchen/chinese-llama-2-13b-gguf
|
shaowenchen
| 2023-09-12T18:18:22Z | 55 | 0 | null |
[
"gguf",
"meta",
"llama",
"llama-2",
"chinese",
"text-generation",
"zh",
"license:other",
"region:us"
] |
text-generation
| 2023-09-12T09:26:08Z |
---
inference: false
language:
- zh
license: other
model_creator: ziqingyang
model_link: https://huggingface.co/ziqingyang/chinese-llama-2-13b
model_name: chinese-llama-2-13b
model_type: llama
pipeline_tag: text-generation
quantized_by: shaowenchen
tasks:
- text2text-generation
tags:
- meta
- gguf
- llama
- llama-2
- chinese
---
## Provided files
| Name | Quant method | Size |
| ------------------------------- | ------------ | ------ |
| chinese-llama-2-13b.Q2_K.gguf | Q2_K | 5.2 GB |
| chinese-llama-2-13b.Q3_K.gguf | Q3_K | 6.0 GB |
| chinese-llama-2-13b.Q3_K_L.gguf | Q3_K_L | 6.6 GB |
| chinese-llama-2-13b.Q3_K_S.gguf | Q3_K_S | 5.4 GB |
| chinese-llama-2-13b.Q4_0.gguf | Q4_0 | 7.0 GB |
| chinese-llama-2-13b.Q4_1.gguf | Q4_1 | 7.8 GB |
| chinese-llama-2-13b.Q4_K.gguf | Q4_K | 7.5 GB |
| chinese-llama-2-13b.Q4_K_S.gguf | Q4_K_S | 7.1 GB |
| chinese-llama-2-13b.Q5_0.gguf | Q5_0 | 8.5 GB |
| chinese-llama-2-13b.Q5_1.gguf | Q5_1 | 9.3 GB |
| chinese-llama-2-13b.Q5_K.gguf | Q5_K | 8.8 GB |
| chinese-llama-2-13b.Q5_K_S.gguf | Q5_K_S | 8.5 GB |
| chinese-llama-2-13b.Q6_K.gguf | Q6_K | 10 GB |
| chinese-llama-2-13b.Q8_0.gguf | Q8_0 | 13 GB |
| chinese-llama-2-13b.gguf | full | 25 GB |
Usage:
```
docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/gguf-model-name.gguf hubimage/llama-cpp-python:latest
```
and you can view http://localhost:8000/docs to see the swagger UI.
|
approach0/mathy-vicuna-13B-FFT-queryLM-adapter
|
approach0
| 2023-09-12T18:13:06Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"trl",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2023-09-12T18:12:46Z |
---
license: apache-2.0
tags:
- trl
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="w32zhong/output/rl__learn_query_lm_by_retriever_reward/watgpu-100-4tricks-no_cosine-no_refineprompt")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("w32zhong/output/rl__learn_query_lm_by_retriever_reward/watgpu-100-4tricks-no_cosine-no_refineprompt")
model = AutoModelForCausalLMWithValueHead.from_pretrained("w32zhong/output/rl__learn_query_lm_by_retriever_reward/watgpu-100-4tricks-no_cosine-no_refineprompt")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
trieudemo11/llama_miravia_17
|
trieudemo11
| 2023-09-12T17:51:16Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T17:50:59Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
GreenBitAI/LLaMA-3B-2bit-groupsize8
|
GreenBitAI
| 2023-09-12T17:44:27Z | 133 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-15T15:23:14Z |
---
license: apache-2.0
---
# GreenBit LLaMA
This is GreenBitAI's pretrained **2-bit** LLaMA model with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/low_bit_llama) for the code to run the model and more information.
## Zero-Shot Evaluation
| Task | Metric | LLaMA 3B q2g32 | LLaMA 3B q2g16 | LLaMA 3B q2g8 | LLaMA-1 7B q2g32 | LLaMA-2 7B q2g32 | LLaMA-2 7B q2g8 | LLaMA 3B FP16 | LLaMA-1 7B FP16 |
|---------------|----------|----------------|----------------|--------------|------------------|------------------|----------------|--------------|-----------------|
| Openbookqa | acc | 0.196 | 0.238 | 0.242 | 0.224 | 0.246 | 0.296 | 0.27 | 0.29 |
| | ac_norm | 0.332 | 0.358 | 0.362 | 0.388 | 0.376 | 0.4 | 0.4 | 0.41 |
| arc_challenge | acc | 0.279 | 0.2978 | 0.3148 | 0.3422 | 0.3268 | 0.3618 | 0.34 | 0.39 |
| | ac_norm | 0.2944 | 0.3319 | 0.3345 | 0.3387 | 0.3387 | 0.372 | 0.37 | 0.41 |
| hellawswag | acc | 0.4238 | 0.444 | 0.462 | 0.4996 | 0.4961 | 0.5379 | 0.49 | 0.68 |
| | ac_norm | 0.5685 | 0.5988 | 0.6242 | 0.6447 | 0.6464 | 0.7014 | 0.67 | 0.73 |
| piqa | acc | 0.7024 | 0.716 | 0.7291 | 0.7476 | 0.7503 | 0.7715 | 0.75 | 0.78 |
| | ac_norm | 0.7116 | 0.7247 | 0.7312 | 0.7443 | 0.7421 | 0.7568 | 0.76 | 0.78 |
| arc_easy | acc | 0.5997 | 0.646 | 0.6528 | 0.6061 | 0.6174 | 0.6254 | 0.69 | 0.68 |
| | ac_norm | 0.5417 | 0.58 | 0.5972 | 0.4566 | 0.4781 | 0.4958 | 0.65 | 0.52 |
| Winogrande | acc | 0.5683 | 0.5888 | 0.6054 | 0.6283 | 0.6298 | 0.6582 | 0.62 | 0.68 |
| boolq | acc | 0.6281 | 0.6636 | 0.6327 | 0.6425 | 0.7061 | 0.7242 | 0.68 | 0.75 |
| truthfulqa_mc | mc1 | 0.2509 | 0.2118 | 0.2252 | 0.224 | 0.2313 | 0.2399 | 0.22 | 0.21 |
| | mc2 | 0.3962 | 0.3501 | 0.3625 | 0.3702 | 0.3854 | 0.3795 | 0.35 | 0.34 |
| anli_r1 | acc | 0.337 | 0.334 | 0.344 | 0.331 | 0.333 | 0.363 | 0.33 | 0.35 |
| anli_r2 | acc | 0.335 | 0.332 | 0.331 | 0.326 | 0.349 | 0.347 | 0.32 | 0.34 |
| anli_r3 | acc | 0.3358 | 0.3383 | 0.3425 | 0.3417 | 0.36 | 0.3733 | 0.35 | 0.37 |
| wic | acc | 0.4984 | 0.5094 | 0.4969 | 0.4984 | 0.4953 | 0.489 | 0.48 | 0.5 |
| rte | acc | 0.5596 | 0.5993 | 0.5632 | 0.639 | 0.6065 | 0.6426 | 0.58 | 0.56 |
| record | f1 | 0.8502 | 0.8625 | 0.8687 | 0.8859 | 0.8872 | 0.9037 | 0.88 | 0.91 |
| | em | 0.8427 | 0.8545 | 0.8612 | 0.8781 | 0.8801 | 0.8959 | 0.89 | 0.91 |
| Average | | 0.4881 | 0.5037 | 0.5087 | 0.5122 | 0.5181 | 0.5391 | 0.528 | 0.5519 |
|
CyberHarem/sakurajima_mai_seishunbutayarou
|
CyberHarem
| 2023-09-12T17:41:52Z | 0 | 0 | null |
[
"art",
"text-to-image",
"dataset:CyberHarem/sakurajima_mai_seishunbutayarou",
"license:mit",
"region:us"
] |
text-to-image
| 2023-09-12T17:12:26Z |
---
license: mit
datasets:
- CyberHarem/sakurajima_mai_seishunbutayarou
pipeline_tag: text-to-image
tags:
- art
---
# Lora of sakurajima_mai_seishunbutayarou
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 9800, you need to download `9800/sakurajima_mai_seishunbutayarou.pt` as the embedding and `9800/sakurajima_mai_seishunbutayarou.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 9800**, with the score of 0.967. The trigger words are:
1. `sakurajima_mai_seishunbutayarou`
2. `long_hair, black_hair, hair_ornament, hairclip, purple_eyes, rabbit_hair_ornament, blue_eyes, bangs, necktie`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | pattern_19 | pattern_20 | pattern_21 | pattern_22 | pattern_23 | pattern_24 | pattern_25 | pattern_26 | pattern_27 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:-------------------------------------------|:---------------------------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:------------------------------------------------|:-------------------------------------------------|:---------------------------------------|:-------------------------------------------|
| 10500 | 0.942 | [Download](10500/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](10500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](10500/previews/nude.png) | [<NSFW, click to see>](10500/previews/nude2.png) |  |  |
| **9800** | **0.967** | [**Download**](9800/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](9800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](9800/previews/nude.png) | [<NSFW, click to see>](9800/previews/nude2.png) |  |  |
| 9100 | 0.914 | [Download](9100/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](9100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](9100/previews/nude.png) | [<NSFW, click to see>](9100/previews/nude2.png) |  |  |
| 8400 | 0.942 | [Download](8400/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8400/previews/nude.png) | [<NSFW, click to see>](8400/previews/nude2.png) |  |  |
| 7700 | 0.966 | [Download](7700/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7700/previews/nude.png) | [<NSFW, click to see>](7700/previews/nude2.png) |  |  |
| 7000 | 0.915 | [Download](7000/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7000/previews/nude.png) | [<NSFW, click to see>](7000/previews/nude2.png) |  |  |
| 6300 | 0.935 | [Download](6300/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6300/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6300/previews/nude.png) | [<NSFW, click to see>](6300/previews/nude2.png) |  |  |
| 5600 | 0.905 | [Download](5600/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5600/previews/nude.png) | [<NSFW, click to see>](5600/previews/nude2.png) |  |  |
| 4900 | 0.906 | [Download](4900/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4900/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4900/previews/nude.png) | [<NSFW, click to see>](4900/previews/nude2.png) |  |  |
| 4200 | 0.934 | [Download](4200/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4200/previews/nude.png) | [<NSFW, click to see>](4200/previews/nude2.png) |  |  |
| 3500 | 0.907 | [Download](3500/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3500/previews/nude.png) | [<NSFW, click to see>](3500/previews/nude2.png) |  |  |
| 2800 | 0.870 | [Download](2800/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2800/previews/nude.png) | [<NSFW, click to see>](2800/previews/nude2.png) |  |  |
| 2100 | 0.896 | [Download](2100/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2100/previews/nude.png) | [<NSFW, click to see>](2100/previews/nude2.png) |  |  |
| 1400 | 0.938 | [Download](1400/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [<NSFW, click to see>](1400/previews/nude2.png) |  |  |
| 700 | 0.915 | [Download](700/sakurajima_mai_seishunbutayarou.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](700/previews/nude.png) | [<NSFW, click to see>](700/previews/nude2.png) |  |  |
|
RafiAIProjects/q-FrozenLake-v1-4x4-noSlippery
|
RafiAIProjects
| 2023-09-12T17:40:25Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T17:40:22Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="RafiAIProjects/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
trieudemo11/llama_miravia_16
|
trieudemo11
| 2023-09-12T17:32:05Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T17:31:47Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
vipin22/pegasus-samsum-test
|
vipin22
| 2023-09-12T17:26:38Z | 9 | 0 |
transformers
|
[
"transformers",
"pegasus",
"text2text-generation",
"summarization",
"en",
"arxiv:1912.08777",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-09-12T10:41:32Z |
---
language: en
tags:
- summarization
---
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@sshleifer](https://twitter.com/sam_shleifer)
Task: Summarization
The following is copied from the authors' README.
# Mixed & Stochastic Checkpoints
We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table.
| dataset | C4 | HugeNews | Mixed & Stochastic|
| ---- | ---- | ---- | ----|
| xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64|
| cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30|
| newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18|
| multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95|
| gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76|
| wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *|
| reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94|
| big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *|
| arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67|
| pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25|
| aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51|
| billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59|
The "Mixed & Stochastic" model has the following changes:
- trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).
- trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
- the model uniformly sample a gap sentence ratio between 15% and 45%.
- importance sentences are sampled using a 20% uniform noise to importance scores.
- the sentencepiece tokenizer is updated to be able to encode newline character.
(*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data:
- wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information.
- we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS.
The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper):
trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).
trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
the model uniformly sample a gap sentence ratio between 15% and 45%.
importance sentences are sampled using a 20% uniform noise to importance scores.
the sentencepiece tokenizer is updated to be able to encode newline character.
Citation
```
@misc{zhang2019pegasus,
title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization},
author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu},
year={2019},
eprint={1912.08777},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Sehwan/R-Pred
|
Sehwan
| 2023-09-12T16:49:13Z | 0 | 0 | null |
[
"robotics",
"arxiv:2211.08609",
"license:apache-2.0",
"region:us"
] |
robotics
| 2023-09-12T16:23:29Z |
---
license: apache-2.0
pipeline_tag: robotics
---
> This is a model card to link the paper [https://arxiv.org/abs/2211.08609](https://arxiv.org/abs/2211.08609) to [ICCV Space demo](https://huggingface.co/spaces/ICCV2023/ICCV2023-papers).
[R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement](https://arxiv.org/abs/2211.08609)
Sehwan Choi, Jungho Kim, Junyong Yun, Jun Won Choi
|
Vigneshsundaram1006/flan-t5-small
|
Vigneshsundaram1006
| 2023-09-12T16:45:04Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-12T16:44:50Z |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-small
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-small
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1038
- Rouge1: 68.104
- Rouge2: 62.8731
- Rougel: 64.5026
- Rougelsum: 64.9706
- Gen Len: 18.2857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 8 | 2.2508 | 50.6686 | 41.7234 | 40.6222 | 39.9018 | 14.5714 |
| No log | 2.0 | 16 | 2.1618 | 67.043 | 61.7975 | 59.6406 | 59.8096 | 18.0 |
| No log | 3.0 | 24 | 2.1275 | 68.4011 | 63.5573 | 65.3917 | 65.8837 | 18.2857 |
| No log | 4.0 | 32 | 2.1100 | 67.547 | 62.8731 | 64.5026 | 64.9706 | 18.2857 |
| No log | 5.0 | 40 | 2.1038 | 68.104 | 62.8731 | 64.5026 | 64.9706 | 18.2857 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.13.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
pinedoalberto/MyModel
|
pinedoalberto
| 2023-09-12T16:42:24Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"es",
"dataset:tiiuae/falcon-refinedweb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-12T16:33:44Z |
---
license: apache-2.0
datasets:
- tiiuae/falcon-refinedweb
language:
- es
library_name: transformers
pipeline_tag: text-generation
---
|
akaashp15/foods_classification
|
akaashp15
| 2023-09-12T16:41:50Z | 2 | 0 |
tf-keras
|
[
"tf-keras",
"image-classification",
"en",
"dataset:food101",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2023-09-11T20:42:04Z |
---
datasets:
- food101
metrics:
- accuracy
license: apache-2.0
language:
- en
pipeline_tag: image-classification
---
|
kmuraish/CoMSES_spam_classification
|
kmuraish
| 2023-09-12T16:40:04Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-12T16:37:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6956
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 96 | 0.5343 | 1.0 |
| No log | 2.0 | 192 | 0.6173 | 1.0 |
| No log | 3.0 | 288 | 0.6956 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
trieudemo11/llama_miravia_13
|
trieudemo11
| 2023-09-12T16:34:50Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T16:34:33Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
Undi95/ReMM-v2.1-L2-13B-GGUF
|
Undi95
| 2023-09-12T16:26:55Z | 4 | 1 | null |
[
"gguf",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2023-09-12T16:02:56Z |
---
license: cc-by-nc-4.0
---
Re:MythoMax v2.1 (ReMM v2.1) is a recreation trial of the original [MythoMax-L2-B13](https://huggingface.co/Gryphe/MythoMax-L2-13b) with updated models.
This merge use SLERP merging method to merge ReML v2.1 and Huginn v1.2.
Explaination :
```shell
- ReML-v2.1: (Chronos-Beluga v2/Hermes/Airboros 2.2)
=> Keeping The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16
=> Replacing jondurbin/spicyboros-13b-2.2 by jondurbin/airoboros-l2-13b-2.2 (last version)
=> Keeping NousResearch/Nous-Hermes-Llama2-13b
With that :
- ReMM-v2.1: (ReML v2.1/Huginn v1.2)
=> Replacing ReML by the one above (ReML v2.1)
=> Keeping The-Face-Of-Goonery/Huginn-13b-v1.2
```
<!-- description start -->
## Description
This repo contains quantized files of ReMM v2.1, a recreation of the original MythoMax, but updated and merged with SLERP.
<!-- description end -->
<!-- description start -->
## Models used
- The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16
- jondurbin/airoboros-l2-13b-2.2
- NousResearch/Nous-Hermes-Llama2-13b
- The-Face-Of-Goonery/Huginn-13b-v1.2
- ReML-v2.1-L2-13B (Private recreation trial of an updated Mythologic-L2-13B)
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
Special thanks to Sushi.
|
dlosada/distilbert-base-uncased-finetuned-emotion
|
dlosada
| 2023-09-12T16:22:29Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-11T16:38:38Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9225
- name: F1
type: f1
value: 0.9228103521544558
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2159
- Accuracy: 0.9225
- F1: 0.9228
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8128 | 1.0 | 250 | 0.3071 | 0.9075 | 0.9069 |
| 0.2443 | 2.0 | 500 | 0.2159 | 0.9225 | 0.9228 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
trieudemo11/llama_miravia_12
|
trieudemo11
| 2023-09-12T16:15:46Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T16:15:22Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
odunola/bloomz_reriever_instruct
|
odunola
| 2023-09-12T16:10:16Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-10T01:43:17Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
CogwiseAI/dpo-falcon-7b
|
CogwiseAI
| 2023-09-12T16:07:49Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-09-12T16:07:49Z |
---
license: bigscience-openrail-m
---
|
vikrat/autotrain
|
vikrat
| 2023-09-12T15:53:52Z | 6 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"region:us"
] |
text-to-image
| 2023-09-11T04:18:57Z |
---
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: photo of VRR
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
anshulpatidar01/my-horse
|
anshulpatidar01
| 2023-09-12T15:49:51Z | 1 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-10T19:26:54Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-horse Dreambooth model trained by anshulpatidar01 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: IIITB-212
Sample pictures of this concept:
|
shuvom/pythia-70m-FT-Lamini-420
|
shuvom
| 2023-09-12T15:41:18Z | 203 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"en",
"dataset:lamini/lamini_docs",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-27T13:41:20Z |
---
datasets:
- lamini/lamini_docs
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
|
trieudemo11/llama_miravia_10
|
trieudemo11
| 2023-09-12T15:36:53Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T15:36:38Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
sjrhuschlee/bart-base-squad2
|
sjrhuschlee
| 2023-09-12T15:35:07Z | 127 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bart",
"question-answering",
"squad",
"squad_v2",
"en",
"dataset:squad_v2",
"dataset:squad",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-05-16T05:55:41Z |
---
language:
- en
license: mit
tags:
- bart
- question-answering
- squad
- squad_v2
datasets:
- squad_v2
- squad
base_model: facebook/bart-base
model-index:
- name: sjrhuschlee/bart-base-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: 75.223
name: Exact Match
- type: f1
value: 78.443
name: F1
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metrics:
- type: exact_match
value: 83.406
name: Exact Match
- type: f1
value: 90.377
name: F1
---
# bart-base for Extractive QA
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset.
## Overview
**Language model:** bart-base
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD 2.0
**Eval data:** SQuAD 2.0
**Infrastructure**: 1x NVIDIA 3070
## Model Usage
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "sjrhuschlee/bart-base-squad2"
# a) Using pipelines
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
qa_input = {
'question': 'Where do I live?',
'context': 'My name is Sarah and I live in London'
}
res = nlp(qa_input)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Metrics
```bash
# Squad v2
{
"eval_HasAns_exact": 76.45074224021593,
"eval_HasAns_f1": 82.88605283171232,
"eval_HasAns_total": 5928,
"eval_NoAns_exact": 74.01177460050462,
"eval_NoAns_f1": 74.01177460050462,
"eval_NoAns_total": 5945,
"eval_best_exact": 75.23793481007327,
"eval_best_exact_thresh": 0.0,
"eval_best_f1": 78.45098300230696,
"eval_best_f1_thresh": 0.0,
"eval_exact": 75.22951233892024,
"eval_f1": 78.44256053115387,
"eval_runtime": 131.875,
"eval_samples": 11955,
"eval_samples_per_second": 90.654,
"eval_steps_per_second": 3.784,
"eval_total": 11873
}
# Squad
{
"eval_exact_match": 83.40586565752129,
"eval_f1": 90.37706849113668,
"eval_runtime": 117.2093,
"eval_samples": 10619,
"eval_samples_per_second": 90.599,
"eval_steps_per_second": 3.78
}
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- max_seq_length 512
- doc_stride 128
- learning_rate: 2e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam8Bit with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4.0
- gradient_checkpointing: True
- tf32: True
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T15:34:43Z | 36 | 6 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"AWQ",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-19T03:14:49Z |
---
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- AWQ
---
# **Llama 2** (4-bit 128g AWQ Quantized)
Llama 2 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 7B pretrained model, converted for the Hugging Face Transformers format.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 19, 2023
## Model License
Please refer to the original LLaMA 2 model license ([link](https://huggingface.co/meta-llama/Llama-2-7b-hf)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f'''What is the difference between nuclear fusion and fission?
###Response:'''
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|12.1967| | |
| | |byte_perplexity| 1.5964| | |
| | |bits_per_byte | 0.6748| | |
[Llama-2-7b-chat (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|12.5962| | |
| | |byte_perplexity| 1.6060| | |
| | |bits_per_byte | 0.6835| | |
## Acknowledgements
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
MattStammers/vizdoom_my_way_home
|
MattStammers
| 2023-09-12T15:32:53Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T15:32:48Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_my_way_home
type: doom_my_way_home
metrics:
- type: mean_reward
value: 0.98 +/- 0.01
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_my_way_home** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MattStammers/vizdoom_my_way_home
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_my_way_home --train_dir=./train_dir --experiment=vizdoom_my_way_home
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_my_way_home --train_dir=./train_dir --experiment=vizdoom_my_way_home --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
typeof/phi-1_5
|
typeof
| 2023-09-12T15:30:46Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"custom_code",
"en",
"arxiv:2309.05463",
"license:other",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-09-12T15:27:58Z |
---
license: other
language:
- en
pipeline_tag: text-generation
---
## Model Summary
The language model phi-1.5 is a Transformer with 1.3 billion parameters. It was trained using the same data sources as [phi-1](https://huggingface.co/microsoft/phi-1), augmented with a new data source that consists of various NLP synthetic texts. When assessed against benchmarks testing common sense, language understanding, and logical reasoning, phi-1.5 demonstrates a nearly state-of-the-art performance among models with less than 10 billion parameters.
We did not fine-tune phi-1.5 either for instruction following or through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
For a safer model release, we exclude generic web-crawl data sources such as common-crawl from the training. This strategy prevents direct exposure to potentially harmful online content, enhancing the model's safety without RLHF. However, the model is still vulnerable to generating harmful content. We hope the model can help the research community to further study the safety of language models.
## Intended Uses
Given the nature of the training data, phi-1.5 is best suited for prompts using the QA format, the chat format, and the code format. Note that phi-1.5, being a base model, often produces irrelevant text following the main answer. In the following example, we've truncated the answer for illustrative purposes only.
#### QA format:
```markdown
Write a detailed analogy between mathematics and a lighthouse.
Answer: Mathematics is like a lighthouse, guiding us through the vast ocean of numbers and calculations. Just as a lighthouse illuminates the darkness, mathematics provides us with a clear path to navigate through complex problems. It helps us make sense of the world around us, just like a lighthouse helps ships find their way home.
```
where the model generates the text after "Answer:".
#### Chat format:
```markdown
Alice: Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
Bob: Have you tried using a timer? It can help you stay on track and avoid distractions.
Alice: That's a good idea. I'll give it a try.
Charlie: Another thing that can help is to break up your study sessions into smaller chunks. It's easier to concentrate on one thing at a time.
Alice: That makes sense. I'll try that too.
Bob: And don't forget to take breaks! It's important to give your brain a rest so you can come back to your studies with a fresh perspective.
Alice: Thanks for the advice, guys. I feel more motivated now.
Charlie: No problem, Alice. We're all in this together.
Bob: Yeah, and remember that it's okay to ask for help if you need it. We're here to support each other.
```
where the model generates the text after the first "Bob:".
#### Code format:
```python
def print_prime(n):
"""
Print all primes between 1 and n
"""
primes = []
for num in range(2, n+1):
is_prime = True
for i in range(2, int(math.sqrt(num))+1):
if num % i == 0:
is_prime = False
break
if is_prime:
primes.append(num)
print(primes)
```
where the model generates the text after the comments.
**Notes**
* phi-1.5 is intended for research purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.
* Direct adoption for production tasks is out of the scope of this research project. As a result, phi-1.5 has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details.
## Limitations of phi-1.5
* Generate Inaccurate Code and Facts: The model often produces incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
* Limited Scope for code: If the model generates Python scripts that utilize uncommon packages or scripts in other languages, we strongly recommend users manually verify all API uses.
* Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.
* Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other language outside of English might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
* Potential Societal Biases: Regardless of the safe data used for its training, the model is not entirely free from societal biases. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
* Toxicity: Despite that the model is trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.
## Training
### Model
* Architecture: a Transformer-based model with next-word prediction objective
* Dataset size: 30B tokens
* Training tokens: 150B tokens
* Precision: fp16
* GPUs: 32xA100-40G
* Training time: 8 days
### Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
* [flash-attention](https://github.com/HazyResearch/flash-attention)
### License
The model is licensed under the [Research License](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx).
### Sample Code
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
inputs = tokenizer('''```python
def print_prime(n):
"""
Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
**Remark.** In the generation function, our model currently does not support beam search (`num_beams` >1) and `attention_mask' parameters.
Furthermore, in the forward pass of the model, we currently do not support outputing hidden states or attention values, or using custom input embeddings (instead of the model's).
### Citation
```bib
@article{textbooks2,
title={Textbooks Are All You Need II: \textbf{phi-1.5} technical report},
author={Li, Yuanzhi and Bubeck, S{\'e}bastien and Eldan, Ronen and Del Giorno, Allie and Gunasekar, Suriya and Lee, Yin Tat},
journal={arXiv preprint arXiv:2309.05463},
year={2023}
}
```
|
Anastasiaps/dqn-SpaceInvadersNoFrameskip-v4
|
Anastasiaps
| 2023-09-12T15:27:18Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T15:26:41Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 561.00 +/- 179.04
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Anastasiaps -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Anastasiaps -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Anastasiaps
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
bigmorning/whisper_4_with_init_sun_syl_wd_0_lr_8en5_0010
|
bigmorning
| 2023-09-12T15:10:10Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-12T15:10:01Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0_lr_8en5_0010
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0_lr_8en5_0010
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8174
- Train Accuracy: 0.0283
- Train Wermet: 0.1811
- Train Wermet Syl: 0.1904
- Validation Loss: 0.9411
- Validation Accuracy: 0.0211
- Validation Wermet: 0.2923
- Validation Wermet Syl: 0.2616
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 8e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 4.9173 | 0.0113 | 1.2294 | 1.1313 | 4.0041 | 0.0114 | 0.9465 | 0.9188 | 0 |
| 4.6657 | 0.0117 | 0.8575 | 0.8170 | 3.8938 | 0.0114 | 0.9522 | 0.9285 | 1 |
| 4.6204 | 0.0118 | 0.8349 | 0.7915 | 3.8636 | 0.0115 | 0.9356 | 0.9050 | 2 |
| 4.5401 | 0.0120 | 0.7910 | 0.7460 | 3.7044 | 0.0119 | 0.8709 | 0.8276 | 3 |
| 4.0455 | 0.0134 | 0.7289 | 0.6945 | 2.6218 | 0.0143 | 0.7096 | 0.6705 | 4 |
| 2.8030 | 0.0175 | 0.5780 | 0.5793 | 1.9960 | 0.0162 | 0.5301 | 0.5039 | 5 |
| 1.9136 | 0.0214 | 0.4292 | 0.4643 | 1.3747 | 0.0187 | 0.4114 | 0.3873 | 6 |
| 1.3530 | 0.0245 | 0.3169 | 0.3444 | 1.0619 | 0.0205 | 0.3371 | 0.3019 | 7 |
| 1.0724 | 0.0263 | 0.2442 | 0.2674 | 0.9876 | 0.0209 | 0.3075 | 0.2717 | 8 |
| 0.8174 | 0.0283 | 0.1811 | 0.1904 | 0.9411 | 0.0211 | 0.2923 | 0.2616 | 9 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
trieudemo11/llama_miravia_8
|
trieudemo11
| 2023-09-12T14:57:35Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T14:57:13Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
hefeng0/my_awesome_wnut_model
|
hefeng0
| 2023-09-12T14:46:51Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-09-12T13:51:58Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5227272727272727
- name: Recall
type: recall
value: 0.29842446709916587
- name: F1
type: f1
value: 0.3799410029498525
- name: Accuracy
type: accuracy
value: 0.9395493993416271
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wnut_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2758
- Precision: 0.5227
- Recall: 0.2984
- F1: 0.3799
- Accuracy: 0.9395
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2952 | 0.5110 | 0.2159 | 0.3036 | 0.9366 |
| No log | 2.0 | 426 | 0.2758 | 0.5227 | 0.2984 | 0.3799 | 0.9395 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
2-2/self-asi
|
2-2
| 2023-09-12T14:39:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-09-12T14:20:00Z |
import sys
import subprocess
import platform
import warnings
warnings.filterwarnings("ignore")
import torch
from torch import nn
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import gym
class SimpleNavigationEnv(gym.Env):
"""Simple Navigation Environment"""
metadata = {'render.modes': ['ansi']}
def __init__(self):
super().__init__()
# Initialize environment variables
self.env = gym.make('CartPole-v1')
self.action_space = self.env.action_space
self.observation_space = self.env.observation_space
# Load tokenizer and LLM
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
llm =pipeline('text-classification', model="xlm-roberta-base", tokenizer="xlm-roberta-base")
# Define helper methods for interacting with the environment
def reset():
observation = self.env.reset()
return observation
def render(mode='ansi'):
return self.env.render(mode)
def step(action):
obs, reward, done, info = self.env.step(action)
return obs, reward, done, info
# Define the agent's behaviour
def interact(question):
# Tokenize question
tokens = tokenizer([question], padding=True, truncation=True, return_tensors='pt')
# Classify question using LLM
classifications = llm(tokens['input_ids'])
# Based on the classifier's output, decide on the next action
if classifications[0]['label']=='go left':
action = self.env.actions[0]
elif classifications[0]['label']=='go right':
action = self.env.actions[1]
else:
raise ValueError(f"Invalid command '{classifications[0]['label']}'")
# Perform the selected action and observe the new state
obs, reward, done, info = step(action)
return obs, reward, done, info
def close(self):
self.env.close()
class HybridAgent:
def __init__(self):
self.model_path = "hybrid_model.bin"
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.num_labels = 2
self.batch_size = 16
self.lr = 2e-5
self.epochs = 10
self.max_seq_len = 128
self.save_freq = 1
# Check if necessary dependencies are installed, otherwise install them
deps = ["transformers", "sentencepiece", "numpy"]
for dep in deps:
if dep not in sys.modules:
print(f"{dep} not found. Installing...")
pip_cmd = ["pip", "install", dep]
subprocess.run(pip_cmd)
# Initialize the LLM and TinyLM MT models
self.tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
self.llm = pipeline('text-classification', model="xlm-roberta-base", tokenizer="xlm-roberta-base")
self.tinylmmt = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-xlm-roberta-large-sentiment").to(self.device)
# Load the training data and split it into train/validation sets
df = pd.read_csv('./data/conversation_logs.csv')
self.train_df, self.val_df = train_test_split(df, test_size=.2)
def fit(self):
# Fine tune the combined model on the training data
optimizer = torch.optim.AdamW(list(self.tinylmmt.parameters()) + list(self.llm.model.classifier.parameters()), lr=self.lr)
loss_fn = nn.CrossEntropyLoss()
for epoch in range(self.epochs):
self.train_epoch(optimizer, loss_fn)
self.evaluate()
if (epoch+1)%self.save_freq==0:
self.save()
def train_epoch(self, optimizer, loss_fn):
self.tinylmmt.train()
self.llm.model.eval()
losses = []
|
AndrewL088/a2c-PandaReachDense-v3
|
AndrewL088
| 2023-09-12T14:37:32Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T14:31:56Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.23 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
openaccess-ai-collective/phi-platypus-qlora
|
openaccess-ai-collective
| 2023-09-12T14:37:10Z | 5 | 2 |
peft
|
[
"peft",
"safetensors",
"mixformer-sequential",
"custom_code",
"dataset:garage-bAInd/Open-Platypus",
"region:us"
] | null | 2023-09-12T12:52:03Z |
---
library_name: peft
datasets:
- garage-bAInd/Open-Platypus
---
# Phi 1.5 Playtpus
QLoRA trained for 2 epochs.
[<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)
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
egorishti/email-summarization-model-t5-v2
|
egorishti
| 2023-09-12T14:30:42Z | 114 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-12T13:07:47Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
model-index:
- name: output-test
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. -->
# output-test
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.5
- training_steps: 200
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
muhtasham/bert-tiny-finetuned-finer-longer
|
muhtasham
| 2023-09-12T14:20:58Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:finer-139",
"base_model:google/bert_uncased_L-2_H-128_A-2",
"base_model:finetune:google/bert_uncased_L-2_H-128_A-2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-25T01:58:25Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- finer-139
metrics:
- precision
- recall
- f1
- accuracy
base_model: google/bert_uncased_L-2_H-128_A-2
model-index:
- name: bertiny-finetuned-finer-full
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: finer-139
type: finer-139
args: finer-139
metrics:
- type: precision
value: 0.555368475586064
name: Precision
- type: recall
value: 0.5164398410213176
name: Recall
- type: f1
value: 0.5351972041937094
name: F1
- type: accuracy
value: 0.988733187308122
name: Accuracy
---
<!-- 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. -->
# bertiny-finetuned-finer-full
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the 10% of finer-139 dataset for 40 epochs according to paper.
It achieves the following results on the evaluation set:
- Loss: 0.0788
- Precision: 0.5554
- Recall: 0.5164
- F1: 0.5352
- Accuracy: 0.9887
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0852 | 1.0 | 11255 | 0.0929 | 1.0 | 0.0001 | 0.0002 | 0.9843 |
| 0.08 | 2.0 | 22510 | 0.0840 | 0.4626 | 0.0730 | 0.1261 | 0.9851 |
| 0.0759 | 3.0 | 33765 | 0.0750 | 0.5113 | 0.2035 | 0.2912 | 0.9865 |
| 0.0569 | 4.0 | 45020 | 0.0673 | 0.4973 | 0.3281 | 0.3953 | 0.9872 |
| 0.0488 | 5.0 | 56275 | 0.0635 | 0.5289 | 0.3749 | 0.4388 | 0.9878 |
| 0.0422 | 6.0 | 67530 | 0.0606 | 0.5258 | 0.4068 | 0.4587 | 0.9880 |
| 0.0364 | 7.0 | 78785 | 0.0600 | 0.5588 | 0.4186 | 0.4787 | 0.9883 |
| 0.0307 | 8.0 | 90040 | 0.0589 | 0.5223 | 0.4916 | 0.5065 | 0.9883 |
| 0.0284 | 9.0 | 101295 | 0.0595 | 0.5588 | 0.4813 | 0.5171 | 0.9887 |
| 0.0255 | 10.0 | 112550 | 0.0597 | 0.5606 | 0.4944 | 0.5254 | 0.9888 |
| 0.0223 | 11.0 | 123805 | 0.0600 | 0.5533 | 0.4998 | 0.5252 | 0.9888 |
| 0.0228 | 12.0 | 135060 | 0.0608 | 0.5290 | 0.5228 | 0.5259 | 0.9885 |
| 0.0225 | 13.0 | 146315 | 0.0612 | 0.5480 | 0.5111 | 0.5289 | 0.9887 |
| 0.0204 | 14.0 | 157570 | 0.0634 | 0.5646 | 0.5120 | 0.5370 | 0.9890 |
| 0.0176 | 15.0 | 168825 | 0.0639 | 0.5611 | 0.5135 | 0.5363 | 0.9889 |
| 0.0167 | 16.0 | 180080 | 0.0647 | 0.5631 | 0.5120 | 0.5363 | 0.9888 |
| 0.0161 | 17.0 | 191335 | 0.0665 | 0.5607 | 0.5081 | 0.5331 | 0.9889 |
| 0.0145 | 18.0 | 202590 | 0.0673 | 0.5437 | 0.5280 | 0.5357 | 0.9887 |
| 0.0166 | 19.0 | 213845 | 0.0687 | 0.5722 | 0.5008 | 0.5341 | 0.9889 |
| 0.0155 | 20.0 | 225100 | 0.0685 | 0.5325 | 0.5337 | 0.5331 | 0.9885 |
| 0.0142 | 21.0 | 236355 | 0.0705 | 0.5626 | 0.5166 | 0.5386 | 0.9890 |
| 0.0127 | 22.0 | 247610 | 0.0694 | 0.5426 | 0.5358 | 0.5392 | 0.9887 |
| 0.0112 | 23.0 | 258865 | 0.0721 | 0.5591 | 0.5129 | 0.5351 | 0.9888 |
| 0.0123 | 24.0 | 270120 | 0.0733 | 0.5715 | 0.5081 | 0.5380 | 0.9889 |
| 0.0116 | 25.0 | 281375 | 0.0735 | 0.5621 | 0.5123 | 0.5361 | 0.9888 |
| 0.0112 | 26.0 | 292630 | 0.0739 | 0.5634 | 0.5181 | 0.5398 | 0.9889 |
| 0.0108 | 27.0 | 303885 | 0.0753 | 0.5548 | 0.5155 | 0.5344 | 0.9887 |
| 0.0125 | 28.0 | 315140 | 0.0746 | 0.5507 | 0.5221 | 0.5360 | 0.9886 |
| 0.0093 | 29.0 | 326395 | 0.0762 | 0.5602 | 0.5156 | 0.5370 | 0.9888 |
| 0.0094 | 30.0 | 337650 | 0.0762 | 0.5625 | 0.5157 | 0.5381 | 0.9889 |
| 0.0117 | 31.0 | 348905 | 0.0767 | 0.5519 | 0.5195 | 0.5352 | 0.9887 |
| 0.0091 | 32.0 | 360160 | 0.0772 | 0.5501 | 0.5198 | 0.5345 | 0.9887 |
| 0.0109 | 33.0 | 371415 | 0.0775 | 0.5635 | 0.5097 | 0.5353 | 0.9888 |
| 0.0094 | 34.0 | 382670 | 0.0776 | 0.5467 | 0.5216 | 0.5339 | 0.9887 |
| 0.009 | 35.0 | 393925 | 0.0782 | 0.5601 | 0.5139 | 0.5360 | 0.9889 |
| 0.0093 | 36.0 | 405180 | 0.0780 | 0.5568 | 0.5156 | 0.5354 | 0.9888 |
| 0.0087 | 37.0 | 416435 | 0.0783 | 0.5588 | 0.5143 | 0.5356 | 0.9888 |
| 0.009 | 38.0 | 427690 | 0.0785 | 0.5483 | 0.5178 | 0.5326 | 0.9887 |
| 0.0094 | 39.0 | 438945 | 0.0787 | 0.5541 | 0.5154 | 0.5340 | 0.9887 |
| 0.0088 | 40.0 | 450200 | 0.0788 | 0.5554 | 0.5164 | 0.5352 | 0.9887 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bookbot/distil-ast-audioset
|
bookbot
| 2023-09-12T14:17:44Z | 5,422 | 18 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"en",
"arxiv:2104.01778",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-03-20T09:41:54Z |
---
language: en
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
metrics:
- accuracy
- f1
---
# Distil Audio Spectrogram Transformer AudioSet
Distil Audio Spectrogram Transformer AudioSet is an audio classification model based on the [Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) architecture. This model is a distilled version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the [AudioSet](https://research.google.com/audioset/download.html) dataset.
This model was trained using HuggingFace's PyTorch framework. All training was done on a Google Cloud Engine VM with a Tesla A100 GPU. All necessary scripts used for training could be found in the [Files and versions](https://huggingface.co/bookbot/distil-ast-audioset/tree/main) tab, as well as the [Training metrics](https://huggingface.co/bookbot/distil-ast-audioset/tensorboard) logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data |
| --------------------- | ------- | ----------------------------- | ------------------------ |
| `distil-ast-audioset` | 44M | Audio Spectrogram Transformer | AudioSet |
## Evaluation Results
The model achieves the following results on evaluation:
| Model | F1 | Roc Auc | Accuracy | mAP |
| ------------------- | ------ | ------- | -------- | ------ |
| Distil-AST AudioSet | 0.4876 | 0.7140 | 0.0714 | 0.4743 |
| AST AudioSet | 0.4989 | 0.6905 | 0.1247 | 0.5603 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- `learning_rate`: 3e-05
- `train_batch_size`: 32
- `eval_batch_size`: 32
- `seed`: 0
- `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`: 10.0
- `mixed_precision_training`: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Map |
| :-----------: | :---: | :---: | :-------------: | :----: | :-----: | :------: | :----: |
| 1.5521 | 1.0 | 153 | 0.7759 | 0.3929 | 0.6789 | 0.0209 | 0.3394 |
| 0.7088 | 2.0 | 306 | 0.5183 | 0.4480 | 0.7162 | 0.0349 | 0.4047 |
| 0.484 | 3.0 | 459 | 0.4342 | 0.4673 | 0.7241 | 0.0447 | 0.4348 |
| 0.369 | 4.0 | 612 | 0.3847 | 0.4777 | 0.7332 | 0.0504 | 0.4463 |
| 0.2943 | 5.0 | 765 | 0.3587 | 0.4838 | 0.7284 | 0.0572 | 0.4556 |
| 0.2446 | 6.0 | 918 | 0.3415 | 0.4875 | 0.7296 | 0.0608 | 0.4628 |
| 0.2099 | 7.0 | 1071 | 0.3273 | 0.4896 | 0.7246 | 0.0648 | 0.4682 |
| 0.186 | 8.0 | 1224 | 0.3140 | 0.4888 | 0.7171 | 0.0689 | 0.4711 |
| 0.1693 | 9.0 | 1377 | 0.3101 | 0.4887 | 0.7157 | 0.0703 | 0.4741 |
| 0.1582 | 10.0 | 1530 | 0.3063 | 0.4876 | 0.7140 | 0.0714 | 0.4743 |
## Disclaimer
Do consider the biases which came from pre-training datasets that may be carried over into the results of this model.
## Authors
Distil Audio Spectrogram Transformer AudioSet was trained and evaluated by [Ananto Joyoadikusumo](https://anantoj.github.io), [David Samuel Setiawan](https://davidsamuell.github.io/), [Wilson Wongso](https://wilsonwongso.dev/). All computation and development are done on Google Cloud.
## Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.10.0
- Tokenizers 0.13.2
|
Bolakubus/fine-finetuned-gtzan
|
Bolakubus
| 2023-09-12T14:15:48Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:Bolakubus/distilhubert-finetuned-gtzan",
"base_model:finetune:Bolakubus/distilhubert-finetuned-gtzan",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-09-12T12:33:06Z |
---
license: apache-2.0
base_model: Bolakubus/distilhubert-finetuned-gtzan
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: fine-fine-tuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.95
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-fine-tuned-gtzan
This model is a fine-tuned version of [Bolakubus/distilhubert-finetuned-gtzan](https://huggingface.co/Bolakubus/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2679
- Accuracy: 0.95
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1137 | 1.0 | 113 | 0.2356 | 0.96 |
| 0.3346 | 2.0 | 226 | 0.1993 | 0.96 |
| 0.0021 | 3.0 | 339 | 0.2483 | 0.95 |
| 0.0586 | 4.0 | 452 | 0.2372 | 0.95 |
| 0.0007 | 5.0 | 565 | 0.3228 | 0.95 |
| 0.0009 | 6.0 | 678 | 0.3505 | 0.95 |
| 0.0004 | 7.0 | 791 | 0.3106 | 0.94 |
| 0.0004 | 8.0 | 904 | 0.3105 | 0.95 |
| 0.0004 | 9.0 | 1017 | 0.2576 | 0.95 |
| 0.0004 | 10.0 | 1130 | 0.2679 | 0.95 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
MattStammers/vizdoom_deadly_corridor
|
MattStammers
| 2023-09-12T14:13:16Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-09-12T14:11:34Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_deadly_corridor
type: doom_deadly_corridor
metrics:
- type: mean_reward
value: 20.86 +/- 5.79
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_deadly_corridor** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MattStammers/vizdoom_deadly_corridor
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_deadly_corridor --train_dir=./train_dir --experiment=vizdoom_deadly_corridor
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_deadly_corridor --train_dir=./train_dir --experiment=vizdoom_deadly_corridor --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
pmfsl/bertimbau-base-finetuned-rte
|
pmfsl
| 2023-09-12T14:10:44Z | 67 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"pt",
"dataset:assin2",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:finetune:neuralmind/bert-base-portuguese-cased",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-04-04T20:56:54Z |
---
language:
- pt
license: mit
tags:
- generated_from_keras_callback
datasets:
- assin2
metrics:
- accuracy
- f1
pipeline_tag: text-classification
base_model: neuralmind/bert-base-portuguese-cased
model-index:
- name: pmfsl/bertimbau-base-finetuned-rte
results:
- task:
type: text-classification
name: Natural Lenguage Inference
dataset:
name: ASSIN2
type: assin2
metrics:
- type: accuracy
value: 0.877859477124183
- type: f1
value: 0.8860083873427372
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# pmfsl/bertimbau-base-finetuned-rte
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0326
- Validation Loss: 0.1834
- Test Loss: 0.5695
- Train Accuracy: 0.9531
- Train F1: 0.9534
- Test Accuracy: 0.8778
- Test F1: 0.8860
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 505, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Train F1 | Epoch |
|:----------:|:---------------:|:--------------:|:--------:|:-----:|
| 0.3846 | 0.2204 | 0.9152 | 0.9191 | 0 |
| 0.1981 | 0.1577 | 0.9442 | 0.9455 | 1 |
| 0.1026 | 0.1348 | 0.9509 | 0.9511 | 2 |
| 0.0593 | 0.1492 | 0.9531 | 0.9542 | 3 |
| 0.0326 | 0.1834 | 0.9531 | 0.9534 | 4 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.2
|
bigmorning/whisper_4_with_init_sun_syl_wd_0_lr_3en4_0015
|
bigmorning
| 2023-09-12T14:08:12Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-12T14:08:04Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0_lr_3en4_0015
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0_lr_3en4_0015
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5904
- Train Accuracy: 0.0299
- Train Wermet: 0.2126
- Train Wermet Syl: 0.3151
- Validation Loss: 1.2623
- Validation Accuracy: 0.0203
- Validation Wermet: 0.3877
- Validation Wermet Syl: 0.4289
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.0003, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 4.9621 | 0.0111 | 1.3737 | 1.2531 | 3.9542 | 0.0114 | 0.9773 | 0.9699 | 0 |
| 4.6813 | 0.0116 | 0.9321 | 0.9542 | 3.9070 | 0.0114 | 0.9669 | 0.9552 | 1 |
| 4.6267 | 0.0117 | 0.9053 | 0.9424 | 3.9109 | 0.0114 | 0.9465 | 0.9128 | 2 |
| 4.5866 | 0.0118 | 0.8961 | 0.9453 | 3.8787 | 0.0115 | 0.9210 | 0.8988 | 3 |
| 4.5246 | 0.0119 | 0.9086 | 1.0238 | 3.8947 | 0.0113 | 0.9346 | 0.8618 | 4 |
| 4.0723 | 0.0131 | 0.9445 | 1.1479 | 2.8783 | 0.0135 | 0.8293 | 0.9656 | 5 |
| 2.9304 | 0.0167 | 1.1693 | 1.7199 | 1.7990 | 0.0168 | 0.5867 | 0.6425 | 6 |
| 2.0844 | 0.0201 | 0.8414 | 1.1849 | 1.5133 | 0.0180 | 0.5178 | 0.5473 | 7 |
| 1.6228 | 0.0225 | 0.7801 | 1.0964 | 1.3701 | 0.0189 | 0.4690 | 0.5092 | 8 |
| 1.2715 | 0.0246 | 0.5866 | 0.8432 | 1.2543 | 0.0196 | 0.4646 | 0.5397 | 9 |
| 1.0487 | 0.0262 | 0.4624 | 0.6756 | 1.4263 | 0.0189 | 0.4109 | 0.4091 | 10 |
| 0.9402 | 0.0270 | 0.4029 | 0.5997 | 1.2081 | 0.0201 | 0.3824 | 0.3900 | 11 |
| 0.7721 | 0.0283 | 0.2795 | 0.4125 | 1.2559 | 0.0198 | 0.3685 | 0.3519 | 12 |
| 0.7018 | 0.0288 | 0.2506 | 0.3646 | 1.2722 | 0.0203 | 0.3594 | 0.3529 | 13 |
| 0.5904 | 0.0299 | 0.2126 | 0.3151 | 1.2623 | 0.0203 | 0.3877 | 0.4289 | 14 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
BubbleJoe/ppo-SnowballTarget
|
BubbleJoe
| 2023-09-12T14:07:48Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-09-12T14:07:41Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: BubbleJoe/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
moska/plt5-seq-clf-with-entities-updated-finetuned
|
moska
| 2023-09-12T14:00:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text-classification",
"generated_from_trainer",
"base_model:allegro/plt5-small",
"base_model:finetune:allegro/plt5-small",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-12T12:51:06Z |
---
license: cc-by-4.0
base_model: allegro/plt5-small
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: plt5-seq-clf-with-entities-updated-finetuned
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. -->
# plt5-seq-clf-with-entities-updated-finetuned
This model is a fine-tuned version of [allegro/plt5-small](https://huggingface.co/allegro/plt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9863
- Accuracy: {'accuracy': 0.6016129032258064}
- Recall: {'recall': 0.6016129032258064}
- F1: {'f1': 0.6090459454706235}
- Precision: {'precision': 0.6487538544674235}
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------:|:-------------------------------:|:---------------------------:|:----------------------------------:|
| 1.7248 | 1.0 | 718 | 1.6722 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.6984 | 2.0 | 1436 | 1.6216 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.6839 | 3.0 | 2154 | 1.6344 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.6882 | 4.0 | 2872 | 1.6261 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.68 | 5.0 | 3590 | 1.6223 | {'accuracy': 0.47258064516129034} | {'recall': 0.47258064516129034} | {'f1': 0.30332120269936047} | {'precision': 0.2233324661810614} |
| 1.6777 | 6.0 | 4308 | 1.6521 | {'accuracy': 0.4854838709677419} | {'recall': 0.4854838709677419} | {'f1': 0.36285141031634727} | {'precision': 0.30095036265938396} |
| 1.6681 | 7.0 | 5026 | 1.6165 | {'accuracy': 0.47096774193548385} | {'recall': 0.47096774193548385} | {'f1': 0.3142909197822259} | {'precision': 0.27758817356390014} |
| 1.6585 | 8.0 | 5744 | 1.5583 | {'accuracy': 0.47580645161290325} | {'recall': 0.47580645161290325} | {'f1': 0.3179514906044033} | {'precision': 0.274054689168981} |
| 1.6399 | 9.0 | 6462 | 1.6084 | {'accuracy': 0.3564516129032258} | {'recall': 0.3564516129032258} | {'f1': 0.30977675417942074} | {'precision': 0.3086887092441926} |
| 1.6158 | 10.0 | 7180 | 1.6613 | {'accuracy': 0.3225806451612903} | {'recall': 0.3225806451612903} | {'f1': 0.2777093706693196} | {'precision': 0.5070305497722745} |
| 1.5835 | 11.0 | 7898 | 1.6525 | {'accuracy': 0.3370967741935484} | {'recall': 0.3370967741935484} | {'f1': 0.2946753320835634} | {'precision': 0.4987499213117131} |
| 1.5443 | 12.0 | 8616 | 1.5433 | {'accuracy': 0.39838709677419354} | {'recall': 0.39838709677419354} | {'f1': 0.37257538542456536} | {'precision': 0.5472359482869795} |
| 1.4792 | 13.0 | 9334 | 1.4685 | {'accuracy': 0.4290322580645161} | {'recall': 0.4290322580645161} | {'f1': 0.3843028777529311} | {'precision': 0.5497170294652844} |
| 1.419 | 14.0 | 10052 | 1.5534 | {'accuracy': 0.4032258064516129} | {'recall': 0.4032258064516129} | {'f1': 0.35189485350144095} | {'precision': 0.5701307405449848} |
| 1.3881 | 15.0 | 10770 | 1.3641 | {'accuracy': 0.4790322580645161} | {'recall': 0.4790322580645161} | {'f1': 0.4461803399889066} | {'precision': 0.5258731490942117} |
| 1.3582 | 16.0 | 11488 | 1.3837 | {'accuracy': 0.43870967741935485} | {'recall': 0.43870967741935485} | {'f1': 0.3975785817347331} | {'precision': 0.5481481481481482} |
| 1.3074 | 17.0 | 12206 | 1.2409 | {'accuracy': 0.5177419354838709} | {'recall': 0.5177419354838709} | {'f1': 0.49737440159156987} | {'precision': 0.5439755251062998} |
| 1.2529 | 18.0 | 12924 | 1.2490 | {'accuracy': 0.5241935483870968} | {'recall': 0.5241935483870968} | {'f1': 0.5075488601971412} | {'precision': 0.5801964826379877} |
| 1.2223 | 19.0 | 13642 | 1.1680 | {'accuracy': 0.5435483870967742} | {'recall': 0.5435483870967742} | {'f1': 0.5172098120467532} | {'precision': 0.5483692723442298} |
| 1.1881 | 20.0 | 14360 | 1.1325 | {'accuracy': 0.5467741935483871} | {'recall': 0.5467741935483871} | {'f1': 0.528976565119481} | {'precision': 0.5918362760770626} |
| 1.1524 | 21.0 | 15078 | 1.1075 | {'accuracy': 0.5338709677419354} | {'recall': 0.5338709677419354} | {'f1': 0.5363641334830415} | {'precision': 0.6113524377471905} |
| 1.1307 | 22.0 | 15796 | 1.0685 | {'accuracy': 0.5612903225806452} | {'recall': 0.5612903225806452} | {'f1': 0.567131293394492} | {'precision': 0.6230821316117012} |
| 1.1198 | 23.0 | 16514 | 1.0978 | {'accuracy': 0.5564516129032258} | {'recall': 0.5564516129032258} | {'f1': 0.5596055517552543} | {'precision': 0.6285694241881432} |
| 1.0856 | 24.0 | 17232 | 1.0779 | {'accuracy': 0.5532258064516129} | {'recall': 0.5532258064516129} | {'f1': 0.5591833153283243} | {'precision': 0.6338935526492327} |
| 1.0829 | 25.0 | 17950 | 1.0175 | {'accuracy': 0.5903225806451613} | {'recall': 0.5903225806451613} | {'f1': 0.5964860501094582} | {'precision': 0.6422535611112073} |
| 1.0613 | 26.0 | 18668 | 1.0426 | {'accuracy': 0.567741935483871} | {'recall': 0.567741935483871} | {'f1': 0.5748961882147833} | {'precision': 0.6378855920377489} |
| 1.0363 | 27.0 | 19386 | 0.9920 | {'accuracy': 0.5935483870967742} | {'recall': 0.5935483870967742} | {'f1': 0.6001368374403852} | {'precision': 0.6385480642288512} |
| 1.0412 | 28.0 | 20104 | 1.0210 | {'accuracy': 0.5758064516129032} | {'recall': 0.5758064516129032} | {'f1': 0.5836230006413563} | {'precision': 0.6487093843541626} |
| 1.0256 | 29.0 | 20822 | 0.9992 | {'accuracy': 0.5870967741935483} | {'recall': 0.5870967741935483} | {'f1': 0.5944960724933464} | {'precision': 0.6439234847872369} |
| 1.0354 | 30.0 | 21540 | 0.9863 | {'accuracy': 0.6016129032258064} | {'recall': 0.6016129032258064} | {'f1': 0.6090459454706235} | {'precision': 0.6487538544674235} |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
Shishir1807/analytic-trogon
|
Shishir1807
| 2023-09-12T13:53:11Z | 151 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-09-12T13:52:32Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/analytic-trogon",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/analytic-trogon",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/analytic-trogon",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/analytic-trogon" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
fynn3003/Zip-Tie-Bag-lora-trained-sdxl
|
fynn3003
| 2023-09-12T13:47:41Z | 1 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-09-08T09:25:01Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of Zip Tie Bag
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - fynn3003/Zip-Tie-Bag-lora-trained-sdxl
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on photo of Zip Tie Bag using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
VMware/minilmv2-l12-h384-from-roberta-large-mrqa
|
VMware
| 2023-09-12T13:47:39Z | 133 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"en",
"dataset:mrqa",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-02-17T21:47:19Z |
---
license: apache-2.0
datasets:
- mrqa
language:
- en
metrics:
- exact_match
- f1
model-index:
- name: VMware/minilmv2-l12-h384-from-roberta-large-mrqa
results:
- task:
type: Question-Answering
dataset:
type: mrqa
name: MRQA
metrics:
- type: exact_match
value: 64.58
name: Eval EM
- type: f1
value: 76.23
name: Eval F1
- type: exact_match
value: 51.28
name: Test EM
- type: f1
value: 62.83
name: Test F1
---
This model release is part of a joint research project with Howard University's Innovation Foundry/AIM-AHEAD Lab.
# Model Details
- **Model name:** MiniLMv2-L12-H384-from-RoBERTa-Large-MRQA
- **Model type:** Extractive Question Answering
- **Parent Model:** [MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large)
- **Training dataset:** [MRQA](https://huggingface.co/datasets/mrqa) (Machine Reading for Question Answering)
- **Training data size:** 516,819 examples
- **Training time:** 4:14:22 on 1 Nvidia V100 32GB GPU
- **Language:** English
- **Framework:** PyTorch
- **Model version:** 1.0
# Intended Use
This model is intended to provide accurate answers to questions based on context passages. It can be used for a variety of tasks, including question-answering for search engines, chatbots, customer service systems, and other applications that require natural language understanding.
# How to Use
```python
from transformers import pipeline
question_answerer = pipeline("question-answering", model='VMware/minilmv2-l12-h384-from-roberta-large-mrqa')
context = "We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training and ensembling. The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than our initial baseline based on BERT."
question = "What is MRQA?"
result = question_answerer(question=question, context=context)
print(result)
# {
# 'score': 0.8364630937576294,
# 'start': 30,
# 'end': 68,
# 'answer': 'Machine Reading for Question Answering'
# }
```
# Training Details
The model was trained for 1 epoch on the MRQA training set.
## Training Hyperparameters
```python
args = TrainingArguments(
"minilmv2-l12-h384-from-roberta-large-mrqa",
save_strategy="epoch",
learning_rate=1e-5,
num_train_epochs=1,
weight_decay=0.01,
per_device_train_batch_size=16,
)
```
# Evaluation Metrics
The model was evaluated using standard metrics for question-answering models, including:
Exact match (EM): The percentage of questions for which the model produces an exact match with the ground truth answer.
F1 score: A weighted average of precision and recall, which measures the overlap between the predicted answer and the ground truth answer.
# Model Family Performance
| Parent Language Model | Number of Parameters | Training Time | Eval Time | Test Time | Eval EM | Eval F1 | Test EM | Test F1 |
|---|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| BERT-Tiny | 4,369,666 | 26:11 | 0:41 | 0:04 | 22.78 | 32.42 | 10.18 | 18.72 |
| BERT-Base | 108,893,186 | 8:39:10 | 18:42 | 2:13 | 64.48 | 76.14 | 48.89 | 59.89 |
| BERT-Large | 334,094,338 | 28:35:38 | 1:00:56 | 7:14 | 69.52 | 80.50 | 55.00 | 65.78 |
| DeBERTa-v3-Extra-Small | 70,682,882 | 5:19:05 | 11:29 | 1:16 | 65.58 | 77.17 | 50.92 | 62.58 |
| DeBERTa-v3-Base | 183,833,090 | 12:13:41 | 28:18 | 3:09 | 71.43 | 82.59 | 59.49 | 70.46 |
| DeBERTa-v3-Large | 434,014,210 | 38:36:13 | 1:25:47 | 9:33 | **76.08** | **86.23** | **64.27** | **75.22** |
| ELECTRA-Small | 13,483,522 | 2:16:36 | 3:55 | 0:27 | 57.63 | 69.38 | 38.68 | 51.56 |
| ELECTRA-Base | 108,893,186 | 8:40:57 | 18:41 | 2:12 | 68.78 | 80.16 | 54.70 | 65.80 |
| ELECTRA-Large | 334,094,338 | 28:31:59 | 1:00:40 | 7:13 | 74.15 | 84.96 | 62.35 | 73.28 |
| MiniLMv2-L6-H384-from-BERT-Large | 22,566,146 | 2:12:48 | 4:23 | 0:40 | 59.31 | 71.09 | 41.78 | 53.30 |
| MiniLMv2-L6-H768-from-BERT-Large | 66,365,954 | 4:42:59 | 10:01 | 1:10 | 64.27 | 75.84 | 49.05 | 59.82 |
| MiniLMv2-L6-H384-from-RoBERTa-Large | 30,147,842 | 2:15:10 | 4:19 | 0:30 | 59.27 | 70.64 | 42.95 | 54.03 |
| MiniLMv2-L12-H384-from-RoBERTa-Large | 40,794,626 | 4:14:22 | 8:27 | 0:58 | 64.58 | 76.23 | 51.28 | 62.83 |
| MiniLMv2-L6-H768-from-RoBERTa-Large | 81,529,346 | 4:39:02 | 9:34 | 1:06 | 65.80 | 77.17 | 51.72 | 63.27 |
| TinyRoBERTa | 81,529.346 | 4:27:06\* | 9:54 | 1:04 | 69.38 | 80.07 | 53.29 | 64.16 |
| RoBERTa-Base | 124,056,578 | 8:50:29 | 18:59 | 2:11 | 69.06 | 80.08 | 55.53 | 66.49 |
| RoBERTa-Large | 354,312,194 | 29:16:06 | 1:01:10 | 7:04 | 74.08 | 84.38 | 62.20 | 72.88 |
\* TinyRoBERTa's training time isn't directly comparable to the other models since it was distilled from [VMware/roberta-large-mrqa](https://huggingface.co/VMware/roberta-large-mrqa) that was already trained on MRQA.
# Limitations and Bias
The model is based on a large and diverse dataset, but it may still have limitations and biases in certain areas. Some limitations include:
- Language: The model is designed to work with English text only and may not perform as well on other languages.
- Domain-specific knowledge: The model has been trained on a general dataset and may not perform well on questions that require domain-specific knowledge.
- Out-of-distribution questions: The model may struggle with questions that are outside the scope of the MRQA dataset. This is best demonstrated by the delta between its scores on the eval vs test datasets.
In addition, the model may have some bias in terms of the data it was trained on. The dataset includes questions from a variety of sources, but it may not be representative of all populations or perspectives. As a result, the model may perform better or worse for certain types of questions or on certain types of texts.
|
ARTeLab/mbart-summarization-fanpage
|
ARTeLab
| 2023-09-12T13:43:38Z | 171 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/fanpage",
"base_model:facebook/mbart-large-cc25",
"base_model:finetune:facebook/mbart-large-cc25",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:04Z |
---
language:
- it
tags:
- summarization
datasets:
- ARTeLab/fanpage
metrics:
- rouge
base_model: facebook/mbart-large-cc25
model-index:
- name: summarization_mbart_fanpage4epoch
results: []
---
# mbart-summarization-fanpage
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on Fanpage dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 2.1833
- Rouge1: 36.5027
- Rouge2: 17.4428
- Rougel: 26.1734
- Rougelsum: 30.2636
- Gen Len: 75.2413
## Usage
```python
from transformers import MBartTokenizer, MBartForConditionalGeneration
tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-fanpage")
model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-fanpage")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.15.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
# Citation
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
```
|
ARTeLab/it5-summarization-fanpage
|
ARTeLab
| 2023-09-12T13:43:22Z | 161 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/fanpage",
"base_model:gsarti/it5-base",
"base_model:finetune:gsarti/it5-base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:04Z |
---
language:
- it
tags:
- summarization
datasets:
- ARTeLab/fanpage
metrics:
- rouge
base_model: gsarti/it5-base
model-index:
- name: summarization_fanpage128
results: []
---
# summarization_fanpage128
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on Fanpage dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 1.5348
- Rouge1: 34.1882
- Rouge2: 15.7866
- Rougel: 25.141
- Rougelsum: 28.4882
- Gen Len: 69.3041
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-fanpage-128")
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-fanpage-128")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
# Citation
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
```
|
ARTeLab/it5-summarization-ilpost
|
ARTeLab
| 2023-09-12T13:43:14Z | 148 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/ilpost",
"base_model:gsarti/it5-base",
"base_model:finetune:gsarti/it5-base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:04Z |
---
language:
- it
tags:
- summarization
datasets:
- ARTeLab/ilpost
metrics:
- rouge
base_model: gsarti/it5-base
model-index:
- name: summarization_ilpost
results: []
---
# summarization_ilpost
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on IlPost dataset for Abstractive Summarization.
It achieves the following results:
- Loss: 1.6020
- Rouge1: 33.7802
- Rouge2: 16.2953
- Rougel: 27.4797
- Rougelsum: 30.2273
- Gen Len: 45.3175
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-ilpost")
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-ilpost")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
ARTeLab/it5-summarization-mlsum
|
ARTeLab
| 2023-09-12T13:43:07Z | 153 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"it",
"dataset:ARTeLab/mlsum-it",
"base_model:gsarti/it5-base",
"base_model:finetune:gsarti/it5-base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:04Z |
---
language:
- it
tags:
- summarization
datasets:
- ARTeLab/mlsum-it
metrics:
- rouge
base_model: gsarti/it5-base
model-index:
- name: summarization_mlsum
results: []
---
# summarization_mlsum
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on MLSum-it for Abstractive Summarization.
It achieves the following results:
- Loss: 2.0190
- Rouge1: 19.3739
- Rouge2: 5.9753
- Rougel: 16.691
- Rougelsum: 16.7862
- Gen Len: 32.5268
## Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-mlsum")
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-mlsum")
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
# Citation
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
```
|
artyomboyko/speecht5_finetuned_voxpopuli_nl
|
artyomboyko
| 2023-09-12T13:41:48Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-08-24T12:40:58Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- voxpopuli
pipeline_tag: text-to-speech
base_model: microsoft/speecht5_tts
model-index:
- name: speecht5_finetuned_voxpopuli_nl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4563
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5255 | 4.3 | 1000 | 0.4760 |
| 0.5017 | 8.61 | 2000 | 0.4619 |
| 0.4961 | 12.91 | 3000 | 0.4564 |
| 0.4881 | 17.21 | 4000 | 0.4563 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
artyomboyko/whisper-small-fine_tuned-ru
|
artyomboyko
| 2023-09-12T13:41:27Z | 77 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-08-14T20:27:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: whisper-small-fine_tuned-ru
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-fine_tuned-ru
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [Mozilla common_voice_13_0](mozilla-foundation/common_voice_13_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2290
- Wer: 17.6336
## 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-07
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 5000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0286 | 0.22 | 500 | 0.2225 | 18.0199 |
| 0.0287 | 0.44 | 1000 | 0.2235 | 18.0455 |
| 0.0334 | 0.66 | 1500 | 0.2243 | 18.1956 |
| 0.0373 | 0.88 | 2000 | 0.2239 | 17.9803 |
| 0.0261 | 1.1 | 2500 | 0.2268 | 17.7674 |
| 0.0252 | 1.32 | 3000 | 0.2277 | 17.8221 |
| 0.0265 | 1.54 | 3500 | 0.2290 | 17.6336 |
| 0.0271 | 1.76 | 4000 | 0.2293 | 17.8279 |
| 0.0252 | 1.97 | 4500 | 0.2293 | 17.8744 |
| 0.023 | 2.19 | 5000 | 0.2298 | 17.8930 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
trieudemo11/llama_miravia_4
|
trieudemo11
| 2023-09-12T13:39:53Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T13:39:38Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
LarryAIDraw/rita_v1
|
LarryAIDraw
| 2023-09-12T13:30:37Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-12T13:23:11Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/142713/rita-rose-oathhonkai-impact-3
|
LarryAIDraw/michiru_hyodo_v1
|
LarryAIDraw
| 2023-09-12T13:28:14Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-09-12T13:09:33Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/142388/michiru-hyodo-or-saekano-how-to-raise-a-boring-girlfriend
|
peteryushunli/bert-finetuned-hausa_ner
|
peteryushunli
| 2023-09-12T13:21:50Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:hausa_voa_ner",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-08-28T02:44:09Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- hausa_voa_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-hausa_ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: hausa_voa_ner
type: hausa_voa_ner
config: hausa_voa_ner
split: validation
args: hausa_voa_ner
metrics:
- name: Precision
type: precision
value: 0.6781609195402298
- name: Recall
type: recall
value: 0.7763157894736842
- name: F1
type: f1
value: 0.7239263803680982
- name: Accuracy
type: accuracy
value: 0.9516353514265832
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-hausa_ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the hausa_voa_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1734
- Precision: 0.6782
- Recall: 0.7763
- F1: 0.7239
- Accuracy: 0.9516
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 127 | 0.2162 | 0.6992 | 0.7342 | 0.7163 | 0.9516 |
| No log | 2.0 | 254 | 0.1702 | 0.6900 | 0.7789 | 0.7318 | 0.9518 |
| No log | 3.0 | 381 | 0.1734 | 0.6782 | 0.7763 | 0.7239 | 0.9516 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
trieudemo11/llama_miravia_3
|
trieudemo11
| 2023-09-12T13:20:39Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T13:20:23Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
ldos/text_shortening_model_v34
|
ldos
| 2023-09-12T13:20:28Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-xsum",
"base_model:finetune:facebook/bart-large-xsum",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-12T07:52:55Z |
---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v34
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. -->
# text_shortening_model_v34
This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7697
- Rouge1: 0.4731
- Rouge2: 0.253
- Rougel: 0.4166
- Rougelsum: 0.416
- Bert precision: 0.8697
- Bert recall: 0.8697
- Average word count: 8.7087
- Max word count: 17
- Min word count: 5
- Average token count: 16.3093
- % shortened texts with length > 12: 6.6066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 2.4675 | 1.0 | 19 | 3.1777 | 0.4029 | 0.1769 | 0.3503 | 0.3498 | 0.8509 | 0.857 | 9.6577 | 17 | 5 | 15.4324 | 10.2102 |
| 1.1669 | 2.0 | 38 | 1.9224 | 0.4506 | 0.2396 | 0.4184 | 0.4181 | 0.864 | 0.8688 | 8.6306 | 15 | 5 | 14.2613 | 4.2042 |
| 0.9292 | 3.0 | 57 | 1.7461 | 0.4654 | 0.2556 | 0.4186 | 0.419 | 0.8654 | 0.8722 | 9.0751 | 17 | 5 | 14.9099 | 4.2042 |
| 0.7876 | 4.0 | 76 | 1.9057 | 0.4003 | 0.207 | 0.367 | 0.366 | 0.8539 | 0.8516 | 8.1021 | 13 | 5 | 16.2883 | 1.2012 |
| 0.5976 | 5.0 | 95 | 1.7603 | 0.4776 | 0.2636 | 0.4254 | 0.4248 | 0.8659 | 0.8754 | 9.1952 | 16 | 5 | 15.0961 | 6.006 |
| 0.469 | 6.0 | 114 | 2.1107 | 0.4675 | 0.2542 | 0.4077 | 0.4081 | 0.856 | 0.8776 | 11.1802 | 20 | 5 | 18.4505 | 31.5315 |
| 0.4291 | 7.0 | 133 | 1.7980 | 0.4701 | 0.2509 | 0.4202 | 0.4195 | 0.8647 | 0.8723 | 9.1832 | 15 | 5 | 14.7267 | 6.3063 |
| 0.3673 | 8.0 | 152 | 1.9170 | 0.4669 | 0.2574 | 0.4188 | 0.4187 | 0.8678 | 0.8698 | 8.6306 | 18 | 5 | 14.3093 | 3.9039 |
| 0.3432 | 9.0 | 171 | 2.0268 | 0.4804 | 0.2691 | 0.4254 | 0.4249 | 0.8682 | 0.8753 | 9.2402 | 18 | 5 | 14.6847 | 9.3093 |
| 0.3094 | 10.0 | 190 | 2.1107 | 0.4809 | 0.2724 | 0.4353 | 0.4337 | 0.8689 | 0.8739 | 9.2883 | 17 | 4 | 16.2162 | 9.009 |
| 0.4402 | 11.0 | 209 | 2.2507 | 0.4816 | 0.268 | 0.428 | 0.4278 | 0.8668 | 0.8743 | 9.4805 | 18 | 4 | 16.6126 | 10.8108 |
| 0.3691 | 12.0 | 228 | 2.1652 | 0.4784 | 0.2637 | 0.4286 | 0.4277 | 0.8683 | 0.8714 | 8.7988 | 15 | 5 | 14.5105 | 6.006 |
| 0.1853 | 13.0 | 247 | 2.3660 | 0.4705 | 0.259 | 0.4119 | 0.4115 | 0.8686 | 0.8695 | 8.7898 | 17 | 5 | 16.2432 | 6.6066 |
| 0.3186 | 14.0 | 266 | 2.3237 | 0.4817 | 0.27 | 0.4273 | 0.4271 | 0.8698 | 0.8738 | 8.973 | 17 | 5 | 16.5976 | 9.3093 |
| 0.1745 | 15.0 | 285 | 2.2675 | 0.4672 | 0.2577 | 0.4177 | 0.4165 | 0.8698 | 0.8694 | 8.6066 | 16 | 5 | 14.7117 | 3.9039 |
| 0.1304 | 16.0 | 304 | 2.5157 | 0.4726 | 0.253 | 0.418 | 0.4167 | 0.8691 | 0.8688 | 8.6517 | 17 | 4 | 15.8468 | 3.9039 |
| 0.1432 | 17.0 | 323 | 2.4798 | 0.4744 | 0.2614 | 0.4204 | 0.4196 | 0.869 | 0.8725 | 8.9189 | 17 | 5 | 15.5015 | 6.006 |
| 0.1116 | 18.0 | 342 | 2.5924 | 0.4772 | 0.2589 | 0.4222 | 0.4221 | 0.87 | 0.8717 | 8.7508 | 17 | 5 | 15.6096 | 6.9069 |
| 0.0921 | 19.0 | 361 | 2.6547 | 0.4733 | 0.2541 | 0.4205 | 0.4199 | 0.8694 | 0.8694 | 8.6787 | 16 | 5 | 15.4204 | 6.006 |
| 0.0679 | 20.0 | 380 | 2.7697 | 0.4731 | 0.253 | 0.4166 | 0.416 | 0.8697 | 0.8697 | 8.7087 | 17 | 5 | 16.3093 | 6.6066 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Shishir1807/stylish-impala
|
Shishir1807
| 2023-09-12T13:19:36Z | 149 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-09-12T13:18:59Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/stylish-impala",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/stylish-impala",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/stylish-impala",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/stylish-impala" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
tum-nlp/IDMGSP-RoBERTa-TRAIN-CONCLUSION
|
tum-nlp
| 2023-09-12T13:17:13Z | 115 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"dataset:tum-nlp/IDMGSP",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-11T13:30:16Z |
---
datasets:
- tum-nlp/IDMGSP
license: openrail++
---
|
tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-ABSTRACT
|
tum-nlp
| 2023-09-12T13:16:56Z | 117 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"dataset:tum-nlp/IDMGSP",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-11T13:34:14Z |
---
datasets:
- tum-nlp/IDMGSP
license: openrail++
---
|
mjwong/mcontriever-msmarco-xnli
|
mjwong
| 2023-09-12T13:12:46Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"zero-shot-classification",
"multilingual",
"en",
"ar",
"bg",
"de",
"el",
"es",
"fr",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh",
"dataset:xnli",
"arxiv:2112.09118",
"base_model:facebook/mcontriever-msmarco",
"base_model:finetune:facebook/mcontriever-msmarco",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
zero-shot-classification
| 2023-06-12T16:36:40Z |
---
language:
- multilingual
- en
- ar
- bg
- de
- el
- es
- fr
- ru
- sw
- th
- tr
- ur
- vi
- zh
license: mit
datasets:
- xnli
pipeline_tag: zero-shot-classification
widget:
- text: Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU
candidate_labels: politics, economy, entertainment, environment
base_model: facebook/mcontriever-msmarco
model-index:
- name: mcontriever-msmarco-xnli
results: []
---
# mcontriever-msmarco-xnli
This model is a fine-tuned version of [facebook/mcontriever-msmarco](https://huggingface.co/facebook/mcontriever-msmarco) on the XNLI dataset.
## Model description
[Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118).
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, Edouard Grave, arXiv 2021
## How to use the model
### With the zero-shot classification pipeline
The model can be loaded with the `zero-shot-classification` pipeline like so:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="mjwong/mcontriever-msmarco-xnli")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
classifier(sequence_to_classify, candidate_labels)
```
If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
```python
candidate_labels = ["politics", "economy", "entertainment", "environment"]
classifier(sequence_to_classify, candidate_labels, multi_label=True)
```
### With manual PyTorch
The model can also be applied on NLI tasks like so:
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# device = "cuda:0" or "cpu"
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "mjwong/mcontriever-msmarco-xnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "But I thought you'd sworn off coffee."
hypothesis = "I thought that you vowed to drink more coffee."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device))
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 2) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Eval results
The model was evaluated using the XNLI test sets on 14 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnam (vi) and Chinese (zh). The metric used is accuracy.
|Datasets|en|ar|bg|de|el|es|fr|ru|sw|th|tr|ur|vi|zh|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|[mcontriever-xnli](https://huggingface.co/mjwong/mcontriever-xnli)|0.820|0.733|0.773|0.774|0.748|0.788|0.781|0.755|0.690|0.690|0.741|0.647|0.766|0.767|
|[mcontriever-msmarco-xnli](https://huggingface.co/mjwong/mcontriever-msmarco-xnli)|0.822|0.731|0.763|0.775|0.752|0.785|0.778|0.749|0.694|0.682|0.738|0.641|0.759|0.768|
### 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.11.0
- Tokenizers 0.12.1
|
mjwong/mcontriever-xnli
|
mjwong
| 2023-09-12T13:12:39Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"zero-shot-classification",
"multilingual",
"en",
"ar",
"bg",
"de",
"el",
"es",
"fr",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh",
"dataset:xnli",
"arxiv:2112.09118",
"base_model:facebook/mcontriever",
"base_model:finetune:facebook/mcontriever",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
zero-shot-classification
| 2023-06-14T15:33:23Z |
---
language:
- multilingual
- en
- ar
- bg
- de
- el
- es
- fr
- ru
- sw
- th
- tr
- ur
- vi
- zh
license: mit
datasets:
- xnli
pipeline_tag: zero-shot-classification
widget:
- text: Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU
candidate_labels: politics, economy, entertainment, environment
base_model: facebook/mcontriever
model-index:
- name: mcontriever-xnli
results: []
---
# mcontriever-xnli
This model is a fine-tuned version of [facebook/mcontriever](https://huggingface.co/facebook/mcontriever) on the XNLI dataset.
## Model description
[Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118).
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, Edouard Grave, arXiv 2021
## How to use the model
### With the zero-shot classification pipeline
The model can be loaded with the `zero-shot-classification` pipeline like so:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="mjwong/mcontriever-xnli")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
classifier(sequence_to_classify, candidate_labels)
```
If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
```python
candidate_labels = ["politics", "economy", "entertainment", "environment"]
classifier(sequence_to_classify, candidate_labels, multi_label=True)
```
### With manual PyTorch
The model can also be applied on NLI tasks like so:
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# device = "cuda:0" or "cpu"
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "mjwong/mcontriever-xnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "But I thought you'd sworn off coffee."
hypothesis = "I thought that you vowed to drink more coffee."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device))
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 2) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Eval results
The model was evaluated using the XNLI test sets on 14 languages: English (en), Arabic (ar), Bulgarian (bg), German (de), Greek (el), Spanish (es), French (fr), Russian (ru), Swahili (sw), Thai (th), Turkish (tr), Urdu (ur), Vietnam (vi) and Chinese (zh). The metric used is accuracy.
|Datasets|en|ar|bg|de|el|es|fr|ru|sw|th|tr|ur|vi|zh|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|[mcontriever-xnli](https://huggingface.co/mjwong/mcontriever-xnli)|0.820|0.733|0.773|0.774|0.748|0.788|0.781|0.755|0.690|0.690|0.741|0.647|0.766|0.767|
|[mcontriever-msmarco-xnli](https://huggingface.co/mjwong/mcontriever-msmarco-xnli)|0.822|0.731|0.763|0.775|0.752|0.785|0.778|0.749|0.694|0.682|0.738|0.641|0.759|0.768|
### 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.11.0
- Tokenizers 0.12.1
|
mjwong/contriever-msmarco-mnli
|
mjwong
| 2023-09-12T13:11:49Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"zero-shot-classification",
"en",
"dataset:glue",
"arxiv:2112.09118",
"base_model:facebook/contriever-msmarco",
"base_model:finetune:facebook/contriever-msmarco",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
zero-shot-classification
| 2023-05-19T02:00:44Z |
---
language:
- en
license: mit
datasets:
- glue
pipeline_tag: zero-shot-classification
base_model: facebook/contriever-msmarco
model-index:
- name: contriever-msmarco-mnli
results: []
---
# contriever-msmarco-mnli
This model is a fine-tuned version of [facebook/contriever-msmarco](https://huggingface.co/facebook/contriever-msmarco) on the glue dataset.
## Model description
[Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118).
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, Edouard Grave, arXiv 2021
## How to use the model
### With the zero-shot classification pipeline
The model can be loaded with the `zero-shot-classification` pipeline like so:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="mjwong/contriever-msmarco-mnli")
```
You can then use this pipeline to classify sequences into any of the class names you specify.
```python
sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
```
If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:
```python
candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
classifier(sequence_to_classify, candidate_labels, multi_class=True)
```
### With manual PyTorch
The model can also be applied on NLI tasks like so:
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# device = "cuda:0" or "cpu"
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "mjwong/contriever-msmarco-mnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "But I thought you'd sworn off coffee."
hypothesis = "I thought that you vowed to drink more coffee."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device))
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 2) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Eval results
The model was evaluated using the dev sets for MultiNLI and test sets for ANLI. The metric used is accuracy.
|Datasets|mnli_dev_m|mnli_dev_mm|anli_test_r1|anli_test_r2|anli_test_r3|
| :---: | :---: | :---: | :---: | :---: | :---: |
|[contriever-mnli](https://huggingface.co/mjwong/contriever-mnli)|0.821|0.822|0.247|0.281|0.312|
|[contriever-msmarco-mnli](https://huggingface.co/mjwong/contriever-msmarco-mnli)|0.820|0.819|0.244|0.296|0.306|
### 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu116
- Datasets 2.11.0
- Tokenizers 0.12.1
|
SBB/sbb_ned-de
|
SBB
| 2023-09-12T13:10:42Z | 0 | 1 | null |
[
"pytorch",
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] | null | 2023-09-07T11:52:31Z |
---
license: apache-2.0
---
# Model Card for sbb_ned-de
<!-- Provide a quick summary of what the model is/does. -->
This model is part of a named entity disambiguation and linking system (NED, NEL).
The system was developed by Berlin State Library (SBB) in the [QURATOR](https://staatsbibliothek-berlin.de/die-staatsbibliothek/projekte/project-id-1060-2018) project.
Questions and comments about the model can be directed to Kai Labusch at kai.labusch@sbb.spk-berlin.de or Clemens Neudecker at clemens.neudecker@sbb.spk-berlin.de.
# Table of Contents
- [Model Card for sbb_ned-de](#model-card-for-sbb_ned-de)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Speeds, Sizes, Times](#speeds-sizes-times)
- [Training Hyperparameters](#training-hyperparameters)
- [Training Results](#training-results)
- [Evaluation](#evaluation)
- [Testing Data, Factors and Metrics](#testing-data-factors-and-metrics)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Software](#software)
- [Citation](#citation)
- [More Information](#more-information)
- [Model Card Authors](#model-card-authors)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
This model forms the core of a named entity disambiguation and linking system (NED, NEL) that consists of three components:
(i) Lookup of possible candidates in an approximative nearest neighbour (ANN) index that stores BERT embeddings.
(ii) Evaluation of each candidate by comparison of text passages of Wikipedia performed by a purpose-trained BERT model.
(iii) Final ranking of candidates on the basis of information gathered from previous steps.
This model is used in order to generate the BERT embeddings in step (i) and to perform the comparison of the text passages in step (ii).
- **Developed by:** [Kai Labusch](https://huggingface.co/labusch)
- **Shared by:** [Staatsbibliothek zu Berlin / Berlin State Library](https://huggingface.co/SBB)
- **Model type:** Language models
- **Language(s) (NLP):** de
- **License:** apache-2.0
- **Parent Model:** The BERT base multilingual cased model as provided by [Google](https://huggingface.co/bert-base-multilingual-cased)
- **Resources for more information:**
- [GitHub Repo](https://github.com/qurator-spk/sbb_ned/tree/6a2a48a9054b3a187b117e490513de5c41638844)
- Associated Paper 1 [CLEF 2020 HIPE paper](http://ceur-ws.org/Vol-2696/paper_163.pdf)
- Associated Paper 2 [CLEF 2022 HIPE paper](http://ceur-ws.org/Vol-3180/paper-85.pdf)
# 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. -->
Disciplines such as the *digital humanities* create use cases for text and data mining or the semantic enrichment of full-texts with named entity recognition and linking, e.g., for the re-construction of historical social networks. NED/NEL opens up new posibilities for improved access to text, knowledge creation and clustering of texts. Linking against Wikidata-IDs makes it possible to join the linked texts with the world knowledge provided by Wikidata by means of arbitrary SPARQL queries.
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
The NED/NEL system was developed on the basis of the [digitised collections of the Staatsbibliothek zu Berlin -- Berlin State Library](https://digital.staatsbibliothek-berlin.de/). The emphasis of this system is therefore on recognition and disambiguation of entities in historical texts.
## Downstream Use
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
Due to the historical nature of the documents being digitised in libraries, standard methods and procedures from the NLP domain typically require additional adaptation in order to successfully deal with the historical spelling variation and the remaining noise resulting from OCR errors. For use on other textual material, e.g. with an emphasis on entities comprised in other Wikipedias than the German, English and French ones, significant adaptations have to be performed. In such a case, the methodology used to develop the process as described in the related papers can serve as a showcase.
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
Though technically possible, named entity disambiguation and linking does not necessarily work well on contemporary data. This is because the disambiguation process relies on a subset of entities available on wikidata. In other words: In order to be reliably identified, those persons, places, or organizations have to be present in the extracted Wikidata.
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The identification and disambiguation of named entities in historical and contemporary texts is a task contributing to knowledge creation aiming at enhancing scientific research and better discoverability of information in digitised historical texts. The aim of the development of these models was to improve this knowledge creation process, an endeavour that was not undertaken for profit. The results of the applied models are freely accessible for the users of the digitised collections of the Berlin State Library. Against this backdrop, ethical challenges cannot be identified; rather, improved access and semantic enrichment of the derived full-texts with NER and NEL serves every human being with access to the digital collections of the Berlin State Library. As a limitation, it has to be noted that in historical texts the vast majority of identified and disambiguated persons are white, heterosexual and male, whereas other groups (e.g., those defeated in a war, colonial subjects, or else) are often not mentioned in such texts or are not addressed as identifiable entities with full names.
The knowledge base has been directly derived from Wikidata and Wikipedia in a two-step process. In the first step, relevant entities have been selected by use of appropriate SPARQL queries on the basis of Wikidata. In the second step, for all selected entities relevant text comparison material has been extracted from Wikipedia.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Disambiguation of named entities proves to be challenging beyond the task of automatically identifying entities. The existence of broad variations in the spelling of person and place names because of non-normalized orthography and linguistic change as well as changes in the naming of places according to the context adds to this challenge. Historical texts, especially newspapers, contain narrative descriptions and visual representations of minorities and disadvantaged groups without naming them; de-anonymizing such persons and groups is a research task in itself which has only been started to be tackled in the 2020's. The biggest potential for improvement of the NER / NEL / NED system is to be expected with improved OCR performance and NEL recall performance.
# 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. -->
Training data have been made available on Zenodo in the form of a sqlite databases for German text snippets. A data card for this data set is available on Zenodo. The German database is available at [10.5281/zenodo.7767404](https://doi.org/10.5281/zenodo.7767404).
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Before entity disambiguation starts, the input text is run through a named entity recognition (NER) system that tags all person (PER), location (LOC) and organization (ORG) entities, [see the related NER model on Hugging Face](https://huggingface.co/models?other=doi:10.57967/hf/0403). A BERT based NER system that has been developed previously at SBB has been used and described in [this paper](https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_4.pdf).
The entity linking and disambiguation works by comparison of continuous text snippets where the entities in question are mentioned. A purpose-trained BERT model (the evaluation model) performs that text comparison task. Therefore, a knowledge base that contains structured information like Wikidata is not sufficient. Rather, additional continuous text is needed where the entities that are part of the knowledge base are discussed, mentioned and referenced. Hence, the knowledge base is derived in such a way that each entity in it has a corresponding Wikipedia page, since the Wikipedia articles contain continuous texts that have been annotated by human authors with references that can serve as ground truth.
### Preprocessing
See section above.
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
Since the NED models are purpose-trained BERT derivatives, all the speed and performance properties of standard BERT models apply.
The models were trained on a two-class classification task. Given a pair of sentences, the models decide if the two sentences reference to the same entity or not.
The construction of the training samples is implemented in the [data processor](https://github.com/qurator-spk/sbb_ned/blob/6a2a48a9054b3a187b117e490513de5c41638844/qurator/sbb_ned/ground_truth/data_processor.py) that can be found in the GitHub repo.
### Training Hyperparameters
The training can be performed by the [ned-bert](https://github.com/qurator-spk/sbb_ned/blob/6a2a48a9054b3a187b117e490513de5c41638844/qurator/sbb_ned/models/bert.py) command line tool. After installation of the sbb_ned package, type "ned-bert --help" in order to get more information about its functionality.
The training hyperparamaters used can be found in the [Makefile](https://github.com/qurator-spk/sbb_ned/blob/6a2a48a9054b3a187b117e490513de5c41638844/Makefile). Here, the **de-ned-train-2**, **en-ned-train-1**, and **fr-ned-train-0** targets have been used in order to train the published models.
### Training Results
During training, the [data processor](https://github.com/qurator-spk/sbb_ned/blob/6a2a48a9054b3a187b117e490513de5c41638844/qurator/sbb_ned/ground_truth/data_processor.py) that feeds the training process continuously generates new sentence pairs without repetition over the entire training period. The models have been trained for roughly two weeks on a V100 GPU. During the entire training period the cross entropy training loss was evaluted and continued to decrease.
# Evaluation
<!-- This section describes the evaluation protocols and provides the results, or cites relevant papers. -->
A first version of the system was evaluated at [CLEF 2020 HIPE](http://ceur-ws.org/Vol-2696/paper_163.pdf). Several lessons learned from that first evaluation were applied to the system and a second evaluation was performed at [CLEF 2022 HIPE](http://ceur-ws.org/Vol-3180/paper-85.pdf). The models published here are the ones that have been evaluated in the CLEF 2022 HIPE competition.
## Testing Data, Factors and Metrics
Please consider the papers mentioned above. For a more complete overview about the used evaluation methodology read the [CLEF HIPE 2020 Overview Paper](https://ceur-ws.org/Vol-2696/paper_255.pdf) and the [CLEF HIPE 2022 Overview Paper](https://ceur-ws.org/Vol-3180/paper-83.pdf).
# 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:** V100.
- **Hours used:** Roughly 1-2 week(s).
- **Cloud Provider:** No cloud.
- **Compute Region:** Germany.
- **Carbon Emitted:** More information needed.
# Technical Specifications
### Software
See the information and source code published on [GitHub](https://github.com/qurator-spk/sbb_ned/tree/6a2a48a9054b3a187b117e490513de5c41638844).
# 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:**
```bibtex
@inproceedings{labusch_named_2020,
title = {Named {Entity} {Disambiguation} and {Linking} on {Historic} {Newspaper} {OCR} with {BERT}},
url = {https://ceur-ws.org/Vol-2696/paper_163.pdf},
abstract = {In this paper, we propose a named entity disambiguation and linking (NED, NEL) system that consists of three components: (i) Lookup of possible candidates in an approximative nearest neighbour (ANN) index that stores BERT-embeddings. (ii) Evaluation of each candidate by comparison of text passages of Wikipedia performed by a purpose-trained BERT model. (iii) Final ranking of candidates on the basis of information gathered from previous steps. We participated in the CLEF 2020 HIPE NERC-COARSE and NEL-LIT tasks for German, French, and English. The CLEF HIPE 2020 results show that our NEL approach is competitive in terms of precision but has low recall performance due to insufficient knowledge base coverage of the test data.},
language = {en},
booktitle = {{CLEF}},
author = {Labusch, Kai and Neudecker, Clemens},
year = {2020},
pages = {14},
}
```
**APA:**
(Labusch et al., 2020)
**BibTex**
```bibtex
@inproceedings{labusch_entity_2022,
title = {Entity {Linking} in {Multilingual} {Newspapers} and {Classical} {Commentaries} with {BERT}},
url = {http://ceur-ws.org/Vol-3180/paper-85.pdf},
abstract = {Building on our BERT-based entity recognition and three stage entity linking (EL) system [1] that we evaluated in the CLEF HIPE 2020 challenge [2], we focused in the CLEF HIPE 2022 challenge [3] on the entity linking part by participation in the EL-only tasks. We submitted results for the multilingual newspaper challenge (MNC), the multilingual classical commentary challenge (MCC), and the global adaptation challenge (GAC). This working note presents the most important modifications of the entity linking system in comparison to the HIPE 2020 approach and the additional results that have been obtained on the ajmc, hipe2020, newseye, topres19th, and sonar datasets for German, French, and English. The results show that our entity linking approach can be applied to a broad range of text categories and qualities without heavy adaptation and reveals qualitative differences of the impact of hyperparameters on our system that need further investigation.},
language = {en},
booktitle = {{CLEF}},
author = {Labusch, Kai and Neudecker, Clemens},
year = {2022},
pages = {11},
}
```
**APA:**
(Labusch et al., 2022)
# More Information
A demo of the named entity recognition and disambiguation tool can be found [here](https://ravius.sbb.berlin/sbb-tools/index.html?ppn=766355942&model_id=precomputed&el_model_id=precomputed&task=ner). Please note that the ppn (Pica Production Number) found in the link can be replaced by the ppn of any other work in the [digitised collections of the Staatsbibliothek zu Berlin / Berlin State Library](https://digital.staatsbibliothek-berlin.de/), provided that there is a fulltext of this work available.
**MD5 hash of the German pytorch_model.bin:**
92dbcdb6df705b6eed55faaf1a887b9d
**SHA256 hash of the German pytorch_model.bin:**
56be2b265348fa4c0f3e00567a0cb2186234861490bed4cc5c9a58bc12afa5fe
# Model Card Authors
<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
[Kai Labusch](kai.labusch@sbb.spk-berlin.de) and [Jörg Lehmann](joerg.lehmann@sbb.spk-berlin.de)
# Model Card Contact
Questions and comments about the model can be directed to Kai Labusch at kai.labusch@sbb.spk-berlin.de, questions and comments about the model card can be directed to Jörg Lehmann at joerg.lehmann@sbb.spk-berlin.de
# How to Get Started with the Model
How to get started with this model is explained in the ReadMe file of the GitHub repository [over here](https://github.com/qurator-spk/sbb_ned/tree/6a2a48a9054b3a187b117e490513de5c41638844#readme).
Model Card as of September 12th, 2023
|
abhinavkulkarni/VMware-open-llama-13b-open-instruct-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T13:09:25Z | 84 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"AWQ",
"en",
"arxiv:2302.13971",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-05T11:22:34Z |
---
license: cc
language:
- en
tags:
- AWQ
inference: false
---
# VMware/open-llama-13B-open-instruct (4-bit 128g AWQ Quantized)
[Instruction-tuned version](https://huggingface.co/VMware/open-llama-13b-open-instruct) of the fully trained [Open LLama 13B](https://huggingface.co/openlm-research/open_llama_13b) model.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 5, 2023
## Model License
Please refer to original Open Llama model license ([link](https://huggingface.co/VMware/open-llama-13b-open-instruct)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/VMware-open-llama-13b-open-instruct"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f'''What is the difference between nuclear fusion and fission?
###Response:'''
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[Open-LLaMA-13B-Instruct](https://huggingface.co/VMware/open-llama-13b-open-instruct)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|11.6564| | |
| | |byte_perplexity| 1.5829| | |
| | |bits_per_byte | 0.6626| | |
[Open-LLaMA-13B-Instruct (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/VMware-open-llama-13b-open-instruct-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|11.9652| | |
| | |byte_perplexity| 1.5907| | |
| | |bits_per_byte | 0.6696| | |
## Acknowledgements
If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX:
```
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
```
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
```
@article{touvron2023llama,
title={Llama: Open and efficient foundation language models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
abhinavkulkarni/tiiuae-falcon-7b-instruct-w4-g64-awq
|
abhinavkulkarni
| 2023-09-12T13:09:21Z | 25 | 5 |
transformers
|
[
"transformers",
"pytorch",
"RefinedWebModel",
"text-generation",
"AWQ",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-05T14:00:16Z |
---
license: apache-2.0
tags:
- AWQ
inference: false
---
# Falcon-7B-Instruct (4-bit 64g AWQ Quantized)
[Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets.
This model is a 4-bit 64 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 5, 2023
## Model License
Please refer to original Falcon model license ([link](https://huggingface.co/tiiuae/falcon-7b-instruct)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/tiiuae-falcon-7b-instruct-w4-g64-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 64,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f'''What is the difference between nuclear fusion and fission?
###Response:'''
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|14.5069| | |
| | |byte_perplexity| 1.6490| | |
| | |bits_per_byte | 0.7216| | |
[Falcon-7B-Instruct (4-bit 64-group AWQ)](https://huggingface.co/abhinavkulkarni/tiiuae-falcon-7b-instruct-w4-g64-awq)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|14.8667| | |
| | |byte_perplexity| 1.6566| | |
| | |bits_per_byte | 0.7282| | |
## Acknowledgements
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T13:09:17Z | 86 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"AWQ",
"en",
"arxiv:2307.09288",
"arxiv:2306.02707",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-29T13:38:49Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- AWQ
inference: false
---
# Stable Beluga 7B
`Stable Beluga 7B` is a Llama2 7B model finetuned on an Orca style Dataset.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 29, 2023
## Model License
Please refer to original StableBeluga model license ([link](https://huggingface.co/stabilityai/StableBeluga-7B)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f'''What is the difference between nuclear fusion and fission?
###Response:'''
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[StableBeluga-7B](https://huggingface.co/stabilityai/StableBeluga-7B)
| Task |Version| Metric |Value | |Stderr|
|--------|------:|---------------|-----:|---|------|
|wikitext| 1|word_perplexity|9.5097| | |
| | |byte_perplexity|1.5238| | |
| | |bits_per_byte |0.6077| | |
[StableBeluga-7B (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq)
| Task |Version| Metric |Value | |Stderr|
|--------|------:|---------------|-----:|---|------|
|wikitext| 1|word_perplexity|9.7783| | |
| | |byte_perplexity|1.5317| | |
| | |bits_per_byte |0.6152| | |
## Acknowledgements
Please cite this model using the following format:
```bibtext
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint={2307.09288},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtext
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
abhinavkulkarni/stabilityai-StableBeluga-13B-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T13:09:15Z | 150 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"AWQ",
"en",
"arxiv:2307.09288",
"arxiv:2306.02707",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-29T17:03:31Z |
---
language:
- en
pipeline_tag: text-generation
tags:
- AWQ
inference: false
---
# Stable Beluga 13B
`Stable Beluga 13B` is a Llama2 13B model finetuned on an Orca style Dataset.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 29, 2023
## Model License
Please refer to original L model license ([link](https://huggingface.co/stabilityai/StableBeluga-13B)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/stabilityai-StableBeluga-13B-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f'''What is the difference between nuclear fusion and fission?
###Response:'''
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[StableBeluga-13B](https://huggingface.co/stabilityai/StableBeluga-13B)
| Task |Version| Metric |Value | |Stderr|
|--------|------:|---------------|-----:|---|------|
|wikitext| 1|word_perplexity|8.4681| | |
| | |byte_perplexity|1.4911| | |
| | |bits_per_byte |0.5764| | |
[StableBeluga-13B (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/stabilityai-StableBeluga-13B-w4-g128-awq)
| Task |Version| Metric |Value | |Stderr|
|--------|------:|---------------|-----:|---|------|
|wikitext| 1|word_perplexity|8.6765| | |
| | |byte_perplexity|1.4979| | |
| | |bits_per_byte |0.5829| | |
## Acknowledgements
Please cite this model using the following format:
```bibtext
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint={2307.09288},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtext
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
abhinavkulkarni/Salesforce-codegen25-7b-instruct-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T13:09:09Z | 81 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"AWQ",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-13T16:35:04Z |
---
license: other
tags:
- AWQ
inference: false
---
# CodeGen2.5-7B-instruct (4-bit 128g AWQ Quantized)
Title: [**CodeGen2.5: Small, but mighty**](https://blog.salesforceairesearch.com/codegen25)
Authors: [Erik Nijkamp](https://eriknijkamp.com)\*, [Hiroaki Hayashi](https://hiroakih.me)\*, Yingbo Zhou, Caiming Xiong
(\* equal contribution)
## Model description
[CodeGen2.5](https://github.com/salesforce/CodeGen) is a family of autoregressive language models for **program synthesis**.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 5, 2023
## Model License
Please refer to original CodeGen2.5 model license ([link](https://huggingface.co/Salesforce/codegen25-7b-instruct)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/Salesforce-codegen25-7b-instruct-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f'''def hello_world():\n'''
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[CodeGen2.5-7B-instruct](https://huggingface.co/Salesforce/codegen25-7b-instruct)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|82.9477| | |
| | |byte_perplexity| 2.2847| | |
| | |bits_per_byte | 1.1920| | |
[CodeGen2.5-7B-instruct (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/Salesforce-codegen25-7b-instruct-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|84.5537| | |
| | |byte_perplexity| 2.2929| | |
| | |bits_per_byte | 1.1972| | |
## Acknowledgements
Please cite CodeGen2 paper:
```bibtex
@article{Nijkamp2023codegen2,
title={CodeGen2: Lessons for Training LLMs on Programming and Natural Languages},
author={Nijkamp, Erik and Hayashi, Hiroaki and Xiong, Caiming and Savarese, Silvio and Zhou, Yingbo},
journal={arXiv preprint},
year={2023}
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
abhinavkulkarni/psmathur-orca_mini_v2_7b-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T13:09:07Z | 83 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"Orca",
"AWQ",
"en",
"arxiv:2302.13971",
"arxiv:2304.12244",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-08T16:18:23Z |
---
license: cc-by-nc-sa-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- Orca
- AWQ
inference: false
---
# orca_mini_v2_7b (4-bit 128g AWQ Quantized)
An **Uncensored** LLaMA-7b model in collaboration with [Eric Hartford](https://huggingface.co/ehartford), trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 8, 2023
## Model License
Please refer to original Orca Mini v2 model license ([link](https://huggingface.co/psmathur/orca_mini_v2_7b)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/psmathur-orca_mini_v2_7b-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f'''What is the difference between nuclear fusion and fission?
###Response:'''
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[orca_mini_v2_7b](https://huggingface.co/psmathur/orca_mini_v2_7b)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|13.7024| | |
| | |byte_perplexity| 1.6315| | |
| | |bits_per_byte | 0.7062| | |
[orca_mini_v2_7b (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/psmathur-orca_mini_v2_7b-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|14.1097| | |
| | |byte_perplexity| 1.6405| | |
| | |bits_per_byte | 0.7141| | |
## Acknowledgements
If you found `orca_mini_v2_7b` useful in your research or applications, please kindly cite using the following BibTeX:
```
@misc{orca_mini_v2_7b,
author = {Pankaj Mathur},
title = {orca_mini_v2_7b: An explain tuned LLaMA-7b model on uncensored wizardlm, alpaca, & dolly datasets},
year = {2023},
publisher = {GitHub, HuggingFace},
journal = {GitHub repository, HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v2_7b},
}
```
```
@software{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
```
@misc{openalpaca,
author = {Yixuan Su and Tian Lan and Deng Cai},
title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}},
}
```
```
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
```
@online{DatabricksBlog2023DollyV2,
author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin},
title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM},
year = {2023},
url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm},
urldate = {2023-06-30}
}
```
```
@misc{xu2023wizardlm,
title={WizardLM: Empowering Large Language Models to Follow Complex Instructions},
author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
year={2023},
eprint={2304.12244},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T13:09:01Z | 58 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mpt",
"text-generation",
"MosaicML",
"AWQ",
"custom_code",
"license:cc-by-sa-3.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-05T03:43:24Z |
---
license: cc-by-sa-3.0
tags:
- MosaicML
- AWQ
inference: false
---
# MPT-7B-Instruct (4-bit 128g AWQ Quantized)
[MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct) is a model for short-form instruction following.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 5, 2023
## Model License
Please refer to original MPT model license ([link](https://huggingface.co/mosaicml/mpt-7b-instruct)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f'''What is the difference between nuclear fusion and fission?
###Response:'''
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|10.8864| | |
| | |byte_perplexity| 1.5628| | |
| | |bits_per_byte | 0.6441| | |
[MPT-7B-Instruct (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|11.2696| | |
| | |byte_perplexity| 1.5729| | |
| | |bits_per_byte | 0.6535| | |
## Acknowledgements
The MPT model was originally finetuned by Sam Havens and the MosaicML NLP team. Please cite this model using the following format:
```
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
abhinavkulkarni/meta-llama-Llama-2-13b-chat-hf-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T13:08:49Z | 19 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"AWQ",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-19T05:17:29Z |
---
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- AWQ
---
# **Llama 2** (4-bit 128g AWQ Quantized)
Llama 2 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 13B pretrained model, converted for the Hugging Face Transformers format.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
July 19, 2023
## Model License
Please refer to the original LLaMA 2 model license ([link](https://huggingface.co/meta-llama/Llama-2-13b-hf)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
## CUDA Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/meta-llama-Llama-2-13b-chat-hf-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f'''What is the difference between nuclear fusion and fission?
###Response:'''
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
[Llama-2-13b-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|10.7231| | |
| | |byte_perplexity| 1.5584| | |
| | |bits_per_byte | 0.6401| | |
[Llama-2-13b-chat (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/meta-llama-Llama-2-13b-chat-hf-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|--------|------:|---------------|------:|---|------|
|wikitext| 1|word_perplexity|10.9812| | |
| | |byte_perplexity| 1.5653| | |
| | |bits_per_byte | 0.6465| | |
## Acknowledgements
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T13:08:44Z | 3,805 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"AWQ",
"code",
"arxiv:2308.12950",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-31T16:37:05Z |
---
license: llama2
language:
- code
tags:
- llama2
- AWQ
inference: false
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
August 31, 2023
## Model License
Please refer to the original Code Llama model license ([link](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f"import socket\n\ndef ping_exponential_backoff(host: str):"
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using OpenAI [HumanEval](https://github.com/openai/human-eval)
[CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)
| Task |Version| Metric | Value | |Stderr|
|---------|------:|---------------|--------:|---|------|
|HumanEval| 1|pass@1 |36.5853% | | |
[CodeLlama-7b-Python-hf (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|---------|------:|---------------|--------:|---|------|
|HumanEval| 1|pass@1 |34.1463% | | |
## Acknowledgements
You can cite codellama paper as follows:
```
@misc{rozière2023code,
title={Code Llama: Open Foundation Models for Code},
author={Baptiste Rozière and Jonas Gehring and Fabian Gloeckle and Sten Sootla and Itai Gat and Xiaoqing Ellen Tan and Yossi Adi and Jingyu Liu and Tal Remez and Jérémy Rapin and Artyom Kozhevnikov and Ivan Evtimov and Joanna Bitton and Manish Bhatt and Cristian Canton Ferrer and Aaron Grattafiori and Wenhan Xiong and Alexandre Défossez and Jade Copet and Faisal Azhar and Hugo Touvron and Louis Martin and Nicolas Usunier and Thomas Scialom and Gabriel Synnaeve},
year={2023},
eprint={2308.12950},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
abhinavkulkarni/codellama-CodeLlama-13b-Python-hf-w4-g128-awq
|
abhinavkulkarni
| 2023-09-12T13:08:38Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"llama2",
"AWQ",
"code",
"arxiv:2308.12950",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-09-01T10:29:35Z |
---
license: llama2
language:
- code
tags:
- llama2
- AWQ
inference: false
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters.
This model is a 4-bit 128 group size AWQ quantized model. For more information about AWQ quantization, please click [here](https://github.com/mit-han-lab/llm-awq).
## Model Date
September 1, 2023
## Model License
Please refer to the original Code Llama model license ([link](https://huggingface.co/codellama/CodeLlama-13b-Python-hf)).
Please refer to the AWQ quantization license ([link](https://github.com/llm-awq/blob/main/LICENSE)).
Version
This model was successfully tested on CUDA driver v530.30.02 and runtime v11.7 with Python v3.10.11. Please note that AWQ requires NVIDIA GPUs with compute capability of `8.0` or higher.
For Docker users, the `nvcr.io/nvidia/pytorch:23.06-py3` image is runtime v12.1 but otherwise the same as the configuration above and has also been verified to work.
## How to Use
```bash
git clone https://github.com/mit-han-lab/llm-awq \
&& cd llm-awq \
&& git checkout f084f40bd996f3cf3a0633c1ad7d9d476c318aaa \
&& pip install -e . \
&& cd awq/kernels \
&& python setup.py install
```
```python
import time
import torch
from awq.quantize.quantizer import real_quantize_model_weight
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer, TextStreamer
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
from huggingface_hub import snapshot_download
model_name = "abhinavkulkarni/codellama-CodeLlama-13b-Python-hf-w4-g128-awq"
# Config
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name, trust_remote_code=True)
except:
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Model
w_bit = 4
q_config = {
"zero_point": True,
"q_group_size": 128,
}
load_quant = snapshot_download(model_name)
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config,
torch_dtype=torch.float16, trust_remote_code=True)
real_quantize_model_weight(model, w_bit=w_bit, q_config=q_config, init_only=True)
model.tie_weights()
model = load_checkpoint_and_dispatch(model, load_quant, device_map="balanced")
# Inference
prompt = f"import socket\n\ndef ping_exponential_backoff(host: str):"
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()
output = model.generate(
inputs=input_ids,
temperature=0.7,
max_new_tokens=512,
top_p=0.15,
top_k=0,
repetition_penalty=1.1,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer)
```
## Evaluation
This evaluation was done using OpenAI [HumanEval](https://github.com/openai/human-eval)
[CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf)
| Task |Version| Metric | Value | |Stderr|
|---------|------:|---------------|--------:|---|------|
|HumanEval| 1|pass@1 |35.9756% | | |
[CodeLlama-13b-Python-hf (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/codellama-CodeLlama-13b-Python-hf-w4-g128-awq)
| Task |Version| Metric | Value | |Stderr|
|---------|------:|---------------|--------:|---|------|
|HumanEval| 1|pass@1 |39.6341% | | |
## Acknowledgements
You can cite codellama paper as follows:
```
@misc{rozière2023code,
title={Code Llama: Open Foundation Models for Code},
author={Baptiste Rozière and Jonas Gehring and Fabian Gloeckle and Sten Sootla and Itai Gat and Xiaoqing Ellen Tan and Yossi Adi and Jingyu Liu and Tal Remez and Jérémy Rapin and Artyom Kozhevnikov and Ivan Evtimov and Joanna Bitton and Manish Bhatt and Cristian Canton Ferrer and Aaron Grattafiori and Wenhan Xiong and Alexandre Défossez and Jade Copet and Faisal Azhar and Hugo Touvron and Louis Martin and Nicolas Usunier and Thomas Scialom and Gabriel Synnaeve},
year={2023},
eprint={2308.12950},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
The model was quantized with AWQ technique. If you find AWQ useful or relevant to your research, please kindly cite the paper:
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
|
Aleksandar/electra-srb-ner-setimes
|
Aleksandar
| 2023-09-12T13:05:34Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"electra",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: electra-srb-ner-setimes
results:
- task:
name: Token Classification
type: token-classification
metric:
name: Accuracy
type: accuracy
value: 0.9546789604788638
---
<!-- 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. -->
# electra-srb-ner-setimes
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2804
- Precision: 0.8286
- Recall: 0.8081
- F1: 0.8182
- Accuracy: 0.9547
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 104 | 0.2981 | 0.6737 | 0.6113 | 0.6410 | 0.9174 |
| No log | 2.0 | 208 | 0.2355 | 0.7279 | 0.6701 | 0.6978 | 0.9307 |
| No log | 3.0 | 312 | 0.2079 | 0.7707 | 0.7062 | 0.7371 | 0.9402 |
| No log | 4.0 | 416 | 0.2078 | 0.7689 | 0.7479 | 0.7582 | 0.9449 |
| 0.2391 | 5.0 | 520 | 0.2089 | 0.8083 | 0.7476 | 0.7767 | 0.9484 |
| 0.2391 | 6.0 | 624 | 0.2199 | 0.7981 | 0.7726 | 0.7851 | 0.9487 |
| 0.2391 | 7.0 | 728 | 0.2528 | 0.8205 | 0.7749 | 0.7971 | 0.9511 |
| 0.2391 | 8.0 | 832 | 0.2265 | 0.8074 | 0.8003 | 0.8038 | 0.9524 |
| 0.2391 | 9.0 | 936 | 0.2843 | 0.8265 | 0.7716 | 0.7981 | 0.9504 |
| 0.0378 | 10.0 | 1040 | 0.2450 | 0.8024 | 0.8019 | 0.8021 | 0.9520 |
| 0.0378 | 11.0 | 1144 | 0.2550 | 0.8116 | 0.7986 | 0.8051 | 0.9519 |
| 0.0378 | 12.0 | 1248 | 0.2706 | 0.8208 | 0.7957 | 0.8081 | 0.9532 |
| 0.0378 | 13.0 | 1352 | 0.2664 | 0.8040 | 0.8035 | 0.8038 | 0.9530 |
| 0.0378 | 14.0 | 1456 | 0.2571 | 0.8011 | 0.8110 | 0.8060 | 0.9529 |
| 0.0099 | 15.0 | 1560 | 0.2673 | 0.8051 | 0.8129 | 0.8090 | 0.9534 |
| 0.0099 | 16.0 | 1664 | 0.2733 | 0.8074 | 0.8087 | 0.8081 | 0.9529 |
| 0.0099 | 17.0 | 1768 | 0.2835 | 0.8254 | 0.8074 | 0.8163 | 0.9543 |
| 0.0099 | 18.0 | 1872 | 0.2771 | 0.8222 | 0.8081 | 0.8151 | 0.9545 |
| 0.0099 | 19.0 | 1976 | 0.2776 | 0.8237 | 0.8084 | 0.8160 | 0.9546 |
| 0.0044 | 20.0 | 2080 | 0.2804 | 0.8286 | 0.8081 | 0.8182 | 0.9547 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
|
Aleksandar/bert-srb-ner
|
Aleksandar
| 2023-09-12T13:05:15Z | 132 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: bert-srb-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: sr
metric:
name: Accuracy
type: accuracy
value: 0.9546696220907545
---
<!-- 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-srb-ner
This model was trained from scratch on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3561
- Precision: 0.8909
- Recall: 0.9082
- F1: 0.8995
- Accuracy: 0.9547
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3907 | 1.0 | 625 | 0.2316 | 0.8255 | 0.8314 | 0.8285 | 0.9259 |
| 0.2091 | 2.0 | 1250 | 0.1920 | 0.8598 | 0.8731 | 0.8664 | 0.9420 |
| 0.1562 | 3.0 | 1875 | 0.1833 | 0.8608 | 0.8820 | 0.8713 | 0.9441 |
| 0.0919 | 4.0 | 2500 | 0.1985 | 0.8712 | 0.8886 | 0.8798 | 0.9476 |
| 0.0625 | 5.0 | 3125 | 0.2195 | 0.8762 | 0.8923 | 0.8842 | 0.9485 |
| 0.0545 | 6.0 | 3750 | 0.2320 | 0.8706 | 0.9004 | 0.8852 | 0.9495 |
| 0.0403 | 7.0 | 4375 | 0.2459 | 0.8817 | 0.8957 | 0.8887 | 0.9505 |
| 0.0269 | 8.0 | 5000 | 0.2603 | 0.8813 | 0.9021 | 0.8916 | 0.9516 |
| 0.0193 | 9.0 | 5625 | 0.2916 | 0.8812 | 0.8949 | 0.8880 | 0.9500 |
| 0.0162 | 10.0 | 6250 | 0.2938 | 0.8814 | 0.9025 | 0.8918 | 0.9520 |
| 0.0134 | 11.0 | 6875 | 0.3330 | 0.8809 | 0.8961 | 0.8885 | 0.9497 |
| 0.0076 | 12.0 | 7500 | 0.3141 | 0.8840 | 0.9025 | 0.8932 | 0.9524 |
| 0.0069 | 13.0 | 8125 | 0.3292 | 0.8819 | 0.9065 | 0.8940 | 0.9535 |
| 0.0053 | 14.0 | 8750 | 0.3454 | 0.8844 | 0.9018 | 0.8930 | 0.9523 |
| 0.0038 | 15.0 | 9375 | 0.3519 | 0.8912 | 0.9061 | 0.8986 | 0.9539 |
| 0.0034 | 16.0 | 10000 | 0.3437 | 0.8894 | 0.9038 | 0.8965 | 0.9539 |
| 0.0024 | 17.0 | 10625 | 0.3518 | 0.8896 | 0.9072 | 0.8983 | 0.9543 |
| 0.0018 | 18.0 | 11250 | 0.3572 | 0.8877 | 0.9072 | 0.8973 | 0.9543 |
| 0.0015 | 19.0 | 11875 | 0.3554 | 0.8910 | 0.9081 | 0.8994 | 0.9549 |
| 0.0011 | 20.0 | 12500 | 0.3561 | 0.8909 | 0.9082 | 0.8995 | 0.9547 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0
- Datasets 1.11.0
- Tokenizers 0.10.1
|
trieudemo11/llama_miravia_2
|
trieudemo11
| 2023-09-12T13:01:27Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T13:01:10Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
actionpace/OpenRP
|
actionpace
| 2023-09-12T12:58:22Z | 0 | 0 | null |
[
"gguf",
"en",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2023-09-12T12:25:55Z |
---
license: other
language:
- en
---
**Some of my own quants:**
* OpenRP_Q4_K_M.gguf
* OpenRP_Q5_K_M.gguf
**Source:** [Undi95](https://huggingface.co/Undi95)
**Source Model:** [OpenRP](https://huggingface.co/Undi95/OpenRP)
**Source models for Undi95/OpenRP (Merge)**
- [Open-Orca/OpenOrcaxOpenChat-Preview2-13B](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B)
- [PygmalionAI/pygmalion-2-13b](https://huggingface.co/PygmalionAI/pygmalion-2-13b) ([Ref](https://huggingface.co/actionpace/pygmalion-2-13b))
- [Undi95/MLewd-L2-13B-v2-3](https://huggingface.co/Undi95/MLewd-L2-13B-v2-3) ([Ref](https://huggingface.co/actionpace/MLewd-L2-13B-v2-3))
- [jondurbin/spicyboros-13b-2.2](https://huggingface.co/jondurbin/spicyboros-13b-2.2) ([Ref](https://huggingface.co/actionpace/spicyboros-13b-2.2))
- [lemonilia/limarp-llama2-v2](https://huggingface.co/lemonilia/limarp-llama2-v2) (Lora)
|
bigmorning/whisper_4_with_init_sun_syl_wd_0_lr_5en4_0015
|
bigmorning
| 2023-09-12T12:52:06Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-12T12:51:57Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0_lr_5en4_0015
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0_lr_5en4_0015
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8512
- Train Accuracy: 0.0276
- Train Wermet: 0.9617
- Train Wermet Syl: 1.6026
- Validation Loss: 1.4447
- Validation Accuracy: 0.0195
- Validation Wermet: 0.6508
- Validation Wermet Syl: 0.9710
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.0005, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 4.9887 | 0.0111 | 2.4313 | 2.0277 | 3.9591 | 0.0113 | 0.9814 | 0.9748 | 0 |
| 4.6955 | 0.0116 | 1.0254 | 1.1408 | 3.9756 | 0.0114 | 0.9556 | 0.9274 | 1 |
| 4.6375 | 0.0117 | 0.9863 | 1.1057 | 3.9172 | 0.0113 | 1.0035 | 1.0561 | 2 |
| 4.5880 | 0.0118 | 0.9259 | 1.0151 | 3.8477 | 0.0115 | 0.9131 | 0.8768 | 3 |
| 4.4281 | 0.0121 | 0.9097 | 1.0158 | 3.4905 | 0.0121 | 0.8963 | 0.8527 | 4 |
| 3.7757 | 0.0137 | 0.9637 | 1.1849 | 2.6810 | 0.0138 | 0.8056 | 0.7421 | 5 |
| 2.9154 | 0.0165 | 0.7874 | 0.9588 | 2.0364 | 0.0160 | 0.6531 | 0.6367 | 6 |
| 2.2740 | 0.0191 | 0.9549 | 1.3576 | 1.6962 | 0.0173 | 0.6010 | 0.6229 | 7 |
| 1.8660 | 0.0210 | 0.8321 | 1.1975 | 1.4948 | 0.0183 | 0.5637 | 0.6709 | 8 |
| 1.5840 | 0.0226 | 1.2436 | 1.9323 | 1.4040 | 0.0188 | 0.5958 | 0.6870 | 9 |
| 1.3490 | 0.0241 | 0.8796 | 1.4101 | 1.3792 | 0.0189 | 0.6074 | 0.6794 | 10 |
| 1.1885 | 0.0252 | 0.6986 | 1.1213 | 1.3298 | 0.0194 | 0.4919 | 0.5833 | 11 |
| 1.0398 | 0.0262 | 0.7525 | 1.2414 | 1.3096 | 0.0197 | 0.5282 | 0.7002 | 12 |
| 0.9501 | 0.0268 | 0.8995 | 1.4857 | 1.3766 | 0.0198 | 0.6507 | 0.9766 | 13 |
| 0.8512 | 0.0276 | 0.9617 | 1.6026 | 1.4447 | 0.0195 | 0.6508 | 0.9710 | 14 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
richieyoum/whisper-large-v2-cv13-en-LORA
|
richieyoum
| 2023-09-12T12:51:54Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T12:51:35Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
|
kimjaehyung/imsquared-non-prehensile
|
kimjaehyung
| 2023-09-12T12:51:36Z | 0 | 0 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-09-12T12:38:56Z |
---
license: cc-by-4.0
---
# imsquared non-prehensile manipulation dataset
## KAIST IM^2 Lab
https://github.com/JaeHyung-Kim/rlds_dataset_builder
|
danbev/lora_example
|
danbev
| 2023-09-12T12:51:06Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T12:12:16Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
hunz/stable-diffusion-xl-lora
|
hunz
| 2023-09-12T12:49:06Z | 3 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-09-12T09:15:55Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of flying ironman
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - hunz/stable-diffusion-xl-lora
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of flying ironman using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
trieudemo11/llama_miravia_1
|
trieudemo11
| 2023-09-12T12:42:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-09-12T12:41:56Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
- PEFT 0.6.0.dev0
|
dcrey7/linkedin
|
dcrey7
| 2023-09-12T12:40:40Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-09-10T11:15:22Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of abhi
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
bigmorning/whisper_4_with_init_sun_syl_wd_0_lr_5en4_0010
|
bigmorning
| 2023-09-12T12:37:11Z | 59 | 0 |
transformers
|
[
"transformers",
"tf",
"whisper",
"automatic-speech-recognition",
"generated_from_keras_callback",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-09-12T12:37:01Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_keras_callback
model-index:
- name: whisper_4_with_init_sun_syl_wd_0_lr_5en4_0010
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# whisper_4_with_init_sun_syl_wd_0_lr_5en4_0010
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5840
- Train Accuracy: 0.0226
- Train Wermet: 1.2436
- Train Wermet Syl: 1.9323
- Validation Loss: 1.4040
- Validation Accuracy: 0.0188
- Validation Wermet: 0.5958
- Validation Wermet Syl: 0.6870
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 0.0005, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Wermet | Train Wermet Syl | Validation Loss | Validation Accuracy | Validation Wermet | Validation Wermet Syl | Epoch |
|:----------:|:--------------:|:------------:|:----------------:|:---------------:|:-------------------:|:-----------------:|:---------------------:|:-----:|
| 4.9887 | 0.0111 | 2.4313 | 2.0277 | 3.9591 | 0.0113 | 0.9814 | 0.9748 | 0 |
| 4.6955 | 0.0116 | 1.0254 | 1.1408 | 3.9756 | 0.0114 | 0.9556 | 0.9274 | 1 |
| 4.6375 | 0.0117 | 0.9863 | 1.1057 | 3.9172 | 0.0113 | 1.0035 | 1.0561 | 2 |
| 4.5880 | 0.0118 | 0.9259 | 1.0151 | 3.8477 | 0.0115 | 0.9131 | 0.8768 | 3 |
| 4.4281 | 0.0121 | 0.9097 | 1.0158 | 3.4905 | 0.0121 | 0.8963 | 0.8527 | 4 |
| 3.7757 | 0.0137 | 0.9637 | 1.1849 | 2.6810 | 0.0138 | 0.8056 | 0.7421 | 5 |
| 2.9154 | 0.0165 | 0.7874 | 0.9588 | 2.0364 | 0.0160 | 0.6531 | 0.6367 | 6 |
| 2.2740 | 0.0191 | 0.9549 | 1.3576 | 1.6962 | 0.0173 | 0.6010 | 0.6229 | 7 |
| 1.8660 | 0.0210 | 0.8321 | 1.1975 | 1.4948 | 0.0183 | 0.5637 | 0.6709 | 8 |
| 1.5840 | 0.0226 | 1.2436 | 1.9323 | 1.4040 | 0.0188 | 0.5958 | 0.6870 | 9 |
### Framework versions
- Transformers 4.34.0.dev0
- TensorFlow 2.13.0
- Tokenizers 0.13.3
|
sivanravid/test_model2
|
sivanravid
| 2023-09-12T12:35:01Z | 0 | 0 | null |
[
"text-classification",
"en",
"dataset:fka/awesome-chatgpt-prompts",
"region:us"
] |
text-classification
| 2023-09-12T07:58:18Z |
---
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
---
My model is very nice
## Datatest
name: <>
version: <>
url: <>
|
CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16
|
CHIH-HUNG
| 2023-09-12T12:30:26Z | 1,481 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:huangyt/FINETUNE1",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-06T09:57:47Z |
---
license: llama2
datasets:
- huangyt/FINETUNE1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
在llama-2-13b上使用huangyt/FINETUNE1資料集進行訓練，總資料筆數約17w
# Fine-Tuning Information
- **GPU:** RTX4090 (single core / 24564MiB)
- **model:** meta-llama/Llama-2-13b-hf
- **dataset:** huangyt/FINETUNE1 (共約17w筆訓練集)
- **peft_type:** LoRA
- **lora_rank:** 16
- **lora_target:** gate_proj, up_proj, down_proj
- **per_device_train_batch_size:** 8
- **gradient_accumulation_steps:** 8
- **learning_rate :** 5e-5
- **epoch:** 1
- **precision:** bf16
- **quantization:** load_in_4bit
# Fine-Tuning Detail
- **train_loss:** 0.66
- **train_runtime:** 16:26:58 (use deepspeed)
# Evaluation
- 評估結果來自**HuggingFaceH4/open_llm_leaderboard**
- 與Llama-2-13b比較4種Benchmark，包含**ARC**、**HellaSwag**、**MMLU**、**TruthfulQA**
| Model |Average| ARC |HellaSwag| MMLU |TruthfulQA|
|--------------------------------------------------------|-------|-------|---------|-------|----------|
|meta-llama/Llama-2-13b-hf | 56.9 | 58.11 | 80.97 | 54.34 | 34.17 |
|meta-llama/Llama-2-13b-chat-hf | 59.93 | 59.04 | 81.94 | 54.64 | 44.12 |
|CHIH-HUNG/llama-2-13b-Fintune_1_17w | 58.24 | 59.47 | 81 | 54.31 | 38.17 |
|CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-q_k_v_o_proj| 58.49 | 59.73 | 81.06 | 54.53 | 38.64 |
|CHIH-HUNG/llama-2-13b-Fintune_1_17w-gate_up_down_proj | 58.81 | 57.17 | 82.26 | 55.89 | 39.93 |
|CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 | 58.86 | 57.25 | 82.27 | 56.16 | 39.75 |
|CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4 | 58.71 | 56.74 | 82.27 | 56.18 | 39.65 |
# How to convert dataset to json
- 在**load_dataset**中輸入資料集名稱，並且在**take**中輸入要取前幾筆資料
- 觀察該資料集的欄位名稱，填入**example**欄位中(例如system_prompt、question、response)
- 最後指定json檔儲存位置 (**json_filename**)
```py
import json
from datasets import load_dataset
# 讀取數據集，take可以取得該數據集前n筆資料
dataset = load_dataset("huangyt/FINETUNE1", split="train", streaming=True)
# 提取所需欄位並建立新的字典列表
extracted_data = []
for example in dataset:
extracted_example = {
"instruction": example["instruction"],
"input": example["input"],
"output": example["output"]
}
extracted_data.append(extracted_example)
# 指定 JSON 文件名稱
json_filename = "huangyt_FINETUNE_1.json"
# 寫入 JSON 文件
with open(json_filename, "w") as json_file:
json.dump(extracted_data, json_file, indent=4)
print(f"數據已提取並保存為 {json_filename}")
```
|
Tottin/Megagon-step1_model
|
Tottin
| 2023-09-12T12:27:45Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:jicoc22578/autotrain-livedoor_news-722922024",
"base_model:finetune:jicoc22578/autotrain-livedoor_news-722922024",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-12T12:16:25Z |
---
base_model: jicoc22578/autotrain-livedoor_news-722922024
tags:
- generated_from_keras_callback
model-index:
- name: Megagon-step1_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Megagon-step1_model
This model is a fine-tuned version of [jicoc22578/autotrain-livedoor_news-722922024](https://huggingface.co/jicoc22578/autotrain-livedoor_news-722922024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0806
- Train Accuracy: 0.9711
- Validation Loss: 0.2171
- Validation Accuracy: 0.9351
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 2832, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3175 | 0.9077 | 0.2711 | 0.9234 | 0 |
| 0.1671 | 0.9429 | 0.2022 | 0.9444 | 1 |
| 0.0806 | 0.9711 | 0.2171 | 0.9351 | 2 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.