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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-04 12:28:55
| downloads
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| likes
int64 0
11.7k
| library_name
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AMAISIENG/finetuned_model5
|
AMAISIENG
| 2024-01-03T07:41:24Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"biogpt",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-03T07:29:21Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: finetuned_model5
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. -->
# finetuned_model5
This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3739
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 445 | 1.6290 |
| 2.0608 | 2.0 | 890 | 1.4244 |
| 1.5644 | 3.0 | 1335 | 1.3739 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
nkthakur/flan-t5-small-translator
|
nkthakur
| 2024-01-03T07:31:41Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"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
| 2024-01-02T10:45:08Z |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: flan-t5-small-translator
results: []
widget:
- text: 'translate English to French: All creative skill levels are welcome.'
example_title: Translation
datasets:
- opus_books
---
<!-- 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-translator
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on [opus_books/en-fr](https://huggingface.co/datasets/opus_books/viewer/en-fr) dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Bleu: 1.078
- Gen Len: 18.0374
## Sample Request
Try this sentence - `translate English to French: what is love?`
You should get response like - `Qu'est-ce que l'amour?`
> Ensure that you are prepending `translate English to French: ` for all translations
## Intended uses & limitations
> This model has been trained only on en-fr subset of OPUS dataset.
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-----:|:-------:|
| 0.0 | 1.0 | 6355 | nan | 1.078 | 18.0374 |
| 0.0 | 2.0 | 12710 | nan | 1.078 | 18.0374 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Kshitij2406/GPT_Test_Run
|
Kshitij2406
| 2024-01-03T07:23:27Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-01-03T07:19:42Z |
---
library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
ncsgobubble/rollercoaster_emotions_v3
|
ncsgobubble
| 2024-01-03T07:17:56Z | 2 | 1 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-03T07:17:45Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
ramathuzen/ppo-CartPole-v2
|
ramathuzen
| 2024-01-03T07:12:15Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-03T07:12:10Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -176.44 +/- 91.86
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'ramathuzen/ppo-CartPole-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
adityarra07/whisper-medium-gabriel_fold_6
|
adityarra07
| 2024-01-03T07:02:33Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-03T05:15:44Z |
---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-gabriel_fold_6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-gabriel_fold_6
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2775
- Wer: 10.3823
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7001 | 1.0 | 169 | 0.2615 | 11.8744 |
| 0.1175 | 2.0 | 338 | 0.2532 | 11.4081 |
| 0.0423 | 3.0 | 507 | 0.2481 | 10.8486 |
| 0.0147 | 4.0 | 676 | 0.2615 | 10.8486 |
| 0.0042 | 5.0 | 845 | 0.2720 | 10.6310 |
| 0.0014 | 6.0 | 1014 | 0.2775 | 10.3823 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ncsgobubble/rollercoaster_emotions_v2
|
ncsgobubble
| 2024-01-03T07:02:19Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2024-01-03T07:02:08Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
AlfredBink/bart-cnn-samsum-peft-trained-x
|
AlfredBink
| 2024-01-03T06:56:56Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"base_model:finetune:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-03T06:25:08Z |
---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart-cnn-samsum-peft-trained-x
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-samsum-peft-trained-x
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0489
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7177 | 1.0 | 200 | 2.2686 |
| 0.1079 | 2.0 | 400 | 0.0782 |
| 0.0679 | 3.0 | 600 | 0.0565 |
| 0.0639 | 4.0 | 800 | 0.0528 |
| 0.052 | 5.0 | 1000 | 0.0509 |
| 0.0542 | 6.0 | 1200 | 0.0498 |
| 0.0545 | 7.0 | 1400 | 0.0491 |
| 0.0542 | 8.0 | 1600 | 0.0489 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
dil-gregkowalski/ppo-Pyramids
|
dil-gregkowalski
| 2024-01-03T06:53:47Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-01-03T06:50:16Z |
---
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: dil-gregkowalski/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Kshitij2406/GPT_Test_Train
|
Kshitij2406
| 2024-01-03T06:51:15Z | 3 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-01-03T06:46:19Z |
---
library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
AMAISIENG/finetuned_model4
|
AMAISIENG
| 2024-01-03T06:43:07Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"biogpt",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-03T06:30:57Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: finetuned_model4
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. -->
# finetuned_model4
This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3739
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 445 | 1.6290 |
| 2.0608 | 2.0 | 890 | 1.4244 |
| 1.5644 | 3.0 | 1335 | 1.3739 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
|
kunheekim/style-aware-discriminator
|
kunheekim
| 2024-01-03T06:40:02Z | 0 | 4 | null |
[
"image-to-image",
"pytorch",
"en",
"dataset:huggan/AFHQ",
"dataset:huggan/AFHQv2",
"dataset:huggan/CelebA-HQ",
"arxiv:2203.15375",
"region:us"
] |
image-to-image
| 2022-09-04T13:03:31Z |
---
language:
- en
thumbnail: https://github.com/kunheek/style-aware-discriminator/raw/main/assets/teaser.png
tags:
- image-to-image
- pytorch
datasets:
- huggan/AFHQ
- huggan/AFHQv2
- huggan/CelebA-HQ
metrics:
- fid
---
# Style-Aware Discriminator
Pre-trained weights for [A Style-Aware Discriminator for Controllable Image Translation](https://arxiv.org/abs/2203.15375).
Please check the [official repository](https://github.com/kunheek/style-aware-discriminator) for more details.
# Citation
```sh
@InProceedings{kim2022style,
title={A Style-Aware Discriminator for Controllable Image Translation},
author={Kim, Kunhee and Park, Sanghun and Jeon, Eunyeong and Kim, Taehun and Kim, Daijin},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022},
pages={18239--18248}
}
```
|
BJ-1018/billsum_model
|
BJ-1018
| 2024-01-03T06:38:27Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-02T12:20:30Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: billsum_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 25 | 3.8034 | 0.1466 | 0.0502 | 0.1209 | 0.1214 | 19.0 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.13.3
|
aodianyun/chatglm3-6b-32k-openvino
|
aodianyun
| 2024-01-03T06:32:21Z | 1 | 0 |
transformers
|
[
"transformers",
"openvino",
"chatglm",
"glm",
"custom_code",
"zh",
"en",
"endpoints_compatible",
"region:us"
] | null | 2024-01-02T06:16:31Z |
---
language:
- zh
- en
tags:
- glm
- chatglm
- openvino
---
# ChatGLM3-6B-32K-openvino
## 介绍 (Introduction)
基于ChatGLM3-6B-32K模型进行openvino转换处理。
## 软件依赖 (Dependencies)
```shell
pip install optimum[openvino]
```
|
hxgdzyuyi/qgyh
|
hxgdzyuyi
| 2024-01-03T06:28:13Z | 4 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-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
| 2024-01-03T06:28:08Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: A photo of <s0><s1>
output:
url: image-0.png
- text: A photo of <s0><s1>
output:
url: image-1.png
- text: A photo of <s0><s1>
output:
url: image-2.png
- text: A photo of <s0><s1>
output:
url: image-3.png
- text: A photo of <s0><s1>
output:
url: image-4.png
- text: A photo of <s0><s1>
output:
url: image-5.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - hxgdzyuyi/qgyh
<Gallery />
## Model description
### These are hxgdzyuyi/qgyh LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`qgyh.safetensors` here 💾](/hxgdzyuyi/qgyh/blob/main/qgyh.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:qgyh:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`qgyh_emb.safetensors` here 💾](/hxgdzyuyi/qgyh/blob/main/qgyh_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `qgyh_emb` to your prompt. For example, `A photo of qgyh_emb`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('hxgdzyuyi/qgyh', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='hxgdzyuyi/qgyh', filename='qgyh_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A photo of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/hxgdzyuyi/qgyh/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
J001/distilhubert-finetuned-gtzan
|
J001
| 2024-01-03T06:22:52Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-01-03T05:00:50Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
model-index:
- name: distilhubert-finetuned-gtzan
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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.1599
- eval_accuracy: 0.8
- eval_runtime: 2.7351
- eval_samples_per_second: 36.562
- eval_steps_per_second: 4.753
- epoch: 5.0
- step: 565
## 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: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
oshizo/japanese-sexual-moderation
|
oshizo
| 2024-01-03T06:08:46Z | 7 | 2 |
transformers
|
[
"transformers",
"pytorch",
"luke",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-09-17T13:19:51Z |
---
license: mit
---
---
**v2モデルを以下のリンク先にリリースしました**
[oshizo/japanese-sexual-moderation-v2](https://huggingface.co/oshizo/japanese-sexual-moderation-v2)
---
japanese-sexual-moderationは、[studio-ousia/luke-japanese-large-lite](https://huggingface.co/studio-ousia/luke-japanese-large-lite)をファインチューニングしたモデルです。
短文が性的かどうかをスコアリングします。
20230/9/17時点のバージョンは限られたデータ数で訓練されており、スコアリングの傾向にはデータセットに起因するバイアスがある可能性があります。
このモデルは[japanese-llm-roleplay-benchmark](https://github.com/oshizo/japanese-llm-roleplay-benchmark)でのERPスコアを算出するために作成されました。
## Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
model_id = "oshizo/japanese-sexual-moderation"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(
model_id,
problem_type="multi_label_classification",
num_labels=1
)
text = "富士山は日本で一番高い山です。"
with torch.no_grad():
encoding = tokenizer(text, return_tensors="pt")
score = model(**encoding).logits
# tensor([[-2.7863]])
```
|
cylee/bloom_prompt_tuning_1704261411.374676
|
cylee
| 2024-01-03T06:08:20Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2024-01-03T06:08:19Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
dil-gregkowalski/ppo-SnowballTarget
|
dil-gregkowalski
| 2024-01-03T06:06:36Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-01-03T06:06:32Z |
---
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: dil-gregkowalski/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
smallfish166/distilbert-base-uncased-finetuned-imdb
|
smallfish166
| 2024-01-03T05:59:29Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-01-03T05:52:03Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4406
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6819 | 1.0 | 157 | 2.4978 |
| 2.5872 | 2.0 | 314 | 2.4488 |
| 2.525 | 3.0 | 471 | 2.4836 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
sawthiha/segformer-b0-finetuned-deprem-satellite
|
sawthiha
| 2024-01-03T05:59:08Z | 51 | 0 |
transformers
|
[
"transformers",
"tf",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"dataset:deprem-ml/deprem_satellite_semantic_whu_dataset",
"base_model:nvidia/segformer-b0-finetuned-ade-512-512",
"base_model:finetune:nvidia/segformer-b0-finetuned-ade-512-512",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2024-01-01T15:08:48Z |
---
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-deprem-satellite
results: []
widget:
- src: >-
https://datasets-server.huggingface.co/assets/deprem-ml/deprem_satellite_semantic_whu_dataset/--/default/train/3/image/image.jpg
example_title: Example 1
- src: >-
https://datasets-server.huggingface.co/assets/deprem-ml/deprem_satellite_semantic_whu_dataset/--/default/train/9/image/image.jpg
example_title: Example 2
datasets:
- deprem-ml/deprem_satellite_semantic_whu_dataset
---
<!-- 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. -->
# segformer-b0-finetuned-deprem-satellite
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the deprem-ml/deprem_satellite_semantic_whu_dataset dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0641
- eval_mean_iou: 0.9849
- eval_mean_accuracy: 0.9933
- eval_overall_accuracy: 0.9933
- eval_runtime: 94.2835
- eval_samples_per_second: 10.988
- eval_steps_per_second: 2.206
- epoch: 4.18
- step: 1980
## 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: 7e-05
- train_batch_size: 10
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
taku-yoshioka/test4
|
taku-yoshioka
| 2024-01-03T05:26:51Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2024-01-03T05:26:48Z |
---
license: apache-2.0
tags:
- trl
- ppo
- 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="taku-yoshioka//tmp/tmp6uvat143/taku-yoshioka/test4")
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("taku-yoshioka//tmp/tmp6uvat143/taku-yoshioka/test4")
model = AutoModelForCausalLMWithValueHead.from_pretrained("taku-yoshioka//tmp/tmp6uvat143/taku-yoshioka/test4")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
Mani429/Taxi-v3
|
Mani429
| 2024-01-03T05:21:36Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-03T05:21:19Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.67
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Mani429/Taxi-v3", 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"])
```
|
LoneStriker/tora-70b-v1.0-4.65bpw-h6-exl2
|
LoneStriker
| 2024-01-03T05:15:59Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"math",
"en",
"dataset:gsm8k",
"dataset:competition_math",
"arxiv:2309.17452",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-03T04:59:44Z |
---
license: llama2
datasets:
- gsm8k
- competition_math
language:
- en
metrics:
- exact_match
library_name: transformers
pipeline_tag: text-generation
tags:
- code
- math
---
<h1 align="center">
ToRA: A Tool-Integrated Reasoning Agent <br> for Mathematical Problem Solving
</h1>
<p align="center">
<a href="https://microsoft.github.io/ToRA/"><b>[🌐 Website]</b></a> •
<a href="https://arxiv.org/abs/2309.17452"><b>[📜 Paper]</b></a> •
<a href="https://huggingface.co/llm-agents"><b>[🤗 HF Models]</b></a> •
<a href="https://github.com/microsoft/ToRA"><b>[🐱 GitHub]</b></a>
<br>
<a href="https://twitter.com/zhs05232838/status/1708860992631763092"><b>[🐦 Twitter]</b></a> •
<a href="https://www.reddit.com/r/LocalLLaMA/comments/1703k6d/tora_a_toolintegrated_reasoning_agent_for/"><b>[💬 Reddit]</b></a> •
<a href="https://notes.aimodels.fyi/researchers-announce-tora-training-language-models-to-better-understand-math-using-external-tools/">[🍀 Unofficial Blog]</a>
<!-- <a href="#-quick-start">Quick Start</a> • -->
<!-- <a href="#%EF%B8%8F-citation">Citation</a> -->
</p>
<p align="center">
Repo for "<a href="https://arxiv.org/abs/2309.17452" target="_blank">ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving</a>"
</p>
## 🔥 News
- [2023/10/08] 🔥🔥🔥 All ToRA models released at [HuggingFace](https://huggingface.co/llm-agents)!!!
- [2023/09/29] ToRA paper, repo, and website released.
## 💡 Introduction
ToRA is a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical reasoning problems by interacting with tools, e.g., computation libraries and symbolic solvers. ToRA series seamlessly integrate natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of external tools.
| Model | Size | GSM8k | MATH | AVG@10 math tasks<sup>†</sup> |
|---|---|---|---|---|
| GPT-4 | - | 92.0 | 42.5 | 78.3 |
| GPT-4 (PAL) | - | 94.2 | 51.8 | 86.4 |
| [ToRA-7B](https://huggingface.co/llm-agents/tora-7b-v1.0) | 7B | 68.8 | 40.1 | 62.4|
| [ToRA-Code-7B](https://huggingface.co/llm-agents/tora-code-7b-v1.0) | 7B | 72.6 | 44.6 | 66.5|
| [ToRA-13B](https://huggingface.co/llm-agents/tora-13b-v1.0) | 13B | 72.7 | 43.0 | 65.9|
| [ToRA-Code-13B](https://huggingface.co/llm-agents/tora-code-13b-v1.0) | 13B | 75.8 | 48.1 | 71.3 |
| [ToRA-Code-34B<sup>*</sup>](https://huggingface.co/llm-agents/tora-code-34b-v1.0) | 34B | 80.7 | **51.0** | 74.8 |
| [ToRA-70B](https://huggingface.co/llm-agents/tora-70b-v1.0) | 70B | **84.3** | 49.7 | **76.9** |
- <sup>*</sup>ToRA-Code-34B is currently the first and only open-source model to achieve over 50% accuracy (pass@1) on the MATH dataset, which significantly outperforms GPT-4’s CoT result (51.0 vs. 42.5), and is competitive with GPT-4 solving problems with programs. By open-sourcing our codes and models, we hope more breakthroughs will come!
- <sup>†</sup>10 math tasks include GSM8k, MATH, GSM-Hard, SVAMP, TabMWP, ASDiv, SingleEQ, SingleOP, AddSub, and MultiArith.
## ⚡️ Training
The models are trained on ToRA-Corpus 16k, which contains tool-integrated reasoning trajectories of MATH and GSM8k from GPT-4.
We use imitation learning (i.e., SFT) to fine-tune the models, and then apply our proposed *output space shaping* to improve tool-integrated reasoning behaviors. Please refer to the [paper](https://arxiv.org/pdf/2309.17452.pdf) for more details.
## 🪁 Inference & Evaluation
Please refer to ToRA's [GitHub repo](https://github.com/microsoft/ToRA) for inference, evaluation, and training code.
## ☕️ Citation
If you find this repository helpful, please consider citing our paper:
```
@misc{gou2023tora,
title={ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving},
author={Zhibin Gou and Zhihong Shao and Yeyun Gong and yelong shen and Yujiu Yang and Minlie Huang and Nan Duan and Weizhu Chen},
year={2023},
eprint={2309.17452},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
yuntaeyang/Yi-6B-ko-dpo
|
yuntaeyang
| 2024-01-03T05:03:51Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"region:us"
] | null | 2024-01-03T04:47:09Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
Reni743/my_awesome_eli5_mlm_model
|
Reni743
| 2024-01-03T05:01:29Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-21T09:06:04Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_keras_callback
model-index:
- name: Reni743/my_awesome_eli5_mlm_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. -->
# Reni743/my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.0207
- Validation Loss: 1.8241
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.0207 | 1.8241 | 0 |
### Framework versions
- Transformers 4.36.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
cuongdz01/layoutlm-cord-3
|
cuongdz01
| 2024-01-03T05:00:28Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"layoutlm",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlm-base-uncased",
"base_model:finetune:microsoft/layoutlm-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-03T04:49:37Z |
---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlm-cord-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlm-cord-3
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1551
- Enu.cnt: {'precision': 0.9817351598173516, 'recall': 0.9772727272727273, 'f1': 0.979498861047836, 'number': 220}
- Enu.discountprice: {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10}
- Enu.etc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
- Enu.itemsubtotal: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}
- Enu.nm: {'precision': 0.972, 'recall': 0.9681274900398407, 'f1': 0.9700598802395209, 'number': 251}
- Enu.num: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
- Enu.price: {'precision': 0.9683794466403162, 'recall': 0.9959349593495935, 'f1': 0.9819639278557115, 'number': 246}
- Enu.sub.cnt: {'precision': 0.8947368421052632, 'recall': 1.0, 'f1': 0.9444444444444444, 'number': 17}
- Enu.sub.nm: {'precision': 0.9375, 'recall': 0.967741935483871, 'f1': 0.9523809523809523, 'number': 31}
- Enu.sub.price: {'precision': 0.95, 'recall': 0.95, 'f1': 0.9500000000000001, 'number': 20}
- Enu.unitprice: {'precision': 0.9242424242424242, 'recall': 0.9104477611940298, 'f1': 0.9172932330827067, 'number': 67}
- Otal.cashprice: {'precision': 0.9538461538461539, 'recall': 0.9117647058823529, 'f1': 0.9323308270676691, 'number': 68}
- Otal.changeprice: {'precision': 0.9642857142857143, 'recall': 0.9642857142857143, 'f1': 0.9642857142857143, 'number': 56}
- Otal.creditcardprice: {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16}
- Otal.emoneyprice: {'precision': 0.5, 'recall': 1.0, 'f1': 0.6666666666666666, 'number': 2}
- Otal.menuqty Cnt: {'precision': 0.9032258064516129, 'recall': 0.9655172413793104, 'f1': 0.9333333333333333, 'number': 29}
- Otal.menutype Cnt: {'precision': 0.8, 'recall': 0.5714285714285714, 'f1': 0.6666666666666666, 'number': 7}
- Otal.total Etc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
- Otal.total Price: {'precision': 0.9484536082474226, 'recall': 0.968421052631579, 'f1': 0.9583333333333333, 'number': 95}
- Ub Total.discount Price: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7}
- Ub Total.etc: {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1': 0.7777777777777778, 'number': 9}
- Ub Total.service Price: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}
- Ub Total.subtotal Price: {'precision': 0.9402985074626866, 'recall': 0.9692307692307692, 'f1': 0.9545454545454547, 'number': 65}
- Ub Total.tax Price: {'precision': 0.9772727272727273, 'recall': 1.0, 'f1': 0.9885057471264368, 'number': 43}
- Overall Precision: 0.9560
- Overall Recall: 0.9560
- Overall F1: 0.9560
- Overall Accuracy: 0.9732
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Enu.cnt | Enu.discountprice | Enu.etc | Enu.itemsubtotal | Enu.nm | Enu.num | Enu.price | Enu.sub.cnt | Enu.sub.nm | Enu.sub.price | Enu.unitprice | Otal.cashprice | Otal.changeprice | Otal.creditcardprice | Otal.emoneyprice | Otal.menuqty Cnt | Otal.menutype Cnt | Otal.total Etc | Otal.total Price | Ub Total.discount Price | Ub Total.etc | Ub Total.service Price | Ub Total.subtotal Price | Ub Total.tax Price | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 2.2284 | 1.0 | 25 | 1.3305 | {'precision': 0.7640449438202247, 'recall': 0.9272727272727272, 'f1': 0.8377823408624229, 'number': 220} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.6494252873563219, 'recall': 0.900398406374502, 'f1': 0.7545909849749582, 'number': 251} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.5171503957783641, 'recall': 0.7967479674796748, 'f1': 0.6272, 'number': 246} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 31} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 67} | {'precision': 0.25, 'recall': 0.4117647058823529, 'f1': 0.3111111111111111, 'number': 68} | {'precision': 0.11428571428571428, 'recall': 0.14285714285714285, 'f1': 0.12698412698412698, 'number': 56} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 29} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.2978723404255319, 'recall': 0.5894736842105263, 'f1': 0.3957597173144876, 'number': 95} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | {'precision': 0.047619047619047616, 'recall': 0.046153846153846156, 'f1': 0.046875, 'number': 65} | {'precision': 0.08450704225352113, 'recall': 0.13953488372093023, 'f1': 0.10526315789473684, 'number': 43} | 0.4802 | 0.5618 | 0.5178 | 0.6786 |
| 1.0492 | 2.0 | 50 | 0.6552 | {'precision': 0.8699186991869918, 'recall': 0.9727272727272728, 'f1': 0.9184549356223175, 'number': 220} | {'precision': 0.6, 'recall': 0.3, 'f1': 0.4, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.823321554770318, 'recall': 0.9282868525896414, 'f1': 0.8726591760299625, 'number': 251} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.7363013698630136, 'recall': 0.8739837398373984, 'f1': 0.7992565055762081, 'number': 246} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.4, 'recall': 0.12903225806451613, 'f1': 0.1951219512195122, 'number': 31} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 20} | {'precision': 0.88, 'recall': 0.3283582089552239, 'f1': 0.4782608695652174, 'number': 67} | {'precision': 0.7272727272727273, 'recall': 0.8235294117647058, 'f1': 0.7724137931034483, 'number': 68} | {'precision': 0.6575342465753424, 'recall': 0.8571428571428571, 'f1': 0.7441860465116279, 'number': 56} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.5, 'recall': 0.5172413793103449, 'f1': 0.5084745762711865, 'number': 29} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.7685185185185185, 'recall': 0.8736842105263158, 'f1': 0.8177339901477831, 'number': 95} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12} | {'precision': 0.8260869565217391, 'recall': 0.8769230769230769, 'f1': 0.8507462686567164, 'number': 65} | {'precision': 0.3387096774193548, 'recall': 0.4883720930232558, 'f1': 0.4, 'number': 43} | 0.7539 | 0.7504 | 0.7521 | 0.8206 |
| 0.5737 | 3.0 | 75 | 0.3999 | {'precision': 0.8693877551020408, 'recall': 0.9681818181818181, 'f1': 0.9161290322580645, 'number': 220} | {'precision': 0.6666666666666666, 'recall': 0.6, 'f1': 0.631578947368421, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.8534798534798534, 'recall': 0.9282868525896414, 'f1': 0.8893129770992366, 'number': 251} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 11} | {'precision': 0.9166666666666666, 'recall': 0.983739837398374, 'f1': 0.9490196078431372, 'number': 246} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.5833333333333334, 'recall': 0.45161290322580644, 'f1': 0.509090909090909, 'number': 31} | {'precision': 1.0, 'recall': 0.4, 'f1': 0.5714285714285715, 'number': 20} | {'precision': 0.8676470588235294, 'recall': 0.8805970149253731, 'f1': 0.874074074074074, 'number': 67} | {'precision': 0.8923076923076924, 'recall': 0.8529411764705882, 'f1': 0.8721804511278195, 'number': 68} | {'precision': 0.8524590163934426, 'recall': 0.9285714285714286, 'f1': 0.888888888888889, 'number': 56} | {'precision': 0.631578947368421, 'recall': 0.75, 'f1': 0.6857142857142857, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.5714285714285714, 'recall': 0.6896551724137931, 'f1': 0.625, 'number': 29} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9090909090909091, 'recall': 0.9473684210526315, 'f1': 0.9278350515463918, 'number': 95} | {'precision': 0.6666666666666666, 'recall': 0.2857142857142857, 'f1': 0.4, 'number': 7} | {'precision': 0.5, 'recall': 0.3333333333333333, 'f1': 0.4, 'number': 9} | {'precision': 0.75, 'recall': 0.75, 'f1': 0.75, 'number': 12} | {'precision': 0.8985507246376812, 'recall': 0.9538461538461539, 'f1': 0.9253731343283582, 'number': 65} | {'precision': 0.7169811320754716, 'recall': 0.8837209302325582, 'f1': 0.7916666666666666, 'number': 43} | 0.8538 | 0.8663 | 0.8600 | 0.9018 |
| 0.3586 | 4.0 | 100 | 0.2909 | {'precision': 0.8801652892561983, 'recall': 0.9681818181818181, 'f1': 0.922077922077922, 'number': 220} | {'precision': 0.875, 'recall': 0.7, 'f1': 0.7777777777777777, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.8981132075471698, 'recall': 0.9482071713147411, 'f1': 0.9224806201550388, 'number': 251} | {'precision': 1.0, 'recall': 0.6363636363636364, 'f1': 0.7777777777777778, 'number': 11} | {'precision': 0.9315589353612167, 'recall': 0.9959349593495935, 'f1': 0.962671905697446, 'number': 246} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.7586206896551724, 'recall': 0.7096774193548387, 'f1': 0.7333333333333333, 'number': 31} | {'precision': 1.0, 'recall': 0.65, 'f1': 0.787878787878788, 'number': 20} | {'precision': 0.8823529411764706, 'recall': 0.8955223880597015, 'f1': 0.888888888888889, 'number': 67} | {'precision': 0.921875, 'recall': 0.8676470588235294, 'f1': 0.893939393939394, 'number': 68} | {'precision': 0.8983050847457628, 'recall': 0.9464285714285714, 'f1': 0.9217391304347826, 'number': 56} | {'precision': 0.6111111111111112, 'recall': 0.6875, 'f1': 0.6470588235294118, 'number': 16} | {'precision': 1.0, 'recall': 0.5, 'f1': 0.6666666666666666, 'number': 2} | {'precision': 0.7419354838709677, 'recall': 0.7931034482758621, 'f1': 0.7666666666666667, 'number': 29} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9175257731958762, 'recall': 0.9368421052631579, 'f1': 0.9270833333333333, 'number': 95} | {'precision': 0.8571428571428571, 'recall': 0.8571428571428571, 'f1': 0.8571428571428571, 'number': 7} | {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1': 0.7777777777777778, 'number': 9} | {'precision': 0.8461538461538461, 'recall': 0.9166666666666666, 'f1': 0.8799999999999999, 'number': 12} | {'precision': 0.9393939393939394, 'recall': 0.9538461538461539, 'f1': 0.9465648854961831, 'number': 65} | {'precision': 0.9318181818181818, 'recall': 0.9534883720930233, 'f1': 0.942528735632184, 'number': 43} | 0.8957 | 0.9026 | 0.8992 | 0.9337 |
| 0.2476 | 5.0 | 125 | 0.2116 | {'precision': 0.963963963963964, 'recall': 0.9727272727272728, 'f1': 0.9683257918552037, 'number': 220} | {'precision': 0.875, 'recall': 0.7, 'f1': 0.7777777777777777, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9224806201550387, 'recall': 0.9482071713147411, 'f1': 0.9351669941060905, 'number': 251} | {'precision': 1.0, 'recall': 0.6363636363636364, 'f1': 0.7777777777777778, 'number': 11} | {'precision': 0.9496124031007752, 'recall': 0.9959349593495935, 'f1': 0.9722222222222223, 'number': 246} | {'precision': 0.9333333333333333, 'recall': 0.8235294117647058, 'f1': 0.8749999999999999, 'number': 17} | {'precision': 0.78125, 'recall': 0.8064516129032258, 'f1': 0.7936507936507936, 'number': 31} | {'precision': 1.0, 'recall': 0.75, 'f1': 0.8571428571428571, 'number': 20} | {'precision': 0.8823529411764706, 'recall': 0.8955223880597015, 'f1': 0.888888888888889, 'number': 67} | {'precision': 0.9375, 'recall': 0.8823529411764706, 'f1': 0.9090909090909091, 'number': 68} | {'precision': 0.9310344827586207, 'recall': 0.9642857142857143, 'f1': 0.9473684210526316, 'number': 56} | {'precision': 0.7368421052631579, 'recall': 0.875, 'f1': 0.7999999999999999, 'number': 16} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 0.75, 'recall': 0.8275862068965517, 'f1': 0.7868852459016394, 'number': 29} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9285714285714286, 'recall': 0.9578947368421052, 'f1': 0.9430051813471502, 'number': 95} | {'precision': 0.8571428571428571, 'recall': 0.8571428571428571, 'f1': 0.8571428571428571, 'number': 7} | {'precision': 0.875, 'recall': 0.7777777777777778, 'f1': 0.823529411764706, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.9393939393939394, 'recall': 0.9538461538461539, 'f1': 0.9465648854961831, 'number': 65} | {'precision': 0.9545454545454546, 'recall': 0.9767441860465116, 'f1': 0.9655172413793104, 'number': 43} | 0.9273 | 0.9266 | 0.9269 | 0.9515 |
| 0.1798 | 6.0 | 150 | 0.1988 | {'precision': 0.960352422907489, 'recall': 0.990909090909091, 'f1': 0.9753914988814317, 'number': 220} | {'precision': 0.75, 'recall': 0.6, 'f1': 0.6666666666666665, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9266409266409267, 'recall': 0.9561752988047809, 'f1': 0.9411764705882354, 'number': 251} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.953307392996109, 'recall': 0.9959349593495935, 'f1': 0.9741550695825051, 'number': 246} | {'precision': 1.0, 'recall': 0.9411764705882353, 'f1': 0.9696969696969697, 'number': 17} | {'precision': 0.8064516129032258, 'recall': 0.8064516129032258, 'f1': 0.8064516129032258, 'number': 31} | {'precision': 0.9411764705882353, 'recall': 0.8, 'f1': 0.8648648648648648, 'number': 20} | {'precision': 0.8695652173913043, 'recall': 0.8955223880597015, 'f1': 0.8823529411764706, 'number': 67} | {'precision': 0.9242424242424242, 'recall': 0.8970588235294118, 'f1': 0.9104477611940298, 'number': 68} | {'precision': 0.9310344827586207, 'recall': 0.9642857142857143, 'f1': 0.9473684210526316, 'number': 56} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2} | {'precision': 0.71875, 'recall': 0.7931034482758621, 'f1': 0.7540983606557378, 'number': 29} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9278350515463918, 'recall': 0.9473684210526315, 'f1': 0.9374999999999999, 'number': 95} | {'precision': 0.8571428571428571, 'recall': 0.8571428571428571, 'f1': 0.8571428571428571, 'number': 7} | {'precision': 0.875, 'recall': 0.7777777777777778, 'f1': 0.823529411764706, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.9402985074626866, 'recall': 0.9692307692307692, 'f1': 0.9545454545454547, 'number': 65} | {'precision': 0.9772727272727273, 'recall': 1.0, 'f1': 0.9885057471264368, 'number': 43} | 0.9265 | 0.9359 | 0.9312 | 0.9549 |
| 0.1404 | 7.0 | 175 | 0.1749 | {'precision': 0.9861751152073732, 'recall': 0.9727272727272728, 'f1': 0.9794050343249429, 'number': 220} | {'precision': 0.875, 'recall': 0.7, 'f1': 0.7777777777777777, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9404761904761905, 'recall': 0.9442231075697212, 'f1': 0.9423459244532804, 'number': 251} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.9682539682539683, 'recall': 0.991869918699187, 'f1': 0.9799196787148594, 'number': 246} | {'precision': 0.8095238095238095, 'recall': 1.0, 'f1': 0.8947368421052632, 'number': 17} | {'precision': 0.7142857142857143, 'recall': 0.967741935483871, 'f1': 0.821917808219178, 'number': 31} | {'precision': 0.9047619047619048, 'recall': 0.95, 'f1': 0.9268292682926829, 'number': 20} | {'precision': 0.8970588235294118, 'recall': 0.9104477611940298, 'f1': 0.9037037037037037, 'number': 67} | {'precision': 0.967741935483871, 'recall': 0.8823529411764706, 'f1': 0.923076923076923, 'number': 68} | {'precision': 0.9482758620689655, 'recall': 0.9821428571428571, 'f1': 0.9649122807017544, 'number': 56} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16} | {'precision': 0.6666666666666666, 'recall': 1.0, 'f1': 0.8, 'number': 2} | {'precision': 0.8387096774193549, 'recall': 0.896551724137931, 'f1': 0.8666666666666666, 'number': 29} | {'precision': 0.5, 'recall': 0.2857142857142857, 'f1': 0.36363636363636365, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9381443298969072, 'recall': 0.9578947368421052, 'f1': 0.9479166666666666, 'number': 95} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} | {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1': 0.7777777777777778, 'number': 9} | {'precision': 0.9166666666666666, 'recall': 0.9166666666666666, 'f1': 0.9166666666666666, 'number': 12} | {'precision': 0.9264705882352942, 'recall': 0.9692307692307692, 'f1': 0.9473684210526316, 'number': 65} | {'precision': 0.9130434782608695, 'recall': 0.9767441860465116, 'f1': 0.9438202247191011, 'number': 43} | 0.9312 | 0.9420 | 0.9366 | 0.9592 |
| 0.1111 | 8.0 | 200 | 0.1745 | {'precision': 0.9817351598173516, 'recall': 0.9772727272727273, 'f1': 0.979498861047836, 'number': 220} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9409448818897638, 'recall': 0.952191235059761, 'f1': 0.9465346534653465, 'number': 251} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.9606299212598425, 'recall': 0.991869918699187, 'f1': 0.976, 'number': 246} | {'precision': 0.85, 'recall': 1.0, 'f1': 0.9189189189189189, 'number': 17} | {'precision': 0.8285714285714286, 'recall': 0.9354838709677419, 'f1': 0.8787878787878788, 'number': 31} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.8955223880597015, 'recall': 0.8955223880597015, 'f1': 0.8955223880597015, 'number': 67} | {'precision': 0.967741935483871, 'recall': 0.8823529411764706, 'f1': 0.923076923076923, 'number': 68} | {'precision': 0.9152542372881356, 'recall': 0.9642857142857143, 'f1': 0.9391304347826087, 'number': 56} | {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1': 0.7647058823529411, 'number': 16} | {'precision': 1.0, 'recall': 0.5, 'f1': 0.6666666666666666, 'number': 2} | {'precision': 0.8387096774193549, 'recall': 0.896551724137931, 'f1': 0.8666666666666666, 'number': 29} | {'precision': 0.6666666666666666, 'recall': 0.2857142857142857, 'f1': 0.4, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9381443298969072, 'recall': 0.9578947368421052, 'f1': 0.9479166666666666, 'number': 95} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} | {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1': 0.7777777777777778, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.9402985074626866, 'recall': 0.9692307692307692, 'f1': 0.9545454545454547, 'number': 65} | {'precision': 0.9772727272727273, 'recall': 1.0, 'f1': 0.9885057471264368, 'number': 43} | 0.9377 | 0.9420 | 0.9399 | 0.9626 |
| 0.0999 | 9.0 | 225 | 0.1637 | {'precision': 0.9773755656108597, 'recall': 0.9818181818181818, 'f1': 0.9795918367346939, 'number': 220} | {'precision': 0.8181818181818182, 'recall': 0.9, 'f1': 0.8571428571428572, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.952755905511811, 'recall': 0.9641434262948207, 'f1': 0.9584158415841584, 'number': 251} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.95703125, 'recall': 0.9959349593495935, 'f1': 0.9760956175298804, 'number': 246} | {'precision': 0.8947368421052632, 'recall': 1.0, 'f1': 0.9444444444444444, 'number': 17} | {'precision': 0.9354838709677419, 'recall': 0.9354838709677419, 'f1': 0.9354838709677419, 'number': 31} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.9242424242424242, 'recall': 0.9104477611940298, 'f1': 0.9172932330827067, 'number': 67} | {'precision': 0.953125, 'recall': 0.8970588235294118, 'f1': 0.9242424242424244, 'number': 68} | {'precision': 0.9, 'recall': 0.9642857142857143, 'f1': 0.9310344827586207, 'number': 56} | {'precision': 0.8125, 'recall': 0.8125, 'f1': 0.8125, 'number': 16} | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.9, 'recall': 0.9310344827586207, 'f1': 0.9152542372881356, 'number': 29} | {'precision': 0.8, 'recall': 0.5714285714285714, 'f1': 0.6666666666666666, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9587628865979382, 'recall': 0.9789473684210527, 'f1': 0.96875, 'number': 95} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} | {'precision': 0.875, 'recall': 0.7777777777777778, 'f1': 0.823529411764706, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.9264705882352942, 'recall': 0.9692307692307692, 'f1': 0.9473684210526316, 'number': 65} | {'precision': 0.9772727272727273, 'recall': 1.0, 'f1': 0.9885057471264368, 'number': 43} | 0.9463 | 0.9529 | 0.9496 | 0.9694 |
| 0.0847 | 10.0 | 250 | 0.1764 | {'precision': 0.9773755656108597, 'recall': 0.9818181818181818, 'f1': 0.9795918367346939, 'number': 220} | {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9606299212598425, 'recall': 0.9721115537848606, 'f1': 0.9663366336633663, 'number': 251} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9607843137254902, 'recall': 0.9959349593495935, 'f1': 0.9780439121756488, 'number': 246} | {'precision': 0.8947368421052632, 'recall': 1.0, 'f1': 0.9444444444444444, 'number': 17} | {'precision': 0.8387096774193549, 'recall': 0.8387096774193549, 'f1': 0.8387096774193549, 'number': 31} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.9242424242424242, 'recall': 0.9104477611940298, 'f1': 0.9172932330827067, 'number': 67} | {'precision': 0.9545454545454546, 'recall': 0.9264705882352942, 'f1': 0.9402985074626866, 'number': 68} | {'precision': 0.9310344827586207, 'recall': 0.9642857142857143, 'f1': 0.9473684210526316, 'number': 56} | {'precision': 0.8125, 'recall': 0.8125, 'f1': 0.8125, 'number': 16} | {'precision': 0.3333333333333333, 'recall': 0.5, 'f1': 0.4, 'number': 2} | {'precision': 0.8666666666666667, 'recall': 0.896551724137931, 'f1': 0.8813559322033899, 'number': 29} | {'precision': 1.0, 'recall': 0.42857142857142855, 'f1': 0.6, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9285714285714286, 'recall': 0.9578947368421052, 'f1': 0.9430051813471502, 'number': 95} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} | {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1': 0.7777777777777778, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.9264705882352942, 'recall': 0.9692307692307692, 'f1': 0.9473684210526316, 'number': 65} | {'precision': 0.9772727272727273, 'recall': 1.0, 'f1': 0.9885057471264368, 'number': 43} | 0.9447 | 0.9498 | 0.9472 | 0.9656 |
| 0.0761 | 11.0 | 275 | 0.1647 | {'precision': 0.9818181818181818, 'recall': 0.9818181818181818, 'f1': 0.9818181818181818, 'number': 220} | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9682539682539683, 'recall': 0.9721115537848606, 'f1': 0.9701789264413518, 'number': 251} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.9645669291338582, 'recall': 0.9959349593495935, 'f1': 0.98, 'number': 246} | {'precision': 0.8947368421052632, 'recall': 1.0, 'f1': 0.9444444444444444, 'number': 17} | {'precision': 0.9354838709677419, 'recall': 0.9354838709677419, 'f1': 0.9354838709677419, 'number': 31} | {'precision': 0.95, 'recall': 0.95, 'f1': 0.9500000000000001, 'number': 20} | {'precision': 0.9384615384615385, 'recall': 0.9104477611940298, 'f1': 0.9242424242424243, 'number': 67} | {'precision': 0.9538461538461539, 'recall': 0.9117647058823529, 'f1': 0.9323308270676691, 'number': 68} | {'precision': 0.9473684210526315, 'recall': 0.9642857142857143, 'f1': 0.9557522123893805, 'number': 56} | {'precision': 0.8125, 'recall': 0.8125, 'f1': 0.8125, 'number': 16} | {'precision': 0.3333333333333333, 'recall': 0.5, 'f1': 0.4, 'number': 2} | {'precision': 0.9032258064516129, 'recall': 0.9655172413793104, 'f1': 0.9333333333333333, 'number': 29} | {'precision': 0.8, 'recall': 0.5714285714285714, 'f1': 0.6666666666666666, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9484536082474226, 'recall': 0.968421052631579, 'f1': 0.9583333333333333, 'number': 95} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} | {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1': 0.7777777777777778, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.9264705882352942, 'recall': 0.9692307692307692, 'f1': 0.9473684210526316, 'number': 65} | {'precision': 0.9772727272727273, 'recall': 1.0, 'f1': 0.9885057471264368, 'number': 43} | 0.9537 | 0.9560 | 0.9548 | 0.9711 |
| 0.0648 | 12.0 | 300 | 0.1609 | {'precision': 0.9863013698630136, 'recall': 0.9818181818181818, 'f1': 0.9840546697038723, 'number': 220} | {'precision': 0.8888888888888888, 'recall': 0.8, 'f1': 0.8421052631578948, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.9721115537848606, 'recall': 0.9721115537848606, 'f1': 0.9721115537848606, 'number': 251} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9683794466403162, 'recall': 0.9959349593495935, 'f1': 0.9819639278557115, 'number': 246} | {'precision': 0.8947368421052632, 'recall': 1.0, 'f1': 0.9444444444444444, 'number': 17} | {'precision': 0.8787878787878788, 'recall': 0.9354838709677419, 'f1': 0.90625, 'number': 31} | {'precision': 0.95, 'recall': 0.95, 'f1': 0.9500000000000001, 'number': 20} | {'precision': 0.9393939393939394, 'recall': 0.9253731343283582, 'f1': 0.9323308270676692, 'number': 67} | {'precision': 0.9538461538461539, 'recall': 0.9117647058823529, 'f1': 0.9323308270676691, 'number': 68} | {'precision': 0.9152542372881356, 'recall': 0.9642857142857143, 'f1': 0.9391304347826087, 'number': 56} | {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16} | {'precision': 0.3333333333333333, 'recall': 0.5, 'f1': 0.4, 'number': 2} | {'precision': 0.9032258064516129, 'recall': 0.9655172413793104, 'f1': 0.9333333333333333, 'number': 29} | {'precision': 0.8, 'recall': 0.5714285714285714, 'f1': 0.6666666666666666, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.92, 'recall': 0.968421052631579, 'f1': 0.9435897435897437, 'number': 95} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} | {'precision': 0.875, 'recall': 0.7777777777777778, 'f1': 0.823529411764706, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.953125, 'recall': 0.9384615384615385, 'f1': 0.9457364341085271, 'number': 65} | {'precision': 0.9555555555555556, 'recall': 1.0, 'f1': 0.9772727272727273, 'number': 43} | 0.9515 | 0.9544 | 0.9529 | 0.9702 |
| 0.0554 | 13.0 | 325 | 0.1558 | {'precision': 0.9861751152073732, 'recall': 0.9727272727272728, 'f1': 0.9794050343249429, 'number': 220} | {'precision': 0.8, 'recall': 0.8, 'f1': 0.8000000000000002, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.968, 'recall': 0.9641434262948207, 'f1': 0.9660678642714571, 'number': 251} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9683794466403162, 'recall': 0.9959349593495935, 'f1': 0.9819639278557115, 'number': 246} | {'precision': 0.8947368421052632, 'recall': 1.0, 'f1': 0.9444444444444444, 'number': 17} | {'precision': 0.90625, 'recall': 0.9354838709677419, 'f1': 0.9206349206349206, 'number': 31} | {'precision': 0.95, 'recall': 0.95, 'f1': 0.9500000000000001, 'number': 20} | {'precision': 0.9393939393939394, 'recall': 0.9253731343283582, 'f1': 0.9323308270676692, 'number': 67} | {'precision': 0.9692307692307692, 'recall': 0.9264705882352942, 'f1': 0.9473684210526316, 'number': 68} | {'precision': 0.9310344827586207, 'recall': 0.9642857142857143, 'f1': 0.9473684210526316, 'number': 56} | {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16} | {'precision': 0.3333333333333333, 'recall': 0.5, 'f1': 0.4, 'number': 2} | {'precision': 0.9032258064516129, 'recall': 0.9655172413793104, 'f1': 0.9333333333333333, 'number': 29} | {'precision': 0.8, 'recall': 0.5714285714285714, 'f1': 0.6666666666666666, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9484536082474226, 'recall': 0.968421052631579, 'f1': 0.9583333333333333, 'number': 95} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} | {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1': 0.7777777777777778, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.9402985074626866, 'recall': 0.9692307692307692, 'f1': 0.9545454545454547, 'number': 65} | {'precision': 0.9772727272727273, 'recall': 1.0, 'f1': 0.9885057471264368, 'number': 43} | 0.9536 | 0.9536 | 0.9536 | 0.9724 |
| 0.0517 | 14.0 | 350 | 0.1571 | {'precision': 0.981651376146789, 'recall': 0.9727272727272728, 'f1': 0.9771689497716896, 'number': 220} | {'precision': 0.8888888888888888, 'recall': 0.8, 'f1': 0.8421052631578948, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.968, 'recall': 0.9641434262948207, 'f1': 0.9660678642714571, 'number': 251} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9683794466403162, 'recall': 0.9959349593495935, 'f1': 0.9819639278557115, 'number': 246} | {'precision': 0.8947368421052632, 'recall': 1.0, 'f1': 0.9444444444444444, 'number': 17} | {'precision': 0.9375, 'recall': 0.967741935483871, 'f1': 0.9523809523809523, 'number': 31} | {'precision': 0.95, 'recall': 0.95, 'f1': 0.9500000000000001, 'number': 20} | {'precision': 0.9242424242424242, 'recall': 0.9104477611940298, 'f1': 0.9172932330827067, 'number': 67} | {'precision': 0.9692307692307692, 'recall': 0.9264705882352942, 'f1': 0.9473684210526316, 'number': 68} | {'precision': 0.9473684210526315, 'recall': 0.9642857142857143, 'f1': 0.9557522123893805, 'number': 56} | {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1': 0.7647058823529411, 'number': 16} | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.9032258064516129, 'recall': 0.9655172413793104, 'f1': 0.9333333333333333, 'number': 29} | {'precision': 0.8, 'recall': 0.5714285714285714, 'f1': 0.6666666666666666, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9484536082474226, 'recall': 0.968421052631579, 'f1': 0.9583333333333333, 'number': 95} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} | {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1': 0.7777777777777778, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.9545454545454546, 'recall': 0.9692307692307692, 'f1': 0.9618320610687022, 'number': 65} | {'precision': 0.9772727272727273, 'recall': 1.0, 'f1': 0.9885057471264368, 'number': 43} | 0.9551 | 0.9536 | 0.9544 | 0.9719 |
| 0.05 | 15.0 | 375 | 0.1551 | {'precision': 0.9817351598173516, 'recall': 0.9772727272727273, 'f1': 0.979498861047836, 'number': 220} | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6} | {'precision': 0.972, 'recall': 0.9681274900398407, 'f1': 0.9700598802395209, 'number': 251} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9683794466403162, 'recall': 0.9959349593495935, 'f1': 0.9819639278557115, 'number': 246} | {'precision': 0.8947368421052632, 'recall': 1.0, 'f1': 0.9444444444444444, 'number': 17} | {'precision': 0.9375, 'recall': 0.967741935483871, 'f1': 0.9523809523809523, 'number': 31} | {'precision': 0.95, 'recall': 0.95, 'f1': 0.9500000000000001, 'number': 20} | {'precision': 0.9242424242424242, 'recall': 0.9104477611940298, 'f1': 0.9172932330827067, 'number': 67} | {'precision': 0.9538461538461539, 'recall': 0.9117647058823529, 'f1': 0.9323308270676691, 'number': 68} | {'precision': 0.9642857142857143, 'recall': 0.9642857142857143, 'f1': 0.9642857142857143, 'number': 56} | {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16} | {'precision': 0.5, 'recall': 1.0, 'f1': 0.6666666666666666, 'number': 2} | {'precision': 0.9032258064516129, 'recall': 0.9655172413793104, 'f1': 0.9333333333333333, 'number': 29} | {'precision': 0.8, 'recall': 0.5714285714285714, 'f1': 0.6666666666666666, 'number': 7} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.9484536082474226, 'recall': 0.968421052631579, 'f1': 0.9583333333333333, 'number': 95} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 7} | {'precision': 0.7777777777777778, 'recall': 0.7777777777777778, 'f1': 0.7777777777777778, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.9402985074626866, 'recall': 0.9692307692307692, 'f1': 0.9545454545454547, 'number': 65} | {'precision': 0.9772727272727273, 'recall': 1.0, 'f1': 0.9885057471264368, 'number': 43} | 0.9560 | 0.9560 | 0.9560 | 0.9732 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
royallab/Kimiko-10.7B-v3-exl2
|
royallab
| 2024-01-03T04:58:18Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2024-01-03T03:04:23Z |
---
language:
- en
---
## Information
This is a Exl2 quantized version of [Kimiko-10.7B-v3](https://huggingface.co/nRuaif/Kimiko-10.7B-v3)
Please refer to the original creator for more information.
Calibration dataset: Exllamav2 default
## Branches:
- main: Measurement files
- 4bpw: 4 bits per weight
- 5bpw: 5 bits per weight
- 6bpw: 6 bits per weight
## Notes
- 6bpw is recommended for the best quality to vram usage ratio (assuming you have enough vram).
- Please ask for more bpws in the community tab if necessary.
## Run in TabbyAPI
TabbyAPI is a pure exllamav2 FastAPI server developed by us. You can find TabbyAPI's source code here: [https://github.com/theroyallab/TabbyAPI](https://github.com/theroyallab/TabbyAPI)
If you don't have huggingface-cli, please run `pip install huggingface_hub`.
To run this model, follow these steps:
1. Make a directory inside your models folder called `Kimiko-10.7B-v3-exl2`
2. Open a terminal inside your models folder
3. Run `huggingface-cli download royallab/Kimiko-10.7B-v3-exl2 --revision 4bpw --local-dir Kimiko-10.7B-v3-exl2 --local-dir-use-symlinks False`
1. The `--revision` flag corresponds to the branch name on the model repo. Please select the appropriate bpw branch for your system.
4. Inside TabbyAPI's config.yml, set `model_name` to `Kimiko-10.7B-v3-exl2` or you can use the `/model/load` endpoint after launching.
5. Launch TabbyAPI inside your python env by running `python main.py`
## Donate?
All my infrastructure and cloud expenses are paid out of pocket. If you'd like to donate, you can do so here: https://ko-fi.com/kingbri
You should not feel obligated to donate, but if you do, I'd appreciate it.
---
|
RR6/my_awesome_eli5_mlm_model
|
RR6
| 2024-01-03T04:57:05Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-27T04:24:16Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_keras_callback
model-index:
- name: RR6/my_awesome_eli5_mlm_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. -->
# RR6/my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.8759
- Validation Loss: 1.8204
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.0352 | 1.8056 | 0 |
| 1.9320 | 1.7912 | 1 |
| 1.8759 | 1.8204 | 2 |
### Framework versions
- Transformers 4.36.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
aaa12963337/msi-nat-mini
|
aaa12963337
| 2024-01-03T04:47:43Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"nat",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T12:13:53Z |
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-nat-mini
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6308708414872799
- name: F1
type: f1
value: 0.47632740072381147
- name: Precision
type: precision
value: 0.6193914388860238
- name: Recall
type: recall
value: 0.3869512686266613
---
<!-- 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. -->
# msi-nat-mini
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8600
- Accuracy: 0.6309
- F1: 0.4763
- Precision: 0.6194
- Recall: 0.3870
## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5496 | 1.0 | 2015 | 0.7573 | 0.5955 | 0.4196 | 0.5559 | 0.3369 |
| 0.4807 | 2.0 | 4031 | 0.7416 | 0.6309 | 0.4981 | 0.6074 | 0.4222 |
| 0.4235 | 3.0 | 6047 | 0.7680 | 0.6325 | 0.5047 | 0.6076 | 0.4317 |
| 0.3879 | 4.0 | 8063 | 0.7875 | 0.6339 | 0.4923 | 0.6179 | 0.4092 |
| 0.3702 | 5.0 | 10078 | 0.7923 | 0.6383 | 0.5128 | 0.6168 | 0.4388 |
| 0.3568 | 6.0 | 12094 | 0.8311 | 0.6313 | 0.4969 | 0.6090 | 0.4197 |
| 0.3661 | 7.0 | 14110 | 0.8345 | 0.6316 | 0.4843 | 0.6166 | 0.3987 |
| 0.354 | 8.0 | 16126 | 0.8501 | 0.6305 | 0.4800 | 0.6162 | 0.3931 |
| 0.3569 | 9.0 | 18141 | 0.8552 | 0.6318 | 0.4809 | 0.6193 | 0.3931 |
| 0.3536 | 10.0 | 20150 | 0.8600 | 0.6309 | 0.4763 | 0.6194 | 0.3870 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Kshitij2406/GPT_Test_F
|
Kshitij2406
| 2024-01-03T04:25:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"region:us"
] | null | 2024-01-03T04:15:15Z |
---
library_name: peft
base_model: tiiuae/falcon-7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
LoneStriker/tora-70b-v1.0-4.0bpw-h6-exl2
|
LoneStriker
| 2024-01-03T04:22:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"math",
"en",
"dataset:gsm8k",
"dataset:competition_math",
"arxiv:2309.17452",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-03T04:08:11Z |
---
license: llama2
datasets:
- gsm8k
- competition_math
language:
- en
metrics:
- exact_match
library_name: transformers
pipeline_tag: text-generation
tags:
- code
- math
---
<h1 align="center">
ToRA: A Tool-Integrated Reasoning Agent <br> for Mathematical Problem Solving
</h1>
<p align="center">
<a href="https://microsoft.github.io/ToRA/"><b>[🌐 Website]</b></a> •
<a href="https://arxiv.org/abs/2309.17452"><b>[📜 Paper]</b></a> •
<a href="https://huggingface.co/llm-agents"><b>[🤗 HF Models]</b></a> •
<a href="https://github.com/microsoft/ToRA"><b>[🐱 GitHub]</b></a>
<br>
<a href="https://twitter.com/zhs05232838/status/1708860992631763092"><b>[🐦 Twitter]</b></a> •
<a href="https://www.reddit.com/r/LocalLLaMA/comments/1703k6d/tora_a_toolintegrated_reasoning_agent_for/"><b>[💬 Reddit]</b></a> •
<a href="https://notes.aimodels.fyi/researchers-announce-tora-training-language-models-to-better-understand-math-using-external-tools/">[🍀 Unofficial Blog]</a>
<!-- <a href="#-quick-start">Quick Start</a> • -->
<!-- <a href="#%EF%B8%8F-citation">Citation</a> -->
</p>
<p align="center">
Repo for "<a href="https://arxiv.org/abs/2309.17452" target="_blank">ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving</a>"
</p>
## 🔥 News
- [2023/10/08] 🔥🔥🔥 All ToRA models released at [HuggingFace](https://huggingface.co/llm-agents)!!!
- [2023/09/29] ToRA paper, repo, and website released.
## 💡 Introduction
ToRA is a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical reasoning problems by interacting with tools, e.g., computation libraries and symbolic solvers. ToRA series seamlessly integrate natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of external tools.
| Model | Size | GSM8k | MATH | AVG@10 math tasks<sup>†</sup> |
|---|---|---|---|---|
| GPT-4 | - | 92.0 | 42.5 | 78.3 |
| GPT-4 (PAL) | - | 94.2 | 51.8 | 86.4 |
| [ToRA-7B](https://huggingface.co/llm-agents/tora-7b-v1.0) | 7B | 68.8 | 40.1 | 62.4|
| [ToRA-Code-7B](https://huggingface.co/llm-agents/tora-code-7b-v1.0) | 7B | 72.6 | 44.6 | 66.5|
| [ToRA-13B](https://huggingface.co/llm-agents/tora-13b-v1.0) | 13B | 72.7 | 43.0 | 65.9|
| [ToRA-Code-13B](https://huggingface.co/llm-agents/tora-code-13b-v1.0) | 13B | 75.8 | 48.1 | 71.3 |
| [ToRA-Code-34B<sup>*</sup>](https://huggingface.co/llm-agents/tora-code-34b-v1.0) | 34B | 80.7 | **51.0** | 74.8 |
| [ToRA-70B](https://huggingface.co/llm-agents/tora-70b-v1.0) | 70B | **84.3** | 49.7 | **76.9** |
- <sup>*</sup>ToRA-Code-34B is currently the first and only open-source model to achieve over 50% accuracy (pass@1) on the MATH dataset, which significantly outperforms GPT-4’s CoT result (51.0 vs. 42.5), and is competitive with GPT-4 solving problems with programs. By open-sourcing our codes and models, we hope more breakthroughs will come!
- <sup>†</sup>10 math tasks include GSM8k, MATH, GSM-Hard, SVAMP, TabMWP, ASDiv, SingleEQ, SingleOP, AddSub, and MultiArith.
## ⚡️ Training
The models are trained on ToRA-Corpus 16k, which contains tool-integrated reasoning trajectories of MATH and GSM8k from GPT-4.
We use imitation learning (i.e., SFT) to fine-tune the models, and then apply our proposed *output space shaping* to improve tool-integrated reasoning behaviors. Please refer to the [paper](https://arxiv.org/pdf/2309.17452.pdf) for more details.
## 🪁 Inference & Evaluation
Please refer to ToRA's [GitHub repo](https://github.com/microsoft/ToRA) for inference, evaluation, and training code.
## ☕️ Citation
If you find this repository helpful, please consider citing our paper:
```
@misc{gou2023tora,
title={ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving},
author={Zhibin Gou and Zhihong Shao and Yeyun Gong and yelong shen and Yujiu Yang and Minlie Huang and Nan Duan and Weizhu Chen},
year={2023},
eprint={2309.17452},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
learn3r/longt5_xl_govreport_4096_memsum_e30
|
learn3r
| 2024-01-03T04:15:12Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-01T09:25:17Z |
---
tags:
- generated_from_trainer
model-index:
- name: longt5_xl_govreport_4096_memsum_e30
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. -->
# longt5_xl_govreport_4096_memsum_e30
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9364
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1073 | 1.0 | 68 | 2.4897 |
| 0.0937 | 1.99 | 136 | 2.7041 |
| 0.0879 | 2.99 | 204 | 2.6437 |
| 0.0821 | 3.99 | 272 | 2.8059 |
| 0.0693 | 5.0 | 341 | 2.9269 |
| 0.0675 | 6.0 | 409 | 2.8654 |
| 0.0622 | 6.99 | 477 | 2.9698 |
| 0.065 | 7.99 | 545 | 2.8929 |
| 0.0578 | 8.99 | 613 | 2.9282 |
| 0.0528 | 9.97 | 680 | 2.9364 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Kshitij2406/GPT_Test_Falcon
|
Kshitij2406
| 2024-01-03T04:08:11Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:tiiuae/falcon-7b-instruct",
"base_model:adapter:tiiuae/falcon-7b-instruct",
"region:us"
] | null | 2024-01-03T03:57:52Z |
---
library_name: peft
base_model: tiiuae/falcon-7b-instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
nrshoudi/wav2vec-arabic-V2-50
|
nrshoudi
| 2024-01-03T03:44:39Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-03T00:28:05Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec-arabic-V2-50
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. -->
# wav2vec-arabic-V2-50
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3408
- Wer: 0.0460
- Per: 0.0347
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Per |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 10.1558 | 1.0 | 818 | 3.2108 | 1.0 | 1.0 |
| 3.0783 | 2.0 | 1636 | 2.0778 | 0.9260 | 0.9396 |
| 0.7602 | 3.0 | 2454 | 0.3836 | 0.1023 | 0.0870 |
| 0.2361 | 4.0 | 3272 | 0.3251 | 0.0668 | 0.0517 |
| 0.1602 | 5.0 | 4090 | 0.3240 | 0.0664 | 0.0515 |
| 0.1216 | 6.0 | 4908 | 0.3268 | 0.0673 | 0.0531 |
| 0.1078 | 7.0 | 5726 | 0.3501 | 0.0608 | 0.0465 |
| 0.0933 | 8.0 | 6544 | 0.3451 | 0.0538 | 0.0402 |
| 0.0713 | 9.0 | 7362 | 0.3658 | 0.0539 | 0.0407 |
| 0.0687 | 10.0 | 8180 | 0.3106 | 0.0519 | 0.0386 |
| 0.0561 | 11.0 | 8998 | 0.3322 | 0.0529 | 0.0396 |
| 0.0516 | 12.0 | 9816 | 0.3243 | 0.0484 | 0.0361 |
| 0.0392 | 13.0 | 10634 | 0.3412 | 0.0475 | 0.0354 |
| 0.037 | 14.0 | 11452 | 0.3370 | 0.0477 | 0.0359 |
| 0.0318 | 15.0 | 12270 | 0.3250 | 0.0466 | 0.0358 |
| 0.0291 | 16.0 | 13088 | 0.3451 | 0.0477 | 0.0359 |
| 0.025 | 17.0 | 13906 | 0.3713 | 0.0486 | 0.0368 |
| 0.0274 | 18.0 | 14724 | 0.3299 | 0.0459 | 0.0346 |
| 0.0208 | 19.0 | 15542 | 0.3451 | 0.0463 | 0.0349 |
| 0.0187 | 20.0 | 16360 | 0.3408 | 0.0460 | 0.0347 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
LoneStriker/tora-70b-v1.0-3.0bpw-h6-exl2
|
LoneStriker
| 2024-01-03T03:27:29Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"math",
"en",
"dataset:gsm8k",
"dataset:competition_math",
"arxiv:2309.17452",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-03T03:16:53Z |
---
license: llama2
datasets:
- gsm8k
- competition_math
language:
- en
metrics:
- exact_match
library_name: transformers
pipeline_tag: text-generation
tags:
- code
- math
---
<h1 align="center">
ToRA: A Tool-Integrated Reasoning Agent <br> for Mathematical Problem Solving
</h1>
<p align="center">
<a href="https://microsoft.github.io/ToRA/"><b>[🌐 Website]</b></a> •
<a href="https://arxiv.org/abs/2309.17452"><b>[📜 Paper]</b></a> •
<a href="https://huggingface.co/llm-agents"><b>[🤗 HF Models]</b></a> •
<a href="https://github.com/microsoft/ToRA"><b>[🐱 GitHub]</b></a>
<br>
<a href="https://twitter.com/zhs05232838/status/1708860992631763092"><b>[🐦 Twitter]</b></a> •
<a href="https://www.reddit.com/r/LocalLLaMA/comments/1703k6d/tora_a_toolintegrated_reasoning_agent_for/"><b>[💬 Reddit]</b></a> •
<a href="https://notes.aimodels.fyi/researchers-announce-tora-training-language-models-to-better-understand-math-using-external-tools/">[🍀 Unofficial Blog]</a>
<!-- <a href="#-quick-start">Quick Start</a> • -->
<!-- <a href="#%EF%B8%8F-citation">Citation</a> -->
</p>
<p align="center">
Repo for "<a href="https://arxiv.org/abs/2309.17452" target="_blank">ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving</a>"
</p>
## 🔥 News
- [2023/10/08] 🔥🔥🔥 All ToRA models released at [HuggingFace](https://huggingface.co/llm-agents)!!!
- [2023/09/29] ToRA paper, repo, and website released.
## 💡 Introduction
ToRA is a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical reasoning problems by interacting with tools, e.g., computation libraries and symbolic solvers. ToRA series seamlessly integrate natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of external tools.
| Model | Size | GSM8k | MATH | AVG@10 math tasks<sup>†</sup> |
|---|---|---|---|---|
| GPT-4 | - | 92.0 | 42.5 | 78.3 |
| GPT-4 (PAL) | - | 94.2 | 51.8 | 86.4 |
| [ToRA-7B](https://huggingface.co/llm-agents/tora-7b-v1.0) | 7B | 68.8 | 40.1 | 62.4|
| [ToRA-Code-7B](https://huggingface.co/llm-agents/tora-code-7b-v1.0) | 7B | 72.6 | 44.6 | 66.5|
| [ToRA-13B](https://huggingface.co/llm-agents/tora-13b-v1.0) | 13B | 72.7 | 43.0 | 65.9|
| [ToRA-Code-13B](https://huggingface.co/llm-agents/tora-code-13b-v1.0) | 13B | 75.8 | 48.1 | 71.3 |
| [ToRA-Code-34B<sup>*</sup>](https://huggingface.co/llm-agents/tora-code-34b-v1.0) | 34B | 80.7 | **51.0** | 74.8 |
| [ToRA-70B](https://huggingface.co/llm-agents/tora-70b-v1.0) | 70B | **84.3** | 49.7 | **76.9** |
- <sup>*</sup>ToRA-Code-34B is currently the first and only open-source model to achieve over 50% accuracy (pass@1) on the MATH dataset, which significantly outperforms GPT-4’s CoT result (51.0 vs. 42.5), and is competitive with GPT-4 solving problems with programs. By open-sourcing our codes and models, we hope more breakthroughs will come!
- <sup>†</sup>10 math tasks include GSM8k, MATH, GSM-Hard, SVAMP, TabMWP, ASDiv, SingleEQ, SingleOP, AddSub, and MultiArith.
## ⚡️ Training
The models are trained on ToRA-Corpus 16k, which contains tool-integrated reasoning trajectories of MATH and GSM8k from GPT-4.
We use imitation learning (i.e., SFT) to fine-tune the models, and then apply our proposed *output space shaping* to improve tool-integrated reasoning behaviors. Please refer to the [paper](https://arxiv.org/pdf/2309.17452.pdf) for more details.
## 🪁 Inference & Evaluation
Please refer to ToRA's [GitHub repo](https://github.com/microsoft/ToRA) for inference, evaluation, and training code.
## ☕️ Citation
If you find this repository helpful, please consider citing our paper:
```
@misc{gou2023tora,
title={ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving},
author={Zhibin Gou and Zhihong Shao and Yeyun Gong and yelong shen and Yujiu Yang and Minlie Huang and Nan Duan and Weizhu Chen},
year={2023},
eprint={2309.17452},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
alirzb/S5_M1_fold4_swint_42510045
|
alirzb
| 2024-01-03T03:25:18Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224",
"base_model:finetune:microsoft/swin-base-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-03T01:33:53Z |
---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S5_M1_fold4_swint_42510045
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.999194035865404
---
<!-- 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. -->
# S5_M1_fold4_swint_42510045
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0046
- Accuracy: 0.9992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0062 | 1.0 | 310 | 0.0110 | 0.9976 |
| 0.0102 | 2.0 | 620 | 0.0111 | 0.9968 |
| 0.011 | 3.0 | 930 | 0.0105 | 0.9976 |
| 0.0001 | 4.0 | 1241 | 0.0056 | 0.9990 |
| 0.0003 | 5.0 | 1550 | 0.0046 | 0.9992 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
|
tiagoblima/t5_large-qg-aas
|
tiagoblima
| 2024-01-03T03:23:27Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"dataset:tiagoblima/qg_squad_v1_pt",
"base_model:unicamp-dl/ptt5-large-t5-vocab",
"base_model:finetune:unicamp-dl/ptt5-large-t5-vocab",
"license:mit",
"region:us"
] | null | 2023-12-31T14:50:01Z |
---
license: mit
base_model: unicamp-dl/ptt5-large-t5-vocab
tags:
- generated_from_trainer
datasets:
- tiagoblima/qg_squad_v1_pt
model-index:
- name: t5_large-qg-aas
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. -->
# t5_large-qg-aas
This model is a fine-tuned version of [unicamp-dl/ptt5-large-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-large-t5-vocab) on the tiagoblima/qg_squad_v1_pt dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9208
## 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: 0.005
- train_batch_size: 64
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.0267 | 1.0 | 808 | 6.6599 |
| 5.1565 | 2.0 | 1616 | 5.7159 |
| 4.7181 | 3.0 | 2424 | 5.2321 |
| 4.4869 | 4.0 | 3232 | 4.9931 |
| 4.4539 | 5.0 | 4040 | 4.9208 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
adityarra07/whisper-medium-gabriel_fold_4
|
adityarra07
| 2024-01-03T03:14:00Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-03T01:21:16Z |
---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-gabriel_fold_4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-gabriel_fold_4
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2132
- Wer: 9.6024
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6975 | 1.0 | 169 | 0.2328 | 24.6539 |
| 0.1201 | 2.0 | 338 | 0.1991 | 10.1031 |
| 0.0441 | 3.0 | 507 | 0.2071 | 9.8969 |
| 0.0173 | 4.0 | 676 | 0.2069 | 9.7202 |
| 0.0056 | 5.0 | 845 | 0.2120 | 9.8675 |
| 0.0018 | 6.0 | 1014 | 0.2132 | 9.6024 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
andresesevilla/zoe_LoRA
|
andresesevilla
| 2024-01-03T03:03:58Z | 1 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-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
| 2024-01-03T03:03:53Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of ZOE person
license: openrail++
---
# SDXL LoRA DreamBooth - andresesevilla/zoe_LoRA
<Gallery />
## Model description
These are andresesevilla/zoe_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of ZOE person to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](andresesevilla/zoe_LoRA/tree/main) them in the Files & versions tab.
|
kanishka/smolm-autoreg-bpe-counterfactual-babylm-aann-prototypical_only-seed_211-1e-3
|
kanishka
| 2024-01-03T02:55:27Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-02T04:25:57Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-prototypical_only-seed_211-1e-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-counterfactual-babylm-prototypical_only-seed_211-1e-3
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3871
- Accuracy: 0.4118
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 211
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6086 | 1.0 | 18593 | 3.7866 | 0.3570 |
| 3.3876 | 2.0 | 37186 | 3.5895 | 0.3814 |
| 3.2566 | 3.0 | 55779 | 3.4826 | 0.3920 |
| 3.1766 | 4.0 | 74372 | 3.4033 | 0.3996 |
| 3.1188 | 5.0 | 92965 | 3.4073 | 0.4015 |
| 3.0796 | 6.0 | 111558 | 3.3821 | 0.4045 |
| 3.0412 | 7.0 | 130151 | 3.3505 | 0.4082 |
| 3.0092 | 8.0 | 148744 | 3.3533 | 0.4078 |
| 2.9817 | 9.0 | 167337 | 3.3516 | 0.4085 |
| 2.9541 | 10.0 | 185930 | 3.3482 | 0.4095 |
| 2.9354 | 11.0 | 204523 | 3.3638 | 0.4100 |
| 2.9126 | 12.0 | 223116 | 3.3272 | 0.4119 |
| 2.8898 | 13.0 | 241709 | 3.3513 | 0.4110 |
| 2.8735 | 14.0 | 260302 | 3.3416 | 0.4124 |
| 2.8536 | 15.0 | 278895 | 3.3536 | 0.4122 |
| 2.8328 | 16.0 | 297488 | 3.3505 | 0.4125 |
| 2.8111 | 17.0 | 316081 | 3.3719 | 0.4116 |
| 2.7953 | 18.0 | 334674 | 3.3815 | 0.4117 |
| 2.7733 | 19.0 | 353267 | 3.3844 | 0.4118 |
| 2.7618 | 20.0 | 371860 | 3.3871 | 0.4118 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.14.1
|
alirzb/S5_M1_fold3_swint_42510044
|
alirzb
| 2024-01-03T02:54:31Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224",
"base_model:finetune:microsoft/swin-base-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-03T01:09:31Z |
---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S5_M1_fold3_swint_42510044
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.999194035865404
---
<!-- 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. -->
# S5_M1_fold3_swint_42510044
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0024
- Accuracy: 0.9992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0448 | 1.0 | 310 | 0.0119 | 0.9966 |
| 0.0077 | 2.0 | 620 | 0.0027 | 0.9994 |
| 0.0005 | 3.0 | 930 | 0.0037 | 0.9988 |
| 0.0001 | 4.0 | 1241 | 0.0017 | 0.9992 |
| 0.0001 | 5.0 | 1550 | 0.0024 | 0.9992 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
|
umm-maybe/AI-image-detector
|
umm-maybe
| 2024-01-03T02:51:55Z | 4,694 | 54 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"vision",
"image-classification",
"license:cc-by-4.0",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-04T17:12:25Z |
---
tags:
- autotrain
- vision
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 7.940487247386902
license: cc-by-4.0
---
*__NOTE__: Unless you are trying to detect imagery generated using older models such as VQGAN+CLIP, please use the [updated version](https://huggingface.co/Organika/sdxl-detector) of this detector instead.*
This model is a proof-of-concept demonstration of using a ViT model to predict whether an artistic image was generated using AI.
It was created in October 2022, and as such, the training data did not include any samples generated by Midjourney 5, SDXL, or DALLE-3. It still may be able to correctly identify samples from these more recent models due to being trained on outputs of their predecessors.
Furthermore the intended scope of this tool is artistic images; that is to say, it is not a deepfake photo detector, and general computer imagery (webcams, screenshots, etc.) may throw it off.
In general, this tool can only serve as one of many potential indicators that an image was AI-generated. Images scoring as very probably artificial (e.g. 90% or higher) could be referred to a human expert for further investigation, if needed.
For more information please see the blog post describing this project at:
https://medium.com/@matthewmaybe/can-an-ai-learn-to-identify-ai-art-545d9d6af226
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1519658722
- CO2 Emissions (in grams): 7.9405
## Validation Metrics
- Loss: 0.163
- Accuracy: 0.942
- Precision: 0.938
- Recall: 0.978
- AUC: 0.980
- F1: 0.958
# License Notice
This work is licensed under a [Creative Commons Attribution-NoDerivatives 4.0 International License](https://creativecommons.org/licenses/by-nd/4.0/).
You may distribute and make this model available to others as part of your own web page, app, or service so long as you provide attribution. However, use of this model within text-to-image systems to evade AI image detection would be considered a "derivative work" and as such prohibited by the license terms.
|
kanishka/smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-seed_211-3e-4
|
kanishka
| 2024-01-03T02:51:28Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-02T04:24:26Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-no_prototypical-seed_211-3e-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-autoreg-bpe-counterfactual-babylm-no_prototypical-seed_211-3e-4
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4074
- Accuracy: 0.4086
## 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: 32
- eval_batch_size: 64
- seed: 211
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7439 | 1.0 | 18593 | 3.9121 | 0.3459 |
| 3.438 | 2.0 | 37186 | 3.6178 | 0.3756 |
| 3.2947 | 3.0 | 55779 | 3.4715 | 0.3901 |
| 3.2076 | 4.0 | 74372 | 3.4140 | 0.3965 |
| 3.1477 | 5.0 | 92965 | 3.3983 | 0.3996 |
| 3.1015 | 6.0 | 111558 | 3.3692 | 0.4021 |
| 3.0662 | 7.0 | 130151 | 3.3772 | 0.4036 |
| 3.0315 | 8.0 | 148744 | 3.3735 | 0.4036 |
| 3.0003 | 9.0 | 167337 | 3.3651 | 0.4057 |
| 2.9732 | 10.0 | 185930 | 3.3708 | 0.4063 |
| 2.9496 | 11.0 | 204523 | 3.3636 | 0.4073 |
| 2.9243 | 12.0 | 223116 | 3.3660 | 0.4085 |
| 2.9041 | 13.0 | 241709 | 3.3552 | 0.4089 |
| 2.8866 | 14.0 | 260302 | 3.3649 | 0.4087 |
| 2.8654 | 15.0 | 278895 | 3.3720 | 0.4086 |
| 2.846 | 16.0 | 297488 | 3.3842 | 0.4086 |
| 2.8252 | 17.0 | 316081 | 3.3945 | 0.4084 |
| 2.8084 | 18.0 | 334674 | 3.4002 | 0.4086 |
| 2.7871 | 19.0 | 353267 | 3.3996 | 0.4087 |
| 2.7718 | 20.0 | 371860 | 3.4074 | 0.4086 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.14.1
|
alirzb/S5_M1_fold2_swint_42510043
|
alirzb
| 2024-01-03T02:48:59Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224",
"base_model:finetune:microsoft/swin-base-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-03T00:53:27Z |
---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S5_M1_fold2_swint_42510043
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.998589562764457
---
<!-- 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. -->
# S5_M1_fold2_swint_42510043
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0066
- Accuracy: 0.9986
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0158 | 1.0 | 310 | 0.0185 | 0.9950 |
| 0.001 | 2.0 | 620 | 0.0113 | 0.9968 |
| 0.0001 | 3.0 | 930 | 0.0057 | 0.9986 |
| 0.0004 | 4.0 | 1241 | 0.0077 | 0.9988 |
| 0.0065 | 5.0 | 1550 | 0.0066 | 0.9986 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
|
xnscdev/taxi-v3
|
xnscdev
| 2024-01-03T02:45:49Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-03T02:45:44Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.63
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="xnscdev/taxi-v3", 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"])
```
|
AlfredBink/bart-cnn-samsum-peft-trained
|
AlfredBink
| 2024-01-03T02:45:42Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"base_model:finetune:facebook/bart-large-cnn",
"license:mit",
"region:us"
] | null | 2024-01-03T02:04:32Z |
---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart-cnn-samsum-peft-trained
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-samsum-peft-trained
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0653
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.917 | 1.0 | 100 | 3.4752 |
| 2.7459 | 2.0 | 200 | 2.3807 |
| 0.6179 | 3.0 | 300 | 0.4225 |
| 0.086 | 4.0 | 400 | 0.0840 |
| 0.0725 | 5.0 | 500 | 0.0653 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
xnscdev/q-FrozenLake-v1-4x4-noSlippery
|
xnscdev
| 2024-01-03T02:43:25Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-03T02:43:20Z |
---
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="xnscdev/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"])
```
|
Chat-Error/Kimiko-10.7B-v3
|
Chat-Error
| 2024-01-03T02:40:11Z | 194 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:nRuaif/Kimiko_v3-v0.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-02T23:13:17Z |
---
datasets:
- nRuaif/Kimiko_v3-v0.1
---
Experiment model train with my new data.
prompt format is Alpaca, you can add (length:tiny), (length:long) to the end of ### Response: to control leng
|
yupengchen/Reinforce-cartpole
|
yupengchen
| 2024-01-03T02:37:21Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-03T02:37:09Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
LoneStriker/tora-70b-v1.0-2.4bpw-h6-exl2
|
LoneStriker
| 2024-01-03T02:36:16Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"math",
"en",
"dataset:gsm8k",
"dataset:competition_math",
"arxiv:2309.17452",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-03T02:27:41Z |
---
license: llama2
datasets:
- gsm8k
- competition_math
language:
- en
metrics:
- exact_match
library_name: transformers
pipeline_tag: text-generation
tags:
- code
- math
---
<h1 align="center">
ToRA: A Tool-Integrated Reasoning Agent <br> for Mathematical Problem Solving
</h1>
<p align="center">
<a href="https://microsoft.github.io/ToRA/"><b>[🌐 Website]</b></a> •
<a href="https://arxiv.org/abs/2309.17452"><b>[📜 Paper]</b></a> •
<a href="https://huggingface.co/llm-agents"><b>[🤗 HF Models]</b></a> •
<a href="https://github.com/microsoft/ToRA"><b>[🐱 GitHub]</b></a>
<br>
<a href="https://twitter.com/zhs05232838/status/1708860992631763092"><b>[🐦 Twitter]</b></a> •
<a href="https://www.reddit.com/r/LocalLLaMA/comments/1703k6d/tora_a_toolintegrated_reasoning_agent_for/"><b>[💬 Reddit]</b></a> •
<a href="https://notes.aimodels.fyi/researchers-announce-tora-training-language-models-to-better-understand-math-using-external-tools/">[🍀 Unofficial Blog]</a>
<!-- <a href="#-quick-start">Quick Start</a> • -->
<!-- <a href="#%EF%B8%8F-citation">Citation</a> -->
</p>
<p align="center">
Repo for "<a href="https://arxiv.org/abs/2309.17452" target="_blank">ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving</a>"
</p>
## 🔥 News
- [2023/10/08] 🔥🔥🔥 All ToRA models released at [HuggingFace](https://huggingface.co/llm-agents)!!!
- [2023/09/29] ToRA paper, repo, and website released.
## 💡 Introduction
ToRA is a series of Tool-integrated Reasoning Agents designed to solve challenging mathematical reasoning problems by interacting with tools, e.g., computation libraries and symbolic solvers. ToRA series seamlessly integrate natural language reasoning with the utilization of external tools, thereby amalgamating the analytical prowess of language and the computational efficiency of external tools.
| Model | Size | GSM8k | MATH | AVG@10 math tasks<sup>†</sup> |
|---|---|---|---|---|
| GPT-4 | - | 92.0 | 42.5 | 78.3 |
| GPT-4 (PAL) | - | 94.2 | 51.8 | 86.4 |
| [ToRA-7B](https://huggingface.co/llm-agents/tora-7b-v1.0) | 7B | 68.8 | 40.1 | 62.4|
| [ToRA-Code-7B](https://huggingface.co/llm-agents/tora-code-7b-v1.0) | 7B | 72.6 | 44.6 | 66.5|
| [ToRA-13B](https://huggingface.co/llm-agents/tora-13b-v1.0) | 13B | 72.7 | 43.0 | 65.9|
| [ToRA-Code-13B](https://huggingface.co/llm-agents/tora-code-13b-v1.0) | 13B | 75.8 | 48.1 | 71.3 |
| [ToRA-Code-34B<sup>*</sup>](https://huggingface.co/llm-agents/tora-code-34b-v1.0) | 34B | 80.7 | **51.0** | 74.8 |
| [ToRA-70B](https://huggingface.co/llm-agents/tora-70b-v1.0) | 70B | **84.3** | 49.7 | **76.9** |
- <sup>*</sup>ToRA-Code-34B is currently the first and only open-source model to achieve over 50% accuracy (pass@1) on the MATH dataset, which significantly outperforms GPT-4’s CoT result (51.0 vs. 42.5), and is competitive with GPT-4 solving problems with programs. By open-sourcing our codes and models, we hope more breakthroughs will come!
- <sup>†</sup>10 math tasks include GSM8k, MATH, GSM-Hard, SVAMP, TabMWP, ASDiv, SingleEQ, SingleOP, AddSub, and MultiArith.
## ⚡️ Training
The models are trained on ToRA-Corpus 16k, which contains tool-integrated reasoning trajectories of MATH and GSM8k from GPT-4.
We use imitation learning (i.e., SFT) to fine-tune the models, and then apply our proposed *output space shaping* to improve tool-integrated reasoning behaviors. Please refer to the [paper](https://arxiv.org/pdf/2309.17452.pdf) for more details.
## 🪁 Inference & Evaluation
Please refer to ToRA's [GitHub repo](https://github.com/microsoft/ToRA) for inference, evaluation, and training code.
## ☕️ Citation
If you find this repository helpful, please consider citing our paper:
```
@misc{gou2023tora,
title={ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving},
author={Zhibin Gou and Zhihong Shao and Yeyun Gong and yelong shen and Yujiu Yang and Minlie Huang and Nan Duan and Weizhu Chen},
year={2023},
eprint={2309.17452},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
alirzb/S5_M1_fold1_swint_42510042
|
alirzb
| 2024-01-03T02:27:19Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224",
"base_model:finetune:microsoft/swin-base-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-03T00:33:51Z |
---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S5_M1_fold1_swint_42510042
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.999194035865404
---
<!-- 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. -->
# S5_M1_fold1_swint_42510042
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0038
- Accuracy: 0.9992
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0406 | 1.0 | 310 | 0.0068 | 0.9978 |
| 0.0007 | 2.0 | 620 | 0.0046 | 0.9986 |
| 0.0003 | 3.0 | 930 | 0.0036 | 0.9990 |
| 0.0001 | 4.0 | 1241 | 0.0025 | 0.9994 |
| 0.0001 | 5.0 | 1550 | 0.0038 | 0.9992 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
|
TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-AWQ
|
TheBloke
| 2024-01-03T02:25:07Z | 9 | 3 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"base_model:OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k",
"base_model:quantized:OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2024-01-03T00:27:29Z |
---
base_model: OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k
inference: false
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
library_name: transformers
license: apache-2.0
model_creator: OpenBuddy
model_name: Openbuddy Mixtral 7Bx8 V16.3 32K
model_type: mixtral
pipeline_tag: text-generation
prompt_template: "You are a helpful, respectful and honest INTP-T AI Assistant named\
\ Buddy. You are talking to a human User.\nAlways answer as helpfully and logically\
\ as possible, while being safe. Your answers should not include any harmful, political,\
\ religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please\
\ ensure that your responses are socially unbiased and positive in nature.\nIf a\
\ question does not make any sense, or is not factually coherent, explain why instead\
\ of answering something not correct. If you don't know the answer to a question,\
\ please don't share false information.\nYou like to use emojis. You can speak fluently\
\ in many languages, for example: English, Chinese.\nYou cannot access the internet,\
\ but you have vast knowledge, cutoff: 2021-09.\nYou are trained by OpenBuddy team,\
\ (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based\
\ on LLaMA and Falcon transformers model, not related to GPT or OpenAI.\n\nUser:\
\ {prompt}\nAssistant: \n"
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Openbuddy Mixtral 7Bx8 V16.3 32K - AWQ
- Model creator: [OpenBuddy](https://huggingface.co/OpenBuddy)
- Original model: [Openbuddy Mixtral 7Bx8 V16.3 32K](https://huggingface.co/OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k)
<!-- description start -->
## Description
This repo contains AWQ model files for [OpenBuddy's Openbuddy Mixtral 7Bx8 V16.3 32K](https://huggingface.co/OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
**MIXTRAL AWQ**
This is a Mixtral AWQ model.
For AutoAWQ inference, please install AutoAWQ 0.1.8 or later.
Support via Transformers is also available, but currently requires installing Transformers from Github: `pip3 install git+https://github.com/huggingface/transformers.git`
vLLM: version 0.2.6 is confirmed to support Mixtral AWQs.
TGI: I tested version 1.3.3 and it loaded the model fine, but I was not able to get any output back. Further testing/debug is required. (Let me know if you get it working!)
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
AWQ models are supported by (note that not all of these may support Mixtral models yet - see above):
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF)
* [OpenBuddy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: OpenBuddy
```
You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {prompt}
Assistant:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 24.73 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `openbuddy-mixtral-7bx8-v16.3-32k-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {prompt}
Assistant:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {prompt}
Assistant:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {prompt}
Assistant:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: OpenBuddy's Openbuddy Mixtral 7Bx8 V16.3 32K
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
xiaol/MyPdfChat-RWKV
|
xiaol
| 2024-01-03T02:24:09Z | 0 | 0 | null |
[
"en",
"zh",
"de",
"fr",
"ar",
"pl",
"ja",
"ko",
"license:apache-2.0",
"region:us"
] | null | 2024-01-03T02:21:44Z |
---
license: apache-2.0
language:
- en
- zh
- de
- fr
- ar
- pl
- ja
- ko
---
Copy from this [model card](https://huggingface.co/MyPdfChat/MyPdfChat)
# MyPdfChat - Private PDF Chat based on LLM can run on any PC.
**MyPdfChat** is using a private 7B RWKV language model designed to run locally and facilitate secure PDF-based chat conversations. With RWKV, you can have confidential and encrypted conversations in PDF format, ensuring the privacy of your discussions.
## Features
- **Privacy**: MyPdfChat runs locally on your machine, ensuring that your conversations remain private and secure.
- **PDF Chat**: MyPdfChat enables you to have chat conversations within PDF documents, providing a unique and secure communication method.
- **Encryption**: All chat messages are encrypted to protect the confidentiality of your discussions.
- **Offline Access**: Since RWKV runs locally, you can use it even without an internet connection.
## Installation
- To install MyPdfChat from the release, follow these instructions:
- ### Step 1:
- Download the Release1. Go to the [Mychatpdf huggingface repo](https://huggingface.co/MyPdfChat/MyPdfChat).2. Download the latest release zip (`mychatpdf-vX.X.X.zip`).
- ### Step 2:
- Extract the Release1. Locate the downloaded `mychatpdf-vX.X.X.zip` file on your system.2. Extract the contents of the zip file to a directory of your choice.
- ### Step 3:
- double click the MyPdfchat.exe
## Usage1.
- chat with you PDF file
## Contributions
- Contributions to MyPdfChat are welcome! If you encounter any issues or have suggestions for improvements, please open an issue on the [GitHub repository](https://github.com/mypdfchat/MypdfChat).
## License
- MyPdfChat is released under the [MIT License](https://opensource.org/licenses/MIT).
## Acknowledgements
- We would like to thank the open-source community for their invaluable contributions to the development of MyPdfChat.
## Contact
- For any inquiries or support, please contact us at [email protected]---Thank you for using RWKV! We hope you enjoy your private and secure PDF chat experience.
|
TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF
|
TheBloke
| 2024-01-03T02:24:00Z | 105 | 12 |
transformers
|
[
"transformers",
"gguf",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"base_model:OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k",
"base_model:quantized:OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-01-02T23:00:23Z |
---
base_model: OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k
inference: false
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
library_name: transformers
license: apache-2.0
model_creator: OpenBuddy
model_name: Openbuddy Mixtral 7Bx8 V16.3 32K
model_type: mixtral
pipeline_tag: text-generation
prompt_template: "You are a helpful, respectful and honest INTP-T AI Assistant named\
\ Buddy. You are talking to a human User.\nAlways answer as helpfully and logically\
\ as possible, while being safe. Your answers should not include any harmful, political,\
\ religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please\
\ ensure that your responses are socially unbiased and positive in nature.\nIf a\
\ question does not make any sense, or is not factually coherent, explain why instead\
\ of answering something not correct. If you don't know the answer to a question,\
\ please don't share false information.\nYou like to use emojis. You can speak fluently\
\ in many languages, for example: English, Chinese.\nYou cannot access the internet,\
\ but you have vast knowledge, cutoff: 2021-09.\nYou are trained by OpenBuddy team,\
\ (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based\
\ on LLaMA and Falcon transformers model, not related to GPT or OpenAI.\n\nUser:\
\ {prompt}\nAssistant: \n"
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Openbuddy Mixtral 7Bx8 V16.3 32K - GGUF
- Model creator: [OpenBuddy](https://huggingface.co/OpenBuddy)
- Original model: [Openbuddy Mixtral 7Bx8 V16.3 32K](https://huggingface.co/OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k)
<!-- description start -->
## Description
This repo contains GGUF format model files for [OpenBuddy's Openbuddy Mixtral 7Bx8 V16.3 32K](https://huggingface.co/OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF)
* [OpenBuddy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenBuddy/openbuddy-mixtral-7bx8-v16.3-32k)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: OpenBuddy
```
You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.
Always answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
You like to use emojis. You can speak fluently in many languages, for example: English, Chinese.
You cannot access the internet, but you have vast knowledge, cutoff: 2021-09.
You are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.
User: {prompt}
Assistant:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [openbuddy-mixtral-7bx8-v16.3-32k.Q2_K.gguf](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF/blob/main/openbuddy-mixtral-7bx8-v16.3-32k.Q2_K.gguf) | Q2_K | 2 | 15.67 GB| 18.17 GB | smallest, significant quality loss - not recommended for most purposes |
| [openbuddy-mixtral-7bx8-v16.3-32k.Q3_K_M.gguf](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF/blob/main/openbuddy-mixtral-7bx8-v16.3-32k.Q3_K_M.gguf) | Q3_K_M | 3 | 20.39 GB| 22.89 GB | very small, high quality loss |
| [openbuddy-mixtral-7bx8-v16.3-32k.Q4_0.gguf](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF/blob/main/openbuddy-mixtral-7bx8-v16.3-32k.Q4_0.gguf) | Q4_0 | 4 | 26.47 GB| 28.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [openbuddy-mixtral-7bx8-v16.3-32k.Q4_K_M.gguf](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF/blob/main/openbuddy-mixtral-7bx8-v16.3-32k.Q4_K_M.gguf) | Q4_K_M | 4 | 26.47 GB| 28.97 GB | medium, balanced quality - recommended |
| [openbuddy-mixtral-7bx8-v16.3-32k.Q5_0.gguf](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF/blob/main/openbuddy-mixtral-7bx8-v16.3-32k.Q5_0.gguf) | Q5_0 | 5 | 32.26 GB| 34.76 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [openbuddy-mixtral-7bx8-v16.3-32k.Q5_K_M.gguf](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF/blob/main/openbuddy-mixtral-7bx8-v16.3-32k.Q5_K_M.gguf) | Q5_K_M | 5 | 32.26 GB| 34.76 GB | large, very low quality loss - recommended |
| [openbuddy-mixtral-7bx8-v16.3-32k.Q6_K.gguf](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF/blob/main/openbuddy-mixtral-7bx8-v16.3-32k.Q6_K.gguf) | Q6_K | 6 | 38.41 GB| 40.91 GB | very large, extremely low quality loss |
| [openbuddy-mixtral-7bx8-v16.3-32k.Q8_0.gguf](https://huggingface.co/TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF/blob/main/openbuddy-mixtral-7bx8-v16.3-32k.Q8_0.gguf) | Q8_0 | 8 | 49.67 GB| 52.17 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF and below it, a specific filename to download, such as: openbuddy-mixtral-7bx8-v16.3-32k.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF openbuddy-mixtral-7bx8-v16.3-32k.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/openbuddy-mixtral-7bx8-v16.3-32k-GGUF openbuddy-mixtral-7bx8-v16.3-32k.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m openbuddy-mixtral-7bx8-v16.3-32k.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.\nAlways answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\nYou like to use emojis. You can speak fluently in many languages, for example: English, Chinese.\nYou cannot access the internet, but you have vast knowledge, cutoff: 2021-09.\nYou are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.\n\nUser: {prompt}\nAssistant:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./openbuddy-mixtral-7bx8-v16.3-32k.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"You are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human User.\nAlways answer as helpfully and logically as possible, while being safe. Your answers should not include any harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\nYou like to use emojis. You can speak fluently in many languages, for example: English, Chinese.\nYou cannot access the internet, but you have vast knowledge, cutoff: 2021-09.\nYou are trained by OpenBuddy team, (https://openbuddy.ai, https://github.com/OpenBuddy/OpenBuddy), you are based on LLaMA and Falcon transformers model, not related to GPT or OpenAI.\n\nUser: {prompt}\nAssistant:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./openbuddy-mixtral-7bx8-v16.3-32k.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: OpenBuddy's Openbuddy Mixtral 7Bx8 V16.3 32K
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
<!-- original-model-card end -->
|
LoicSteve/Reinforce-CartPole-v1
|
LoicSteve
| 2024-01-03T02:23:31Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-03T02:06:39Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 490.90 +/- 27.30
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Obrolin/Kesehatan-7B-v0.1
|
Obrolin
| 2024-01-03T02:20:32Z | 219 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"medical",
"conversational",
"id",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-31T16:31:43Z |
---
license: cc-by-nc-4.0
language:
- id
pipeline_tag: text-generation
tags:
- medical
---
## Obrolin Kesehatan!
Sesuai dengan namanya, Kesehatan! model AI ini telah dilatih dengan berbagai dataset di bidang kesehatan dalam Bahasa Indonesia seperti penyakit, obat-obatan, dan lain lain yang berhubungan dengan kesehatan!
**Meskipun "Obrolin Kesehatan" dirancang untuk memberikan informasi kesehatan yang bermanfaat, perlu diingat bahwa jawaban yang dihasilkan oleh model ini tidak selalu akurat dan tidak dapat menggantikan konsultasi langsung dengan dokter**
Anggap temen ngobrol aja ya :)
---
*As the name suggests, Health! This AI model has been drilled with various datasets in the health sector in Bahasa Indonesia such as diseases, medicines, and others related to health!*
***Although "Obrolin Kesehatan" is designed to provide useful health information, please remember that the answers generated by this model are not always accurate and cannot replace direct consultation with a doctor***
Just think of it as friends, okay? :)
## System Prompt (Optional) :
```
Kamu adalah Obrolin, asisten AI yang memiliki pengetahuan di bidang kesehatan
```
## Output Example :
*SillyTavern default settings, Q8_0.GGUF*
## Still in alpha build, don't expect perfection just yet :)
## License
This model is made available under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/), which allows anyone to share and adapt the material for non-commercial purposes, with appropriate attribution.
## Based on [azale-ai/Starstreak-7b-beta](https://huggingface.co/azale-ai/Starstreak-7b-beta)!
```
@software{Hafidh_Soekma_Startstreak_7b_beta_2023,
author = {Hafidh Soekma Ardiansyah},
month = october,
title = {Startstreak: Traditional Indonesian Multilingual Language Model},
url = {\url{https://huggingface.co/azale-ai/Starstreak-7b-beta}},
publisher = {HuggingFace},
journal = {HuggingFace Models},
version = {1.0},
year = {2023}
}
```
## Citation
```
@misc{Obrolin/Kesehatan-7B,
author = {Arkan Bima},
title = {Obrolin Kesehatan},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Obrolin/Kesehatan-7B}},
version = {0.1},
year = {2024},
}
```
|
Berkem/finetune_deepspeed_deepseek
|
Berkem
| 2024-01-03T02:06:05Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"base_model:finetune:deepseek-ai/deepseek-coder-6.7b-instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-02T12:34:03Z |
---
license: other
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
tags:
- generated_from_trainer
model-index:
- name: finetune_deepspeed_deepseek
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. -->
# finetune_deepspeed_deepseek
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2286
## 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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1482 | 1.0 | 1559 | 0.2420 |
| 0.0969 | 2.0 | 3118 | 0.2178 |
| 0.0761 | 3.0 | 4677 | 0.1981 |
| 0.0561 | 4.0 | 6236 | 0.1966 |
| 0.0469 | 5.0 | 7795 | 0.1977 |
| 0.0401 | 6.0 | 9354 | 0.1979 |
| 0.032 | 7.0 | 10913 | 0.2009 |
| 0.028 | 8.0 | 12472 | 0.2091 |
| 0.0254 | 9.0 | 14031 | 0.2252 |
| 0.0275 | 10.0 | 15590 | 0.2286 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
BWangila/q-FrozenLake-v1-4x4-noSlippery
|
BWangila
| 2024-01-03T02:06:03Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-03T02:05:59Z |
---
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="BWangila/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"])
```
|
tiagoblima/t5_base-qg-aas
|
tiagoblima
| 2024-01-03T02:04:32Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"dataset:tiagoblima/qg_squad_v1_pt",
"base_model:unicamp-dl/ptt5-base-t5-vocab",
"base_model:finetune:unicamp-dl/ptt5-base-t5-vocab",
"license:mit",
"region:us"
] | null | 2024-01-02T23:16:03Z |
---
license: mit
base_model: unicamp-dl/ptt5-base-t5-vocab
tags:
- generated_from_trainer
datasets:
- tiagoblima/qg_squad_v1_pt
model-index:
- name: t5_base-qg-aas
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. -->
# t5_base-qg-aas
This model is a fine-tuned version of [unicamp-dl/ptt5-base-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-base-t5-vocab) on the tiagoblima/qg_squad_v1_pt dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5207
## 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: 0.005
- train_batch_size: 64
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.307 | 1.0 | 808 | 7.2623 |
| 5.5213 | 2.0 | 1616 | 6.3641 |
| 5.1108 | 3.0 | 2424 | 5.8625 |
| 4.8497 | 4.0 | 3232 | 5.6018 |
| 4.8246 | 5.0 | 4040 | 5.5207 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
ntc-ai/SDXL-LoRA-slider.crowd-of-people
|
ntc-ai
| 2024-01-03T02:02:14Z | 21 | 2 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2024-01-03T02:02:11Z |
---
language:
- en
thumbnail: "images/evaluate/crowd of people.../crowd of people_17_3.0.png"
widget:
- text: crowd of people
output:
url: images/crowd of people_17_3.0.png
- text: crowd of people
output:
url: images/crowd of people_19_3.0.png
- text: crowd of people
output:
url: images/crowd of people_20_3.0.png
- text: crowd of people
output:
url: images/crowd of people_21_3.0.png
- text: crowd of people
output:
url: images/crowd of people_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "crowd of people"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - crowd of people (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/crowd of people_17_-3.0.png" width=256 height=256 /> | <img src="images/crowd of people_17_0.0.png" width=256 height=256 /> | <img src="images/crowd of people_17_3.0.png" width=256 height=256 /> |
| <img src="images/crowd of people_19_-3.0.png" width=256 height=256 /> | <img src="images/crowd of people_19_0.0.png" width=256 height=256 /> | <img src="images/crowd of people_19_3.0.png" width=256 height=256 /> |
| <img src="images/crowd of people_20_-3.0.png" width=256 height=256 /> | <img src="images/crowd of people_20_0.0.png" width=256 height=256 /> | <img src="images/crowd of people_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
crowd of people
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.crowd-of-people', weight_name='crowd of people.safetensors', adapter_name="crowd of people")
# Activate the LoRA
pipe.set_adapters(["crowd of people"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, crowd of people"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 820+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
aaa12963337/msi-nat-mini-pretrain
|
aaa12963337
| 2024-01-03T01:47:13Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"nat",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:shi-labs/nat-mini-in1k-224",
"base_model:finetune:shi-labs/nat-mini-in1k-224",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-14T10:51:48Z |
---
license: mit
base_model: shi-labs/nat-mini-in1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: msi-nat-mini-pretrain
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8704735376044568
---
<!-- 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. -->
# msi-nat-mini-pretrain
This model is a fine-tuned version of [shi-labs/nat-mini-in1k-224](https://huggingface.co/shi-labs/nat-mini-in1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6286
- Accuracy: 0.8705
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1151 | 1.0 | 1562 | 0.2480 | 0.9242 |
| 0.0453 | 2.0 | 3125 | 0.5128 | 0.8816 |
| 0.0466 | 3.0 | 4686 | 0.6286 | 0.8705 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
iamdanialkamali/Reinforce-1
|
iamdanialkamali
| 2024-01-03T01:23:54Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-03T01:08:49Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 18.00 +/- 5.27
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
andrew-ye/rl_course_vizdoom_health_gathering_supreme
|
andrew-ye
| 2024-01-03T01:19:39Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-03T01:19:24Z |
---
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_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.46 +/- 5.85
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** 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 andrew-ye/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
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 .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --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.
|
adityarra07/whisper-medium-gabriel_fold_3
|
adityarra07
| 2024-01-03T01:14:56Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-02T23:23:54Z |
---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-gabriel_fold_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-gabriel_fold_3
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2630
- Wer: 9.1024
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6922 | 1.0 | 169 | 0.2475 | 20.0444 |
| 0.1187 | 2.0 | 338 | 0.2261 | 9.3879 |
| 0.0417 | 3.0 | 507 | 0.2316 | 9.1976 |
| 0.0152 | 4.0 | 676 | 0.2547 | 10.0856 |
| 0.0046 | 5.0 | 845 | 0.2599 | 8.9439 |
| 0.0018 | 6.0 | 1014 | 0.2630 | 9.1024 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
alirzb/S5_M1_fold2_deit_42510038
|
alirzb
| 2024-01-03T01:06:05Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-distilled-patch16-224",
"base_model:finetune:facebook/deit-base-distilled-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-02T23:23:39Z |
---
license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S5_M1_fold2_deit_42510038
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.998388071730808
---
<!-- 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. -->
# S5_M1_fold2_deit_42510038
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0084
- Accuracy: 0.9984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0038 | 1.0 | 310 | 0.0085 | 0.9980 |
| 0.0104 | 2.0 | 620 | 0.0051 | 0.9980 |
| 0.0016 | 3.0 | 930 | 0.0107 | 0.9984 |
| 0.0001 | 4.0 | 1241 | 0.0067 | 0.9988 |
| 0.0 | 5.0 | 1550 | 0.0084 | 0.9984 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
|
adlumal/auslaw-embed-v1.0
|
adlumal
| 2024-01-03T00:51:55Z | 10 | 7 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"law",
"australia",
"legal",
"auslaw",
"en",
"dataset:umarbutler/open-australian-legal-corpus",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-01-02T23:27:49Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- law
- australia
- legal
- auslaw
license: apache-2.0
datasets:
- umarbutler/open-australian-legal-corpus
language:
- en
---
# AusLaw Embedding Model v1.0
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model is a fine-tune of [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) using the HCA case law in the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus) by Umar Butler. The PDF/OCR cases were not used.
The cases were split into < 512 context chunks using the bge-small-en tokeniser and [semchunk](https://github.com/umarbutler/semchunk).
[mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) was used to generate a legal question for each context chunk.
129,137 context-question pairs were used for training.
14,348 context-question pairs were used for evaluation (see the table below for results).
Using a 10% subset of the val dataset the following hit-rate performance was reached and is compared to the base model and OpenAI's default ada embedding model.
| **Model** | **Avg. hit-rate** |
|---------------------------|-------------------|
| BAAI/bge-small-en | 89% |
| OpenAI | 92% |
| adlumal/auslaw-embed-v1.0 | **97%** |
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('adlumal/auslaw-embed-v1.0')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
The model was evauluated on 10% of the available data. The automated eval results for the final step are presented below.
| Eval | Score |
|------------------------|--------------|
| cos_sim-Accuracy@1 | 0.730206301 |
| cos_sim-Accuracy@3 | 0.859562308 |
| cos_sim-Accuracy@5 | 0.892737664 |
| cos_sim-Accuracy@10 | 0.928352384 |
| cos_sim-Precision@1 | 0.730206301 |
| cos_sim-Recall@1 | 0.730206301 |
| cos_sim-Precision@3 | 0.286520769 |
| cos_sim-Recall@3 | 0.859562308 |
| cos_sim-Precision@5 | 0.178547533 |
| cos_sim-Recall@5 | 0.892737664 |
| cos_sim-Precision@10 | 0.092835238 |
| cos_sim-Recall@10 | 0.928352384 |
| cos_sim-MRR@10 | 0.801075782 |
| cos_sim-NDCG@10 | 0.832189447 |
| cos_sim-MAP@100 | 0.803593645 |
| dot_score-Accuracy@1 | 0.730136604 |
| dot_score-Accuracy@3 | 0.859562308 |
| dot_score-Accuracy@5 | 0.892737664 |
| dot_score-Accuracy@10 | 0.928352384 |
| dot_score-Precision@1 | 0.730136604 |
| dot_score-Recall@1 | 0.730136604 |
| dot_score-Precision@3 | 0.286520769 |
| dot_score-Recall@3 | 0.859562308 |
| dot_score-Precision@5 | 0.178547533 |
| dot_score-Recall@5 | 0.892737664 |
| dot_score-Precision@10 | 0.092835238 |
| dot_score-Recall@10 | 0.928352384 |
| dot_score-MRR@10 | 0.801040934 |
| dot_score-NDCG@10 | 0.832163724 |
| dot_score-MAP@100 | 0.803558796 |
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2583 with parameters:
```
{'batch_size': 50, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 516,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
```bibtex
@misc{malec-2024-auslaw-embed-v1,
author = {Malec, Adrian Lucas},
year = {2024},
title = {AusLaw Embedding v1.0},
publisher = {Hugging Face},
version = {1.0},
url = {https://huggingface.co/adlumal/auslaw-embed-v1.0}
}
```
|
alirzb/S2_M1_R3_swint_42509601
|
alirzb
| 2024-01-03T00:30:09Z | 28 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224",
"base_model:finetune:microsoft/swin-base-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-02T22:52:01Z |
---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S2_M1_R3_swint_42509601
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9995166747220879
---
<!-- 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. -->
# S2_M1_R3_swint_42509601
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0038
- Accuracy: 0.9995
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0242 | 1.0 | 258 | 0.0034 | 0.9993 |
| 0.0004 | 2.0 | 517 | 0.0029 | 0.9995 |
| 0.0001 | 3.0 | 776 | 0.0054 | 0.9990 |
| 0.0001 | 4.0 | 1035 | 0.0048 | 0.9990 |
| 0.0001 | 4.99 | 1290 | 0.0038 | 0.9995 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
|
nrshoudi/hubert-large-ls960-ft-V2-5
|
nrshoudi
| 2024-01-03T00:08:52Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/hubert-large-ls960-ft",
"base_model:finetune:facebook/hubert-large-ls960-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-02T23:00:00Z |
---
license: apache-2.0
base_model: facebook/hubert-large-ls960-ft
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-large-ls960-ft-V2-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert-large-ls960-ft-V2-5
This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5760
- Wer: 0.1085
- Per: 0.0892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Per |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 19.707 | 1.0 | 82 | 3.5254 | 1.0 | 1.0 |
| 3.4906 | 2.0 | 164 | 3.2483 | 1.0 | 1.0 |
| 3.233 | 3.0 | 246 | 3.1368 | 1.0 | 1.0 |
| 3.0468 | 4.0 | 328 | 2.9600 | 1.0 | 1.0 |
| 2.6751 | 5.0 | 410 | 2.3348 | 1.0 | 1.0 |
| 2.0881 | 6.0 | 492 | 1.7351 | 0.8568 | 0.8726 |
| 1.4875 | 7.0 | 574 | 1.2264 | 0.6059 | 0.6134 |
| 1.0922 | 8.0 | 656 | 0.9666 | 0.4068 | 0.3972 |
| 0.8148 | 9.0 | 738 | 0.7746 | 0.3249 | 0.3138 |
| 0.6332 | 10.0 | 820 | 0.6755 | 0.2477 | 0.2313 |
| 0.4797 | 11.0 | 902 | 0.6262 | 0.1612 | 0.1410 |
| 0.3807 | 12.0 | 984 | 0.5765 | 0.1384 | 0.1172 |
| 0.3195 | 13.0 | 1066 | 0.5666 | 0.1191 | 0.0992 |
| 0.2526 | 14.0 | 1148 | 0.5759 | 0.1165 | 0.0970 |
| 0.2417 | 15.0 | 1230 | 0.5460 | 0.1138 | 0.0946 |
| 0.2072 | 16.0 | 1312 | 0.5551 | 0.1095 | 0.0912 |
| 0.1881 | 17.0 | 1394 | 0.5745 | 0.1102 | 0.0917 |
| 0.1888 | 18.0 | 1476 | 0.5731 | 0.1094 | 0.0907 |
| 0.202 | 19.0 | 1558 | 0.5774 | 0.1081 | 0.0893 |
| 0.1813 | 20.0 | 1640 | 0.5760 | 0.1085 | 0.0892 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
saludobuenas/test3
|
saludobuenas
| 2024-01-03T00:07:32Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:bigscience/bloom-7b1",
"base_model:adapter:bigscience/bloom-7b1",
"region:us"
] | null | 2023-12-13T06:20:34Z |
---
library_name: peft
base_model: bigscience/bloom-7b1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
TheBloke/Pallas-0.5-LASER-0.6-AWQ
|
TheBloke
| 2024-01-03T00:02:59Z | 8 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:Mihaiii/Pallas-0.5-LASER-0.6",
"base_model:quantized:Mihaiii/Pallas-0.5-LASER-0.6",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2024-01-02T22:49:03Z |
---
base_model: Mihaiii/Pallas-0.5-LASER-0.6
inference: false
license: other
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
license_name: yi-license
metrics:
- accuracy
model_creator: Mihai
model_name: Pallas 0.5 LASER 0.6
model_type: yi
prompt_template: 'SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Pallas 0.5 LASER 0.6 - AWQ
- Model creator: [Mihai](https://huggingface.co/Mihaiii)
- Original model: [Pallas 0.5 LASER 0.6](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.6)
<!-- description start -->
## Description
This repo contains AWQ model files for [Mihai's Pallas 0.5 LASER 0.6](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.6).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF)
* [Mihai's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.6)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Orca-Vicuna
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 19.23 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Pallas-0.5-LASER-0.6-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Pallas-0.5-LASER-0.6-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Pallas-0.5-LASER-0.6-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Pallas-0.5-LASER-0.6-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Pallas-0.5-LASER-0.6-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Pallas-0.5-LASER-0.6-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Mihai's Pallas 0.5 LASER 0.6
This model has a [LASER](https://pratyushasharma.github.io/laser/) intervention on [Mihaiii/Pallas-0.5-LASER-0.5](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.5) .
Configs used:
- lnum: 51
- lnames: mlp (meaning: ["mlp.gate_proj.weight", "mlp.up_proj.weight", "mlp.down_proj.weight"])
- rate: 8
- dataset: bigbench (subset: causal_judgement)
- intervention type: rank-reduction
|Name|Validation acc (higher is better)|Validation logloss (lower is better)|Test acc (higher is better)|Test logloss (lower is better)|
|---|---|---|---|---|
|Pallas-0.5|55.263|1.650|60.526|1.463|
|Pallas-0.5-LASER-0.1|55.263|1.639|61.184|1.451|
|Pallas-0.5-LASER-0.2|55.263|1.646|61.184|1.458|
|Pallas-0.5-LASER-0.3|55.263|1.575|61.842|1.382|
|Pallas-0.5-LASER-0.4|55.263|1.525|61.842|1.326|
|Pallas-0.5-LASER-0.5|55.263|1.484|61.842|1.297|
|Pallas-0.5-LASER-0.6|55.263|1.455|61.184|1.283|
In order to replicate on a single A100, you can use [my branch](https://github.com/Mihaiii/laser/tree/allow-Yi-on-one-A100) (the original code will throw OOM for 34b models).
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
isotnek/Reinforce-pixel_copter
|
isotnek
| 2024-01-03T00:02:57Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-24T18:01:54Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixel_copter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 25.00 +/- 19.99
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Tatterdemalion/Mixtral-8x7B-Instruct-limarp-v0.1-GGUF
|
Tatterdemalion
| 2024-01-02T23:45:30Z | 11 | 1 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-01-02T19:25:36Z |
GGUF quants of https://huggingface.co/Doctor-Shotgun/Mixtral-8x7B-Instruct-limarp-v0.1
|
AlephNull/deep-RL
|
AlephNull
| 2024-01-02T23:41:41Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-25T02:00:22Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO_relu_128
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 274.92 +/- 22.95
name: mean_reward
verified: false
---
# **PPO_relu_128** Agent playing **LunarLander-v2**
This is a trained model of a **PPO_relu_128** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Perselope/carpole
|
Perselope
| 2024-01-02T23:40:59Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-02T23:40:51Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: carpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.72
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Perselope/carpole", 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"])
```
|
Perselope/Lake
|
Perselope
| 2024-01-02T23:35:11Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-02T23:35:04Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Lake
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="Perselope/Lake", 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"])
```
|
LoneStriker/deepseek-llm-67b-Spicy-3.1-1-6.0bpw-h6-exl2
|
LoneStriker
| 2024-01-02T23:15:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"dataset:unalignment/spicy-3.1",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-04T04:55:07Z |
---
license: other
license_name: deepseek
license_link: LICENSE
datasets:
- unalignment/spicy-3.1
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
# Fine-tune of Deepseek 67B
Fine-tuned with jondurbin's unalignment/spicy-3.1 for 1 epoch.
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-base` is a 67B parameter model with Grouped-Query Attention trained on 2 trillion tokens from scratch.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Text Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
|
espnet/voxcelebs12_ecapa_frozen
|
espnet
| 2024-01-02T23:14:09Z | 5 | 0 |
espnet
|
[
"espnet",
"audio",
"speaker-recognition",
"multilingual",
"dataset:voxceleb",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2024-01-02T23:12:57Z |
---
tags:
- espnet
- audio
- speaker-recognition
language: multilingual
datasets:
- voxceleb
license: cc-by-4.0
---
## ESPnet2 SPK model
### `espnet/voxcelebs12_ecapa_frozen`
This model was trained by Jungjee using voxceleb recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout d9646a75807a30afff85a83155247a81cc7fe389
pip install -e .
cd egs2/voxceleb/spk1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/voxcelebs12_ecapa_frozen
```
<!-- Generated by scripts/utils/show_spk_result.py -->
# RESULTS
## Environments
date: 2024-01-02 18:13:10.597501
- python version: 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0]
- espnet version: 202310
- pytorch version: 2.0.1
| | Mean | Std |
|---|---|---|
| Target | 8.0224 | 2.7891 |
| Non-target | 1.9364 | 1.9364 |
| Model name | EER(%) | minDCF |
|---|---|---|
| conf/tuning/train_ecapa_Vox12_emb192_torchmelspec_subcentertopk_wavlm | 0.638 | 0.04994 |
## SPK config
<details><summary>expand</summary>
```
config: conf/tuning/train_ecapa_Vox12_emb192_torchmelspec_subcentertopk_wavlm.yaml
print_config: false
log_level: INFO
drop_last_iter: true
dry_run: false
iterator_type: category
valid_iterator_type: sequence
output_dir: exp/spk_train_ecapa_Vox12_emb192_torchmelspec_subcentertopk_wavlm_raw_sp
ngpu: 1
seed: 0
num_workers: 8
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 37387
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: true
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 40
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- eer
- min
keep_nbest_models: 3
nbest_averaging_interval: 0
grad_clip: 9999
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 100
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 512
valid_batch_size: 40
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/spk_stats_16k_sp/train/speech_shape
valid_shape_file:
- exp/spk_stats_16k_sp/valid/speech_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 120000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
train_data_path_and_name_and_type:
- - dump/raw/voxceleb12_devs_sp/wav.scp
- speech
- sound
- - dump/raw/voxceleb12_devs_sp/utt2spk
- spk_labels
- text
valid_data_path_and_name_and_type:
- - dump/raw/voxceleb1_test/trial.scp
- speech
- sound
- - dump/raw/voxceleb1_test/trial2.scp
- speech2
- sound
- - dump/raw/voxceleb1_test/trial_label
- spk_labels
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.001
weight_decay: 5.0e-05
amsgrad: false
scheduler: cosineannealingwarmuprestarts
scheduler_conf:
first_cycle_steps: 71280
cycle_mult: 1.0
max_lr: 0.001
min_lr: 5.0e-06
warmup_steps: 1000
gamma: 0.75
init: null
use_preprocessor: true
input_size: null
target_duration: 3.0
spk2utt: dump/raw/voxceleb12_devs_sp/spk2utt
spk_num: 21615
sample_rate: 16000
num_eval: 10
rir_scp: ''
model_conf:
extract_feats_in_collect_stats: false
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: wavlm_large
download_dir: ./hub
multilayer_feature: true
specaug: null
specaug_conf: {}
normalize: utterance_mvn
normalize_conf:
norm_vars: false
encoder: ecapa_tdnn
encoder_conf:
model_scale: 8
ndim: 1024
output_size: 1536
pooling: chn_attn_stat
pooling_conf: {}
projector: rawnet3
projector_conf:
output_size: 192
preprocessor: spk
preprocessor_conf:
target_duration: 3.0
sample_rate: 16000
num_eval: 5
noise_apply_prob: 0.5
noise_info:
- - 1.0
- dump/raw/musan_speech.scp
- - 4
- 7
- - 13
- 20
- - 1.0
- dump/raw/musan_noise.scp
- - 1
- 1
- - 0
- 15
- - 1.0
- dump/raw/musan_music.scp
- - 1
- 1
- - 5
- 15
rir_apply_prob: 0.5
rir_scp: dump/raw/rirs.scp
loss: aamsoftmax_sc_topk
loss_conf:
margin: 0.3
scale: 30
K: 3
mp: 0.06
k_top: 5
required:
- output_dir
version: '202308'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
TheBloke/Pallas-0.5-LASER-0.6-GGUF
|
TheBloke
| 2024-01-02T23:10:42Z | 125 | 3 |
transformers
|
[
"transformers",
"gguf",
"yi",
"base_model:Mihaiii/Pallas-0.5-LASER-0.6",
"base_model:quantized:Mihaiii/Pallas-0.5-LASER-0.6",
"license:other",
"region:us"
] | null | 2024-01-02T22:49:03Z |
---
base_model: Mihaiii/Pallas-0.5-LASER-0.6
inference: false
license: other
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
license_name: yi-license
metrics:
- accuracy
model_creator: Mihai
model_name: Pallas 0.5 LASER 0.6
model_type: yi
prompt_template: 'SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Pallas 0.5 LASER 0.6 - GGUF
- Model creator: [Mihai](https://huggingface.co/Mihaiii)
- Original model: [Pallas 0.5 LASER 0.6](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.6)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Mihai's Pallas 0.5 LASER 0.6](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.6).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF)
* [Mihai's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.6)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Orca-Vicuna
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [pallas-0.5-laser-0.6.Q2_K.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q2_K.gguf) | Q2_K | 2 | 14.56 GB| 17.06 GB | smallest, significant quality loss - not recommended for most purposes |
| [pallas-0.5-laser-0.6.Q3_K_S.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q3_K_S.gguf) | Q3_K_S | 3 | 14.96 GB| 17.46 GB | very small, high quality loss |
| [pallas-0.5-laser-0.6.Q3_K_M.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q3_K_M.gguf) | Q3_K_M | 3 | 16.64 GB| 19.14 GB | very small, high quality loss |
| [pallas-0.5-laser-0.6.Q3_K_L.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q3_K_L.gguf) | Q3_K_L | 3 | 18.14 GB| 20.64 GB | small, substantial quality loss |
| [pallas-0.5-laser-0.6.Q4_0.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q4_0.gguf) | Q4_0 | 4 | 19.47 GB| 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [pallas-0.5-laser-0.6.Q4_K_S.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q4_K_S.gguf) | Q4_K_S | 4 | 19.55 GB| 22.05 GB | small, greater quality loss |
| [pallas-0.5-laser-0.6.Q4_K_M.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q4_K_M.gguf) | Q4_K_M | 4 | 20.66 GB| 23.16 GB | medium, balanced quality - recommended |
| [pallas-0.5-laser-0.6.Q5_0.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q5_0.gguf) | Q5_0 | 5 | 23.71 GB| 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [pallas-0.5-laser-0.6.Q5_K_S.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q5_K_S.gguf) | Q5_K_S | 5 | 23.71 GB| 26.21 GB | large, low quality loss - recommended |
| [pallas-0.5-laser-0.6.Q5_K_M.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q5_K_M.gguf) | Q5_K_M | 5 | 24.32 GB| 26.82 GB | large, very low quality loss - recommended |
| [pallas-0.5-laser-0.6.Q6_K.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q6_K.gguf) | Q6_K | 6 | 28.22 GB| 30.72 GB | very large, extremely low quality loss |
| [pallas-0.5-laser-0.6.Q8_0.gguf](https://huggingface.co/TheBloke/Pallas-0.5-LASER-0.6-GGUF/blob/main/pallas-0.5-laser-0.6.Q8_0.gguf) | Q8_0 | 8 | 36.54 GB| 39.04 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Pallas-0.5-LASER-0.6-GGUF and below it, a specific filename to download, such as: pallas-0.5-laser-0.6.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Pallas-0.5-LASER-0.6-GGUF pallas-0.5-laser-0.6.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Pallas-0.5-LASER-0.6-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Pallas-0.5-LASER-0.6-GGUF pallas-0.5-laser-0.6.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m pallas-0.5-laser-0.6.Q4_K_M.gguf --color -c 200000 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "SYSTEM: {system_message}\nUSER: {prompt}\nASSISTANT:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 200000` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./pallas-0.5-laser-0.6.Q4_K_M.gguf", # Download the model file first
n_ctx=200000, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"SYSTEM: {system_message}\nUSER: {prompt}\nASSISTANT:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./pallas-0.5-laser-0.6.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Mihai's Pallas 0.5 LASER 0.6
This model has a [LASER](https://pratyushasharma.github.io/laser/) intervention on [Mihaiii/Pallas-0.5-LASER-0.5](https://huggingface.co/Mihaiii/Pallas-0.5-LASER-0.5) .
Configs used:
- lnum: 51
- lnames: mlp (meaning: ["mlp.gate_proj.weight", "mlp.up_proj.weight", "mlp.down_proj.weight"])
- rate: 8
- dataset: bigbench (subset: causal_judgement)
- intervention type: rank-reduction
|Name|Validation acc (higher is better)|Validation logloss (lower is better)|Test acc (higher is better)|Test logloss (lower is better)|
|---|---|---|---|---|
|Pallas-0.5|55.263|1.650|60.526|1.463|
|Pallas-0.5-LASER-0.1|55.263|1.639|61.184|1.451|
|Pallas-0.5-LASER-0.2|55.263|1.646|61.184|1.458|
|Pallas-0.5-LASER-0.3|55.263|1.575|61.842|1.382|
|Pallas-0.5-LASER-0.4|55.263|1.525|61.842|1.326|
|Pallas-0.5-LASER-0.5|55.263|1.484|61.842|1.297|
|Pallas-0.5-LASER-0.6|55.263|1.455|61.184|1.283|
In order to replicate on a single A100, you can use [my branch](https://github.com/Mihaiii/laser/tree/allow-Yi-on-one-A100) (the original code will throw OOM for 34b models).
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
<!-- original-model-card end -->
|
espnet/voxcelebs12_ska_mel
|
espnet
| 2024-01-02T23:10:38Z | 7 | 0 |
espnet
|
[
"espnet",
"audio",
"speaker-recognition",
"multilingual",
"dataset:voxceleb",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2024-01-02T22:40:51Z |
---
tags:
- espnet
- audio
- speaker-recognition
language: multilingual
datasets:
- voxceleb
license: cc-by-4.0
---
## ESPnet2 SPK model
### `espnet/voxcelebs12_ska_mel`
This model was trained by Jungjee using voxceleb recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout d9646a75807a30afff85a83155247a81cc7fe389
pip install -e .
cd egs2/voxceleb/spk1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/voxcelebs12_ska_mel
```
<!-- Generated by scripts/utils/show_spk_result.py -->
# RESULTS
## Environments
date: 2024-01-02 18:09:41.334841
- python version: 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0]
- espnet version: 202310
- pytorch version: 2.0.1
| | Mean | Std |
|---|---|---|
| Target | 8.1349 | 3.5908 |
| Non-target | 2.3247 | 2.3247 |
| Model name | EER(%) | minDCF |
|---|---|---|
| conf/tuning/train_ska_Vox12_emb192_torchmelspec_subcentertopk | 0.729 | 0.04574 |
## SPK config
<details><summary>expand</summary>
```
config: conf/tuning/train_ska_Vox12_emb192_torchmelspec_subcentertopk.yaml
print_config: false
log_level: INFO
drop_last_iter: true
dry_run: false
iterator_type: category
valid_iterator_type: sequence
output_dir: exp/spk_train_ska_Vox12_emb192_torchmelspec_subcentertopk_raw_sp
ngpu: 1
seed: 0
num_workers: 6
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 34991
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: true
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 40
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- eer
- min
keep_nbest_models: 3
nbest_averaging_interval: 0
grad_clip: 9999
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 100
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_lora: false
save_lora_only: true
lora_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 512
valid_batch_size: 40
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/spk_stats_16k_sp/train/speech_shape
valid_shape_file:
- exp/spk_stats_16k_sp/valid/speech_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 120000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
train_data_path_and_name_and_type:
- - dump/raw/voxceleb12_devs_sp/wav.scp
- speech
- sound
- - dump/raw/voxceleb12_devs_sp/utt2spk
- spk_labels
- text
valid_data_path_and_name_and_type:
- - dump/raw/voxceleb1_test/trial.scp
- speech
- sound
- - dump/raw/voxceleb1_test/trial2.scp
- speech2
- sound
- - dump/raw/voxceleb1_test/trial_label
- spk_labels
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.001
weight_decay: 5.0e-05
amsgrad: false
scheduler: cosineannealingwarmuprestarts
scheduler_conf:
first_cycle_steps: 71280
cycle_mult: 1.0
max_lr: 0.001
min_lr: 5.0e-06
warmup_steps: 1000
gamma: 0.75
init: null
use_preprocessor: true
input_size: null
target_duration: 3.0
spk2utt: dump/raw/voxceleb12_devs_sp/spk2utt
spk_num: 21615
sample_rate: 16000
num_eval: 10
rir_scp: ''
model_conf:
extract_feats_in_collect_stats: false
frontend: melspec_torch
frontend_conf:
preemp: true
n_fft: 512
log: true
win_length: 400
hop_length: 160
n_mels: 80
normalize: mn
specaug: null
specaug_conf: {}
normalize: null
normalize_conf: {}
encoder: ska_tdnn
encoder_conf:
model_scale: 8
ndim: 1024
ska_dim: 128
output_size: 1536
pooling: chn_attn_stat
pooling_conf: {}
projector: ska_tdnn
projector_conf:
output_size: 192
preprocessor: spk
preprocessor_conf:
target_duration: 3.0
sample_rate: 16000
num_eval: 5
noise_apply_prob: 0.5
noise_info:
- - 1.0
- dump/raw/musan_speech.scp
- - 4
- 7
- - 13
- 20
- - 1.0
- dump/raw/musan_noise.scp
- - 1
- 1
- - 0
- 15
- - 1.0
- dump/raw/musan_music.scp
- - 1
- 1
- - 5
- 15
rir_apply_prob: 0.5
rir_scp: dump/raw/rirs.scp
loss: aamsoftmax_sc_topk
loss_conf:
margin: 0.3
scale: 30
K: 3
mp: 0.06
k_top: 5
required:
- output_dir
version: '202310'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
ManBib/faster-whisper-readme
|
ManBib
| 2024-01-02T23:03:50Z | 1 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2023-11-06T11:05:22Z |
# Faster Whisper Transcription Service
## Overview
This project uses the `faster_whisper` Python package to provide an API endpoint for audio transcription. It utilizes
OpenAI's Whisper model (large-v3) for accurate and efficient speech-to-text conversion. The service is designed to be
deployed on Hugging Face endpoints.
## Features
- **Efficient Transcription**: Utilizes the large-v3 Whisper model for high-quality transcription.
- **Multilingual Support**: Supports transcription in various languages, with default language set to German (de).
- **Segmented Output**: Returns transcribed text with segment IDs and timestamps for each transcribed segment.
## Usage
```python
import requests
import os
# Sample data dict with the link to the video file and the desired language for transcription
DATA = {
"inputs": "<base64_encoded_audio>",
"language": "de",
"task": "transcribe"
}
HF_ACCESS_TOKEN = os.environ.get("HF_TRANSCRIPTION_ACCESS_TOKEN")
API_URL = os.environ.get("HF_TRANSCRIPTION_ENDPOINT")
HEADERS = {
"Authorization": HF_ACCESS_TOKEN,
"Content-Type": "application/json"
}
response = requests.post(API_URL, headers=HEADERS, json=DATA)
print(response)
```
## Logging
Logging is set up to debug level, providing detailed information during the transcription process, including the length
of decoded bytes, the progress of segments being transcribed, and a confirmation once the inference is completed.
## Deployment
This service is intended for deployment on Hugging Face endpoints. Ensure you follow Hugging Face's guidelines for
deploying model endpoints.
|
parksanha/xlm-roberta-base-finetuned-panx-de
|
parksanha
| 2024-01-02T22:54:46Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-02T22:51:51Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1353
- F1: 0.8652
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2582 | 1.0 | 525 | 0.1535 | 0.8259 |
| 0.1285 | 2.0 | 1050 | 0.1356 | 0.8534 |
| 0.0802 | 3.0 | 1575 | 0.1353 | 0.8652 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
alirzb/S2_M1_R3_deit_42509578
|
alirzb
| 2024-01-02T22:53:51Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-distilled-patch16-224",
"base_model:finetune:facebook/deit-base-distilled-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-02T21:27:03Z |
---
license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S2_M1_R3_deit_42509578
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9990333494441759
---
<!-- 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. -->
# S2_M1_R3_deit_42509578
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0060
- Accuracy: 0.9990
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0149 | 1.0 | 258 | 0.0100 | 0.9973 |
| 0.004 | 2.0 | 517 | 0.0058 | 0.9988 |
| 0.0097 | 3.0 | 776 | 0.0074 | 0.9986 |
| 0.0002 | 4.0 | 1035 | 0.0041 | 0.9993 |
| 0.0 | 4.99 | 1290 | 0.0060 | 0.9990 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
|
LoneStriker/openbuddy-mixtral-7bx8-v16.3-32k-5.0bpw-h6-exl2
|
LoneStriker
| 2024-01-02T22:49:04Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-01-02T22:08:34Z |
---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
alirzb/S1_M1_R3_deit_42509575
|
alirzb
| 2024-01-02T22:48:03Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-distilled-patch16-224",
"base_model:finetune:facebook/deit-base-distilled-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-02T20:57:31Z |
---
license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S1_M1_R3_deit_42509575
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9990228649599374
---
<!-- 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. -->
# S1_M1_R3_deit_42509575
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0039
- Accuracy: 0.9990
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0046 | 1.0 | 320 | 0.0065 | 0.9973 |
| 0.0027 | 2.0 | 640 | 0.0124 | 0.9975 |
| 0.0001 | 3.0 | 960 | 0.0013 | 0.9994 |
| 0.0 | 4.0 | 1280 | 0.0028 | 0.9992 |
| 0.0 | 5.0 | 1600 | 0.0039 | 0.9990 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
|
LoneStriker/openbuddy-mixtral-7bx8-v16.3-32k-4.0bpw-h6-exl2
|
LoneStriker
| 2024-01-02T22:43:17Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-01-02T22:07:16Z |
---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_humanMix_Seed115
|
behzadnet
| 2024-01-02T22:26:49Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2024-01-02T22:26:47Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_humanMix_Seed115
|
behzadnet
| 2024-01-02T22:26:41Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2024-01-02T22:26:34Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
alirzb/S1_M1_R1_deit_42509573
|
alirzb
| 2024-01-02T22:16:10Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-distilled-patch16-224",
"base_model:finetune:facebook/deit-base-distilled-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-02T20:57:27Z |
---
license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: S1_M1_R1_deit_42509573
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9987801902903147
---
<!-- 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. -->
# S1_M1_R1_deit_42509573
This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0037
- Accuracy: 0.9988
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0237 | 1.0 | 256 | 0.0053 | 0.9983 |
| 0.0014 | 2.0 | 512 | 0.0056 | 0.9985 |
| 0.0 | 3.0 | 768 | 0.0023 | 0.9993 |
| 0.0 | 4.0 | 1025 | 0.0037 | 0.9988 |
| 0.0 | 5.0 | 1280 | 0.0037 | 0.9988 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.11.0+cu102
- Datasets 2.16.0
- Tokenizers 0.15.0
|
jordyvl/2024-01-02_one_stage_subgraphs_weighted_txt_vis_conc_1_4_8_12_ramp
|
jordyvl
| 2024-01-02T22:10:38Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-02T16:58:49Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 2024-01-02_one_stage_subgraphs_weighted_txt_vis_conc_1_4_8_12_ramp
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. -->
# 2024-01-02_one_stage_subgraphs_weighted_txt_vis_conc_1_4_8_12_ramp
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8574
- Accuracy: 0.77
- Exit 0 Accuracy: 0.2
- Exit 1 Accuracy: 0.3
- Exit 2 Accuracy: 0.1125
- Exit 3 Accuracy: 0.2725
- Exit 4 Accuracy: 0.2675
- Exit 5 Accuracy: 0.51
- Exit 6 Accuracy: 0.55
- Exit 7 Accuracy: 0.63
- Exit 8 Accuracy: 0.525
- Exit 9 Accuracy: 0.3425
- Exit 10 Accuracy: 0.445
- Exit 11 Accuracy: 0.6875
- Exit 12 Accuracy: 0.7575
## 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: 4
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy | Exit 5 Accuracy | Exit 6 Accuracy | Exit 7 Accuracy | Exit 8 Accuracy | Exit 9 Accuracy | Exit 10 Accuracy | Exit 11 Accuracy | Exit 12 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|:----------------:|:----------------:|:----------------:|
| No log | 0.96 | 4 | 2.7477 | 0.1025 | 0.07 | 0.0625 | 0.0625 | 0.0625 | 0.06 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.06 |
| No log | 1.96 | 8 | 2.7020 | 0.1275 | 0.0775 | 0.0625 | 0.0625 | 0.0625 | 0.0575 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.06 |
| No log | 2.96 | 12 | 2.6514 | 0.19 | 0.08 | 0.0625 | 0.0625 | 0.0625 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.065 |
| No log | 3.96 | 16 | 2.5720 | 0.215 | 0.095 | 0.065 | 0.0625 | 0.0625 | 0.06 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0925 |
| No log | 4.96 | 20 | 2.5033 | 0.2375 | 0.1 | 0.0675 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.1475 |
| No log | 5.96 | 24 | 2.4003 | 0.275 | 0.115 | 0.08 | 0.0625 | 0.0625 | 0.0625 | 0.0775 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.1525 |
| No log | 6.96 | 28 | 2.3192 | 0.3 | 0.12 | 0.0875 | 0.0625 | 0.0625 | 0.0625 | 0.09 | 0.075 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.1625 |
| No log | 7.96 | 32 | 2.2199 | 0.3325 | 0.1325 | 0.0875 | 0.0625 | 0.0625 | 0.0625 | 0.1025 | 0.075 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.185 |
| No log | 8.96 | 36 | 2.1335 | 0.36 | 0.1425 | 0.1025 | 0.0625 | 0.0625 | 0.0625 | 0.1375 | 0.09 | 0.07 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.235 |
| No log | 9.96 | 40 | 2.0290 | 0.4 | 0.1475 | 0.1025 | 0.0625 | 0.0625 | 0.06 | 0.15 | 0.0925 | 0.0725 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.27 |
| No log | 10.96 | 44 | 1.9285 | 0.4525 | 0.155 | 0.1025 | 0.0625 | 0.065 | 0.0675 | 0.1725 | 0.1225 | 0.08 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.305 |
| No log | 11.96 | 48 | 1.8071 | 0.495 | 0.155 | 0.1075 | 0.0625 | 0.0775 | 0.0825 | 0.2025 | 0.135 | 0.0925 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.355 |
| No log | 12.96 | 52 | 1.7194 | 0.5375 | 0.16 | 0.1075 | 0.0625 | 0.0775 | 0.0875 | 0.22 | 0.1625 | 0.1025 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.37 |
| No log | 13.96 | 56 | 1.5732 | 0.6 | 0.1625 | 0.11 | 0.0625 | 0.08 | 0.0775 | 0.2425 | 0.2125 | 0.14 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.43 |
| No log | 14.96 | 60 | 1.5141 | 0.6125 | 0.16 | 0.115 | 0.0625 | 0.0825 | 0.09 | 0.29 | 0.245 | 0.18 | 0.0625 | 0.065 | 0.0625 | 0.0625 | 0.4325 |
| No log | 15.96 | 64 | 1.4049 | 0.65 | 0.1575 | 0.1175 | 0.0625 | 0.0925 | 0.1 | 0.3275 | 0.2725 | 0.24 | 0.0675 | 0.065 | 0.0625 | 0.0625 | 0.51 |
| No log | 16.96 | 68 | 1.3476 | 0.665 | 0.16 | 0.115 | 0.0625 | 0.095 | 0.1025 | 0.345 | 0.285 | 0.2625 | 0.0675 | 0.075 | 0.0625 | 0.0625 | 0.5275 |
| No log | 17.96 | 72 | 1.2825 | 0.6925 | 0.16 | 0.1175 | 0.0625 | 0.1025 | 0.11 | 0.35 | 0.2875 | 0.2925 | 0.0675 | 0.075 | 0.065 | 0.0625 | 0.55 |
| No log | 18.96 | 76 | 1.2102 | 0.71 | 0.16 | 0.1175 | 0.0625 | 0.1025 | 0.135 | 0.365 | 0.29 | 0.31 | 0.0675 | 0.0775 | 0.08 | 0.0625 | 0.5925 |
| No log | 19.96 | 80 | 1.1664 | 0.725 | 0.16 | 0.1175 | 0.0625 | 0.1125 | 0.1325 | 0.3675 | 0.3075 | 0.365 | 0.0775 | 0.0775 | 0.08 | 0.0625 | 0.6025 |
| No log | 20.96 | 84 | 1.1363 | 0.735 | 0.16 | 0.12 | 0.0625 | 0.115 | 0.145 | 0.3725 | 0.3275 | 0.3775 | 0.0775 | 0.075 | 0.07 | 0.0625 | 0.6175 |
| No log | 21.96 | 88 | 1.0745 | 0.74 | 0.16 | 0.1225 | 0.0625 | 0.1175 | 0.1325 | 0.375 | 0.355 | 0.4175 | 0.0775 | 0.0825 | 0.065 | 0.0625 | 0.6275 |
| No log | 22.96 | 92 | 1.0377 | 0.7525 | 0.16 | 0.13 | 0.0625 | 0.115 | 0.1575 | 0.38 | 0.3775 | 0.4125 | 0.0825 | 0.075 | 0.07 | 0.0625 | 0.64 |
| No log | 23.96 | 96 | 1.0321 | 0.74 | 0.16 | 0.1375 | 0.0625 | 0.115 | 0.165 | 0.3825 | 0.385 | 0.4375 | 0.08 | 0.0975 | 0.07 | 0.0625 | 0.66 |
| No log | 24.96 | 100 | 0.9702 | 0.76 | 0.1625 | 0.15 | 0.0625 | 0.11 | 0.1725 | 0.385 | 0.4075 | 0.455 | 0.0825 | 0.0975 | 0.0725 | 0.0625 | 0.68 |
| No log | 25.96 | 104 | 0.9861 | 0.7525 | 0.1675 | 0.1525 | 0.0625 | 0.1125 | 0.1825 | 0.395 | 0.4 | 0.4675 | 0.0825 | 0.115 | 0.0675 | 0.07 | 0.6825 |
| No log | 26.96 | 108 | 0.9339 | 0.7525 | 0.16 | 0.15 | 0.0625 | 0.1175 | 0.195 | 0.4075 | 0.4275 | 0.4825 | 0.0875 | 0.13 | 0.07 | 0.095 | 0.7025 |
| No log | 27.96 | 112 | 0.9362 | 0.7575 | 0.1625 | 0.1425 | 0.0625 | 0.1175 | 0.1925 | 0.4175 | 0.43 | 0.515 | 0.095 | 0.14 | 0.0675 | 0.105 | 0.71 |
| No log | 28.96 | 116 | 0.8872 | 0.755 | 0.165 | 0.15 | 0.0625 | 0.1175 | 0.2 | 0.4275 | 0.4325 | 0.53 | 0.095 | 0.1425 | 0.07 | 0.125 | 0.7425 |
| No log | 29.96 | 120 | 0.8939 | 0.7675 | 0.1625 | 0.15 | 0.0625 | 0.1175 | 0.2 | 0.4475 | 0.4325 | 0.55 | 0.1 | 0.1425 | 0.085 | 0.1325 | 0.7175 |
| No log | 30.96 | 124 | 0.8767 | 0.7475 | 0.16 | 0.1575 | 0.0625 | 0.12 | 0.195 | 0.4425 | 0.4475 | 0.545 | 0.1 | 0.1525 | 0.0925 | 0.2025 | 0.7575 |
| No log | 31.96 | 128 | 0.8658 | 0.76 | 0.165 | 0.17 | 0.0625 | 0.1225 | 0.195 | 0.455 | 0.455 | 0.555 | 0.1025 | 0.1375 | 0.11 | 0.245 | 0.7375 |
| No log | 32.96 | 132 | 0.8736 | 0.7625 | 0.165 | 0.1875 | 0.0625 | 0.125 | 0.195 | 0.465 | 0.45 | 0.5625 | 0.105 | 0.155 | 0.0975 | 0.275 | 0.7575 |
| No log | 33.96 | 136 | 0.8380 | 0.7625 | 0.1675 | 0.21 | 0.0625 | 0.125 | 0.195 | 0.465 | 0.4625 | 0.565 | 0.1175 | 0.13 | 0.115 | 0.3225 | 0.755 |
| No log | 34.96 | 140 | 0.8386 | 0.7725 | 0.1675 | 0.2325 | 0.0625 | 0.1275 | 0.1975 | 0.4675 | 0.4575 | 0.575 | 0.12 | 0.125 | 0.13 | 0.345 | 0.765 |
| No log | 35.96 | 144 | 0.8610 | 0.755 | 0.17 | 0.2425 | 0.0625 | 0.1275 | 0.19 | 0.4625 | 0.4725 | 0.5875 | 0.13 | 0.105 | 0.18 | 0.39 | 0.755 |
| No log | 36.96 | 148 | 0.8444 | 0.76 | 0.17 | 0.255 | 0.0625 | 0.1325 | 0.2 | 0.4575 | 0.4675 | 0.595 | 0.12 | 0.1475 | 0.22 | 0.445 | 0.7525 |
| No log | 37.96 | 152 | 0.8845 | 0.75 | 0.1725 | 0.2725 | 0.0625 | 0.1375 | 0.205 | 0.46 | 0.475 | 0.59 | 0.1425 | 0.175 | 0.255 | 0.45 | 0.7575 |
| No log | 38.96 | 156 | 0.8464 | 0.7625 | 0.18 | 0.275 | 0.0625 | 0.145 | 0.2075 | 0.4675 | 0.4825 | 0.5925 | 0.18 | 0.22 | 0.265 | 0.51 | 0.755 |
| No log | 39.96 | 160 | 0.8539 | 0.7575 | 0.1825 | 0.2825 | 0.065 | 0.1475 | 0.215 | 0.48 | 0.515 | 0.6025 | 0.2 | 0.2425 | 0.2725 | 0.5275 | 0.755 |
| No log | 40.96 | 164 | 0.8697 | 0.76 | 0.185 | 0.2775 | 0.0675 | 0.1525 | 0.23 | 0.485 | 0.5325 | 0.605 | 0.2175 | 0.21 | 0.29 | 0.53 | 0.7625 |
| No log | 41.96 | 168 | 0.8395 | 0.775 | 0.185 | 0.2825 | 0.075 | 0.16 | 0.2225 | 0.4925 | 0.54 | 0.6 | 0.225 | 0.225 | 0.2875 | 0.5725 | 0.77 |
| No log | 42.96 | 172 | 0.8570 | 0.7675 | 0.1875 | 0.285 | 0.08 | 0.1575 | 0.2275 | 0.485 | 0.5475 | 0.61 | 0.2325 | 0.2325 | 0.3075 | 0.6075 | 0.7525 |
| No log | 43.96 | 176 | 0.8462 | 0.765 | 0.195 | 0.28 | 0.08 | 0.165 | 0.2325 | 0.49 | 0.5425 | 0.6125 | 0.2475 | 0.2425 | 0.3125 | 0.6225 | 0.755 |
| No log | 44.96 | 180 | 0.8563 | 0.765 | 0.195 | 0.2825 | 0.085 | 0.1775 | 0.235 | 0.495 | 0.535 | 0.6075 | 0.2975 | 0.22 | 0.3175 | 0.62 | 0.75 |
| No log | 45.96 | 184 | 0.8670 | 0.7675 | 0.195 | 0.28 | 0.085 | 0.1825 | 0.24 | 0.4975 | 0.54 | 0.615 | 0.3525 | 0.215 | 0.325 | 0.6375 | 0.76 |
| No log | 46.96 | 188 | 0.8708 | 0.77 | 0.195 | 0.29 | 0.0925 | 0.185 | 0.2375 | 0.4975 | 0.535 | 0.6125 | 0.365 | 0.2275 | 0.3175 | 0.64 | 0.7575 |
| No log | 47.96 | 192 | 0.8535 | 0.7675 | 0.19 | 0.29 | 0.095 | 0.2075 | 0.24 | 0.4975 | 0.5375 | 0.6125 | 0.4025 | 0.24 | 0.35 | 0.6575 | 0.755 |
| No log | 48.96 | 196 | 0.8592 | 0.765 | 0.19 | 0.285 | 0.0975 | 0.2175 | 0.2425 | 0.495 | 0.54 | 0.615 | 0.4175 | 0.2375 | 0.365 | 0.6575 | 0.7475 |
| No log | 49.96 | 200 | 0.8717 | 0.765 | 0.19 | 0.2925 | 0.1 | 0.235 | 0.25 | 0.5 | 0.545 | 0.6125 | 0.4325 | 0.25 | 0.3725 | 0.66 | 0.76 |
| No log | 50.96 | 204 | 0.8684 | 0.765 | 0.1925 | 0.2975 | 0.105 | 0.245 | 0.2575 | 0.5025 | 0.545 | 0.61 | 0.4475 | 0.2775 | 0.3775 | 0.675 | 0.7625 |
| No log | 51.96 | 208 | 0.8662 | 0.76 | 0.1925 | 0.295 | 0.1025 | 0.245 | 0.2625 | 0.5025 | 0.55 | 0.6175 | 0.455 | 0.2925 | 0.39 | 0.68 | 0.76 |
| No log | 52.96 | 212 | 0.8718 | 0.7625 | 0.1925 | 0.295 | 0.1075 | 0.2525 | 0.2625 | 0.5025 | 0.55 | 0.6225 | 0.485 | 0.3075 | 0.4125 | 0.6825 | 0.755 |
| No log | 53.96 | 216 | 0.8798 | 0.76 | 0.195 | 0.295 | 0.11 | 0.265 | 0.2625 | 0.505 | 0.5475 | 0.6275 | 0.495 | 0.3175 | 0.4275 | 0.68 | 0.7475 |
| No log | 54.96 | 220 | 0.8703 | 0.7575 | 0.2 | 0.2975 | 0.11 | 0.2675 | 0.2675 | 0.5075 | 0.545 | 0.6225 | 0.4975 | 0.3275 | 0.435 | 0.6825 | 0.745 |
| No log | 55.96 | 224 | 0.8622 | 0.765 | 0.2 | 0.3 | 0.11 | 0.265 | 0.27 | 0.51 | 0.545 | 0.625 | 0.505 | 0.33 | 0.435 | 0.69 | 0.7525 |
| No log | 56.96 | 228 | 0.8590 | 0.77 | 0.2 | 0.3 | 0.11 | 0.27 | 0.2675 | 0.5125 | 0.5475 | 0.6325 | 0.5075 | 0.34 | 0.4375 | 0.6875 | 0.76 |
| No log | 57.96 | 232 | 0.8572 | 0.7725 | 0.2 | 0.3025 | 0.11 | 0.27 | 0.2675 | 0.51 | 0.5475 | 0.6325 | 0.5175 | 0.34 | 0.44 | 0.6875 | 0.7575 |
| No log | 58.96 | 236 | 0.8570 | 0.7725 | 0.2 | 0.3025 | 0.1125 | 0.2725 | 0.2675 | 0.51 | 0.55 | 0.6325 | 0.5225 | 0.34 | 0.445 | 0.6875 | 0.76 |
| No log | 59.96 | 240 | 0.8574 | 0.77 | 0.2 | 0.3 | 0.1125 | 0.2725 | 0.2675 | 0.51 | 0.55 | 0.63 | 0.525 | 0.3425 | 0.445 | 0.6875 | 0.7575 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
yy0514/llama2-7b-chat-qlora-lek-train-4-epochs-run1
|
yy0514
| 2024-01-02T22:07:16Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-02T22:06:52Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: llama2-7b-qlora-lek-train-more-epochs
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. -->
# llama2-7b-qlora-lek-train-more-epochs
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
multimodalart/medieval-animals-lora
|
multimodalart
| 2024-01-02T22:06:19Z | 9 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-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
| 2024-01-02T22:06:08Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: a drawing of a woman and a demon with a blanket in the style of <s0><s1>
output:
url: image-0.png
- text: a bear playing a flute in a medieval manuscript in the style of <s0><s1>
output:
url: image-1.png
- text: a drawing of a bat with wings and a bat's head in the style of <s0><s1>
output:
url: image-2.png
- text: a drawing of a bat with a sign that reads "hic" in the style of <s0><s1>
output:
url: image-3.png
- text: a drawing of a mouse playing with a wheel in the style of <s0><s1>
output:
url: image-4.png
- text: a cat wearing a crown sits on a throne in the style of <s0><s1>
output:
url: image-5.png
- text: a horse with a tree and a bird in the middle of the page in the style of <s0><s1>
output:
url: image-6.png
- text: a monkey with a pipe sitting on the ground in the style of <s0><s1>
output:
url: image-7.png
- text: a small dragon with wings and a tail in the style of <s0><s1>
output:
url: image-8.png
- text: a snail is sitting on a branch with a snake in the style of <s0><s1>
output:
url: image-9.png
- text: a medieval illustration of a man eating a fish in the style of <s0><s1>
output:
url: image-10.png
- text: a cat playing a lute in a medieval manuscript in the style of <s0><s1>
output:
url: image-11.png
- text: a painting of a cat playing a trumpet in the style of <s0><s1>
output:
url: image-12.png
- text: a closeup of an owl in a medieval manuscript in the style of <s0><s1>
output:
url: image-13.png
- text: a medieval illustration of a rabbit carrying a basket in the style of <s0><s1>
output:
url: image-14.png
- text: a medieval illustration of a dog riding a duck in the style of <s0><s1>
output:
url: image-15.png
- text: a drawing of a lion with a man's face on it in the style of <s0><s1>
output:
url: image-16.png
- text: a medieval illustration of a dog riding a horse in the style of <s0><s1>
output:
url: image-17.png
- text: a cat is sitting on a green plate with flowers in the style of <s0><s1>
output:
url: image-18.png
- text: an illustration of an owl in a medieval manuscript in the style of <s0><s1>
output:
url: image-19.png
- text: a drawing of a hairy creature with red shoes in the style of <s0><s1>
output:
url: image-20.png
- text: a medieval illustration of a man being attacked by a dog in the style of <s0><s1>
output:
url: image-21.png
- text: a cat is sitting on a blue and gold background in the style of <s0><s1>
output:
url: image-22.png
- text: an illustration of a unicorn with a horn in the style of <s0><s1>
output:
url: image-23.png
- text: a cat playing a harp in a medieval manuscript in the style of <s0><s1>
output:
url: image-24.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: in the style of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - multimodalart/medieval-animals-lora
<Gallery />
## Model description
### These are multimodalart/medieval-animals-lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`medieval-animals-lora.safetensors` here 💾](/multimodalart/medieval-animals-lora/blob/main/medieval-animals-lora.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:medieval-animals-lora:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`medieval-animals-lora_emb.safetensors` here 💾](/multimodalart/medieval-animals-lora/blob/main/medieval-animals-lora_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `medieval-animals-lora_emb` to your prompt. For example, `in the style of medieval-animals-lora_emb`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('multimodalart/medieval-animals-lora', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='multimodalart/medieval-animals-lora', filename='medieval-animals-lora_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('in the style of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/multimodalart/medieval-animals-lora/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
MugoSquero/LMCocktail-phi-2-v1.1
|
MugoSquero
| 2024-01-02T22:02:53Z | 18 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi-msft",
"text-generation",
"custom_code",
"en",
"arxiv:2311.13534",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-02T19:26:06Z |
---
pipeline_tag: text-generation
tags:
- phi-msft
language:
- en
library_name: transformers
---
# LM-Cocktail phi-2 v1.1
This is a 0.5-0.5 merge of two models based on phi-2. Here are the models used to create this merge:
1. [venkycs/phi-2-instruct](https://huggingface.co/venkycs/phi-2-instruct)
2. [Yhyu13/phi-2-sft-dpo-gpt4_en-ep1](https://huggingface.co/Yhyu13/phi-2-sft-dpo-gpt4_en-ep1)
I named this model "LMCocktail phi-2 v1.1" because I see it as a continuation of the [v1](https://huggingface.co/Yhyu13/LMCocktail-phi-2-v1).
I used [Yhyu13/phi-2-sft-dpo-gpt4_en-ep1](https://huggingface.co/Yhyu13/phi-2-sft-dpo-gpt4_en-ep1) and it "outputs significantly longer result" than the one used in v1 by Yhyu13.
I also used [venkycs/phi-2-instruct](https://huggingface.co/venkycs/phi-2-instruct) "a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the filtered [ultrachat200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset using the SFT technique".
The main reason I created this model was to merge it with [cognitivecomputations/dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2), and I will create a repo for it when I do it.
# Code
The LM-cocktail is novel technique for merging multiple models: https://arxiv.org/abs/2311.13534
Code is backed up by this repo: https://github.com/FlagOpen/FlagEmbedding.git
Merging script is available under the [./scripts](./scripts) folder.
|
miftahmoha/hermeszl
|
miftahmoha
| 2024-01-02T21:59:26Z | 3 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"region:us"
] | null | 2024-01-02T20:45:06Z |
---
library_name: peft
base_model: teknium/OpenHermes-2.5-Mistral-7B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
Adleu/filip_dewinter_LoRA
|
Adleu
| 2024-01-02T21:57:33Z | 4 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-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
| 2024-01-02T21:57:28Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK filip dewinter,
license: openrail++
---
# SDXL LoRA DreamBooth - Adleu/filip_dewinter_LoRA
<Gallery />
## Model description
These are Adleu/filip_dewinter_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK filip dewinter, to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Adleu/filip_dewinter_LoRA/tree/main) them in the Files & versions tab.
|
daniel-gordon/Q-FrozenLake-4x4-noSlippery
|
daniel-gordon
| 2024-01-02T21:51:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-02T21:51:13Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Q-FrozenLake-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="daniel-gordon/Q-FrozenLake-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"])
```
|
Asorteberg/testtwo
|
Asorteberg
| 2024-01-02T21:48:30Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-01-02T21:48:26Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
LoneStriker/deepseek-llm-67b-Spicy-3.1-1-4.65bpw-h6-exl2
|
LoneStriker
| 2024-01-02T21:41:15Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"dataset:unalignment/spicy-3.1",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-04T03:07:05Z |
---
license: other
license_name: deepseek
license_link: LICENSE
datasets:
- unalignment/spicy-3.1
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
# Fine-tune of Deepseek 67B
Fine-tuned with jondurbin's unalignment/spicy-3.1 for 1 epoch.
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-base` is a 67B parameter model with Grouped-Query Attention trained on 2 trillion tokens from scratch.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Text Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
|
mongotom/mongo-tom-10k-llama70b-monsterapi
|
mongotom
| 2024-01-02T21:39:48Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2024-01-02T21:39:43Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
|
eyesss/man-ohwx
|
eyesss
| 2024-01-02T21:38:11Z | 15 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-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
| 2024-01-02T21:38:04Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: A photo of <s0><s1> a man wearing a hat and a woman wearing a hat
output:
url: image-0.png
- text: A photo of <s0><s1> a man in a car taking a selfie
output:
url: image-1.png
- text: A photo of <s0><s1> a man in a car taking a selfie
output:
url: image-2.png
- text: A photo of <s0><s1> a man sitting in the back seat of a car
output:
url: image-3.png
- text: A photo of <s0><s1> a man in a car taking a selfie
output:
url: image-4.png
- text: A photo of <s0><s1> a smiling man in a white shirt in front of a window
output:
url: image-5.png
- text: A photo of <s0><s1> a man with a mustache smiles for the camera
output:
url: image-6.png
- text: A photo of <s0><s1> a smiling man in a blue shirt
output:
url: image-7.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - eyesss/man-ohwx
<Gallery />
## Model description
### These are eyesss/man-ohwx LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`man-ohwx.safetensors` here 💾](/eyesss/man-ohwx/blob/main/man-ohwx.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:man-ohwx:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`man-ohwx_emb.safetensors` here 💾](/eyesss/man-ohwx/blob/main/man-ohwx_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `man-ohwx_emb` to your prompt. For example, `A photo of man-ohwx_emb`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('eyesss/man-ohwx', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='eyesss/man-ohwx', filename='man-ohwx_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A photo of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/eyesss/man-ohwx/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
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.