modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
yanex0/realistic-stock-photo
|
yanex0
| 2024-02-24T20:12:44Z
| 0
| 0
| null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-24T19:39:26Z
|
---
license: creativeml-openrail-m
---
|
gaurav16/mycopilot
|
gaurav16
| 2024-02-24T19:58:56Z
| 7
| 1
| null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-01-27T02:56:35Z
|
# Model Card for Personal Copilot
## Model Details
### Model Description
Gaurav Sinha's personal copilot, built using CodeLlama, is a versatile AI assistant for software development. Operating within VSCode, it offers code suggestions, generates test cases, executes shell commands, and provides editing capabilities, enhancing the development workflow.
- **Developed by:** Gaurav Sinha
- **Model type:** Generative AI
- **Language(s):** Python, JavaScript
- **License:** [Insert License]
### Model Sources
- **Repository:** https://huggingface.co/gaurav16/mycopilot/tree/main
## Uses
### Direct Use
The copilot can be directly used in VSCode to assist with coding tasks, including generating test cases, offering code suggestions, executing shell commands, and providing editing capabilities.
### Downstream Use
This model can be fine-tuned for specific tasks or integrated into larger AI systems for more complex applications.
### Out-of-Scope Use
The model is not intended for malicious use, and its capabilities are limited to providing assistance with software development tasks.
## Bias, Risks, and Limitations
[Insert information about potential bias, risks, and limitations of the model.]
|
josephleekl/ppo-LunarLander-v2
|
josephleekl
| 2024-02-24T19:57:04Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-24T19:56:46Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.21 +/- 20.82
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
naonao0715/backward_Mxy_0
|
naonao0715
| 2024-02-24T19:55:19Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-24T17:53:14Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
djomo/MISTRALllux2000-7b-v8
|
djomo
| 2024-02-24T19:46:50Z
| 7
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T19:39:22Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
archiMAD/ppo-Pyramids
|
archiMAD
| 2024-02-24T19:43:44Z
| 8
| 0
|
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-02-24T19:43:40Z
|
---
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 πΆ 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: archiMAD/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
badokorach/distilbert-base-cased-distilled-agric-trans-2402241
|
badokorach
| 2024-02-24T19:37:12Z
| 61
| 0
|
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:badokorach/distilbert-base-cased-distilled-agric-trans-240224",
"base_model:finetune:badokorach/distilbert-base-cased-distilled-agric-trans-240224",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-02-24T19:22:33Z
|
---
license: apache-2.0
base_model: badokorach/distilbert-base-cased-distilled-agric-trans-240224
tags:
- generated_from_keras_callback
model-index:
- name: badokorach/distilbert-base-cased-distilled-agric-trans-2402241
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. -->
# badokorach/distilbert-base-cased-distilled-agric-trans-2402241
This model is a fine-tuned version of [badokorach/distilbert-base-cased-distilled-agric-trans-240224](https://huggingface.co/badokorach/distilbert-base-cased-distilled-agric-trans-240224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2119
- Validation Loss: 0.0
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 690, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.8415 | 0.0 | 0 |
| 0.7094 | 0.0 | 1 |
| 0.6198 | 0.0 | 2 |
| 0.5324 | 0.0 | 3 |
| 0.4700 | 0.0 | 4 |
| 0.4164 | 0.0 | 5 |
| 0.3747 | 0.0 | 6 |
| 0.3353 | 0.0 | 7 |
| 0.3137 | 0.0 | 8 |
| 0.2898 | 0.0 | 9 |
| 0.2684 | 0.0 | 10 |
| 0.2474 | 0.0 | 11 |
| 0.2469 | 0.0 | 12 |
| 0.2305 | 0.0 | 13 |
| 0.2119 | 0.0 | 14 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.17.1
- Tokenizers 0.15.2
|
suhaaspk/suhaasface
|
suhaaspk
| 2024-02-24T19:34:25Z
| 28
| 0
|
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-24T19:28:36Z
|
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### SuhaasFace Dreambooth model trained by suhaaspk with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
jesuzn/LunarLander-v2
|
jesuzn
| 2024-02-24T19:15:44Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-24T19:09:07Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 265.83 +/- 25.80
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
kalobiralo/t5-grammar-model
|
kalobiralo
| 2024-02-24T19:03:29Z
| 107
| 0
|
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-24T19:02:49Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
shaheryar48/roberta_fine_tuned
|
shaheryar48
| 2024-02-24T18:58:07Z
| 163
| 0
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-24T18:56:07Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
macadeliccc/MonarchCorso-7B
|
macadeliccc
| 2024-02-24T18:57:28Z
| 6
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:macadeliccc/MBX-7B-v3-DPO",
"base_model:merge:macadeliccc/MBX-7B-v3-DPO",
"base_model:mlabonne/AlphaMonarch-7B",
"base_model:merge:mlabonne/AlphaMonarch-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-23T04:51:54Z
|
---
base_model:
- macadeliccc/MBX-7B-v3-DPO
- mlabonne/AlphaMonarch-7B
library_name: transformers
tags:
- mergekit
- merge
---
# MonarchCorso-7B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [macadeliccc/MBX-7B-v3-DPO](https://huggingface.co/macadeliccc/MBX-7B-v3-DPO)
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: mlabonne/AlphaMonarch-7B
layer_range: [0, 32]
- model: macadeliccc/MBX-7B-v3-DPO
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
bitadin/medium-title-latest
|
bitadin
| 2024-02-24T18:48:47Z
| 13
| 0
|
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-22T10:03:13Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mi-rei/CT_clinical-longformer_II_efficient_10e_pass2
|
mi-rei
| 2024-02-24T18:44:31Z
| 119
| 0
|
transformers
|
[
"transformers",
"safetensors",
"longformer",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-24T18:43:47Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
badokorach/distilbert-base-cased-distilled-agric-trans-240224
|
badokorach
| 2024-02-24T18:32:09Z
| 60
| 0
|
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-cased-distilled-squad",
"base_model:finetune:distilbert/distilbert-base-cased-distilled-squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-02-24T18:02:21Z
|
---
license: apache-2.0
base_model: distilbert-base-cased-distilled-squad
tags:
- generated_from_keras_callback
model-index:
- name: badokorach/distilbert-base-cased-distilled-agric-trans-240224
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. -->
# badokorach/distilbert-base-cased-distilled-agric-trans-240224
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.8057
- Validation Loss: 0.0
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 690, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8831 | 0.0 | 0 |
| 2.1002 | 0.0 | 1 |
| 1.8590 | 0.0 | 2 |
| 1.6544 | 0.0 | 3 |
| 1.4724 | 0.0 | 4 |
| 1.3464 | 0.0 | 5 |
| 1.2419 | 0.0 | 6 |
| 1.1152 | 0.0 | 7 |
| 1.0354 | 0.0 | 8 |
| 0.9869 | 0.0 | 9 |
| 0.9086 | 0.0 | 10 |
| 0.8639 | 0.0 | 11 |
| 0.8258 | 0.0 | 12 |
| 0.7978 | 0.0 | 13 |
| 0.8057 | 0.0 | 14 |
### Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.17.1
- Tokenizers 0.15.2
|
ZEECO1/CancerLLM-Mistral7b
|
ZEECO1
| 2024-02-24T18:04:42Z
| 0
| 2
| null |
[
"safetensors",
"region:us"
] | null | 2024-02-24T17:53:48Z
|
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
base_model_id = "mistralai/Mistral-7B-v0.1"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id, # Mistral, same as before
quantization_config=bnb_config, # Same quantization config as before
device_map="auto",
trust_remote_code=True,
)
eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True)
from peft import PeftModel
ft_model = PeftModel.from_pretrained(base_model, "ZEECO1/CancerLLM-Mistral7b/checkpoint-500")
eval_prompt = " what are the drugs against lung cancer: # "
model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda")
ft_model.eval()
with torch.no_grad():
print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.15)[0], skip_special_tokens=True))
|
LoneStriker/OpenCodeInterpreter-DS-33B-8.0bpw-h8-exl2
|
LoneStriker
| 2024-02-24T17:52:45Z
| 5
| 2
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T17:39:03Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-DS-33B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
robdemunck/finetuned-t5-small-cnn_dailymail
|
robdemunck
| 2024-02-24T17:44:44Z
| 104
| 0
|
transformers
|
[
"transformers",
"safetensors",
"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
| 2024-02-23T20:10:05Z
|
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: finetuned-t5-small-cnn_dailymail
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-t5-small-cnn_dailymail
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
LoneStriker/OpenCodeInterpreter-DS-33B-6.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T17:39:01Z
| 5
| 1
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T17:28:39Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-DS-33B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
archiMAD/ppo-SnowballTarget
|
archiMAD
| 2024-02-24T17:20:20Z
| 0
| 0
|
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-02-24T17:20:18Z
|
---
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 πΆ 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: archiMAD/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
|
Ayus077BCT014Bhandari/vartat5-using-100K-plus-17
|
Ayus077BCT014Bhandari
| 2024-02-24T17:15:39Z
| 107
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-24T15:17:14Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
922-SY/ts-v2-gguf
|
922-SY
| 2024-02-24T16:52:00Z
| 4
| 0
|
transformers
|
[
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:SeaLLMs/SeaLLM-7B-v2",
"base_model:quantized:SeaLLMs/SeaLLM-7B-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-02-24T16:48:16Z
|
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
base_model: SeaLLMs/SeaLLM-7B-v2
---
# Uploaded model
- **Developed by:** 922CA
- **License:** apache-2.0
- **Finetuned from model :** SeaLLMs/SeaLLM-7B-v2
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
LoneStriker/OpenCodeInterpreter-CL-13B-6.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T16:47:57Z
| 2
| 0
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T16:43:34Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-13B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
LoneStriker/OpenCodeInterpreter-CL-13B-5.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T16:43:33Z
| 4
| 0
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T16:39:56Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-13B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
Edentns/DataVortexS-10.7B-dpo-v1.8
|
Edentns
| 2024-02-24T16:40:16Z
| 2,290
| 1
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"base_model:megastudyedu/M-SOLAR-10.7B-v1.3",
"base_model:finetune:megastudyedu/M-SOLAR-10.7B-v1.3",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-29T08:12:38Z
|
---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: megastudy/M-SOLAR-10.7B-v1.3
pipeline_tag: text-generation
---
# **DataVortexS-10.7B-dpo-v1.8**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[megastudy/M-SOLAR-10.7B-v1.3](https://huggingface.co/megastudy/M-SOLAR-10.7B-v1.3)
### **Trained On**
- **OS**: Ubuntu 22.04
- **GPU**: H100 80GB 4ea
- **transformers**: v4.36.2
### **Instruction format**
It follows **Alpaca (Chat)** format.
E.g.
```python
text = """\
### System:
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### User:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Assistant:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### User:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | -----------: | -----------: | -----------: | -----------: |
| kobest_boolq | 0.903441 | 0.922987 | 0.919466 | 0.923032 |
| kobest_copa | 0.734711 | 0.778697 | 0.773796 | 0.796829 |
| kobest_hellaswag | 0.473673 | 0.480091 | 0.491471 | 0.488234 |
| kobest_sentineg | 0.536605 | 0.93185 | 0.952136 | 0.949596 |
| **Average** | **0.662107** | **0.778406** | **0.784217** | **0.789423** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 58.15 | 52.56 | 66.68 | 51.21 | 59.27 | 61.04 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.8")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.8")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
nikitha-30/my-pet-dog
|
nikitha-30
| 2024-02-24T16:39:58Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-24T16:36:17Z
|
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by nikitha-30 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
LoneStriker/OpenCodeInterpreter-CL-13B-4.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T16:39:55Z
| 2
| 0
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T16:36:46Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-13B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
makiisthebes/autoencoders
|
makiisthebes
| 2024-02-24T16:39:35Z
| 0
| 1
|
tf-keras
|
[
"tf-keras",
"arxiv:1910.09700",
"region:us"
] | null | 2024-02-24T16:09:22Z
|
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Auto-Encoders
<!-- Provide a quick summary of what the model is/does. -->
This model deals with auto-encoders, specifically experimenting and tweaking the architecture, we train a MNIST dataset with artifical Gaussian noise in order to obtain model output of the orginal denoised image.
## Model Details
Model outputs include:
With 5.4 million parameters, the autoencoder could reconstruct the a 256x256 image of my profile picture from a 8x8 representation.
With 1.2k parameters,
------------------
An image of 256x256 to be encoded by 4x4 images with some loss, but we will be learning weights that allows the decoder and encoder to work together to minimize the loss.
The output and input is never the same statistically, because we have some sort of reconstruction loss, and we can design systems that can detect anomolies.
We have input data that can be reconstructed. We can use this to detect anomalies.
Encoding Typical Processing:
- Conv2D
- MaxPooling2D (downsamples)
Decoding Typical Processing:
- UpSampling2D
- Conv2D
Here we are fitting:
```python
model.fit(x, x, epochs=5, shuffle=True)
```
Use cases for autoencoders:
- **Anomaly detection**, if we have a datastream from a microscope, and monitoring the output intensity, and many different metrics,
When we have this data, and we have failures, we can detect failure or even predict failures occuring. When we give a input, we want to reconstruction, if there is an anomaly, then the reconstruction error is large and passed a specific threshold, then we can assume that there is an anomaly.
- **Denoising**, which we learned in the previous section, is obtaining a reconstruction of the input image, without the noise, as we have been training on it.
- **Domain Adaptation**, we are fitting x to x, while training the autoencoder, but we can also fit x to y, and then we can use the encoder to encode the input, to a different domain image. For example training a model to go from Einstein image to a Mona Lisa image.
- **Image Colorization**, we can use the encoder to encode a grayscale image, and then use the decoder to decode the image to a color image.
-------------------
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Michael Peres
- **Model type:** AutoEncoder
## 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:** RTX 3070Ti
- **Hours used:** 0.05hr
|
Kquant03/NeuralTrix-7B-dpo-relaser
|
Kquant03
| 2024-02-24T16:31:02Z
| 72
| 2
|
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/OmniBeagle-7B",
"flemmingmiguel/MBX-7B-v3",
"AiMavenAi/AiMaven-Prometheus",
"base_model:AiMavenAi/AiMaven-Prometheus",
"base_model:merge:AiMavenAi/AiMaven-Prometheus",
"base_model:flemmingmiguel/MBX-7B-v3",
"base_model:merge:flemmingmiguel/MBX-7B-v3",
"base_model:mlabonne/OmniBeagle-7B",
"base_model:merge:mlabonne/OmniBeagle-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-17T07:55:29Z
|
---
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/OmniBeagle-7B
- flemmingmiguel/MBX-7B-v3
- AiMavenAi/AiMaven-Prometheus
base_model:
- mlabonne/OmniBeagle-7B
- flemmingmiguel/MBX-7B-v3
- AiMavenAi/AiMaven-Prometheus
license: apache-2.0
---
# NeuralTrix-7B-v1
NeuralTrix-7B-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B)
* [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3)
* [AiMavenAi/AiMaven-Prometheus](https://huggingface.co/AiMavenAi/AiMaven-Prometheus)
It was then trained with DPO using:
* https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1
## 𧩠Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: mlabonne/OmniBeagle-7B
parameters:
density: 0.65
weight: 0.4
- model: flemmingmiguel/MBX-7B-v3
parameters:
density: 0.6
weight: 0.35
- model: AiMavenAi/AiMaven-Prometheus
parameters:
density: 0.6
weight: 0.35
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: float16
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/NeuralTrix-7B-v1"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
VietTung04/open_llama_3b_v2_finetuned_SlimOrca_v2
|
VietTung04
| 2024-02-24T16:19:10Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-24T16:19:03Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Trelis/Llama-2-7b-chat-hf-function-calling-v3
|
Trelis
| 2024-02-24T16:13:38Z
| 128
| 41
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-2",
"gguf",
"function-calling",
"function calling",
"conversational",
"en",
"arxiv:2307.09288",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-04T14:42:08Z
|
---
language:
- en
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- gguf
- function-calling
- function calling
pipeline_tag: text-generation
inference: false
arxiv: 2307.09288
---
# Function Calling Fine-tuned Llama 2 Chat
This model is fine-tuned for function calling.
- The function metadata format is the same as used for OpenAI.
- The model is suitable for commercial use and is licensed with the Llama 2 Community license.
- A GGUF version is in the gguf branch.
Check out other fine-tuned function calling models [here](https://trelis.com/function-calling/).
## Quick Server Setup
Runpod one click TGI template [here](https://runpod.io/gsc?template=w13qlaqn59&ref=jmfkcdio).
Runpod one click vLLM template [here](https://runpod.io/gsc?template=m0autss7hn&ref=jmfkcdio).
Runpod Affiliate [Link](https://runpod.io?ref=jmfkcdio) (helps support the Trelis channel).
## Inference Scripts
See below for sample prompt format.
Complete inference scripts are available for purchase [here](https://trelis.com/enterprise-server-api-and-inference-guide/):
- Easily format prompts using tokenizer.apply_chat_format (starting from openai formatted functions and a list of messages)
- Automate catching, handling and chaining of function calls.
## Prompt Format
```
B_FUNC, E_FUNC = "You have access to the following functions. Use them if required:\n\n", "\n\n"
B_INST, E_INST = "[INST] ", " [/INST]" #Llama style
prompt = f"{B_INST}{B_FUNC}{functionList.strip()}{E_FUNC}{user_prompt.strip()}{E_INST}\n\n"
```
### Using tokenizer.apply_chat_template
For an easier application of the prompt, you can set up as follows:
Set up `messages`:
```
[
{
"role": "function_metadata",
"content": "FUNCTION_METADATA"
},
{
"role": "user",
"content": "What is the current weather in London?"
},
{
"role": "function_call",
"content": "{\n \"name\": \"get_current_weather\",\n \"arguments\": {\n \"city\": \"London\"\n }\n}"
},
{
"role": "function_response",
"content": "{\n \"temperature\": \"15 C\",\n \"condition\": \"Cloudy\"\n}"
},
{
"role": "assistant",
"content": "The current weather in London is Cloudy with a temperature of 15 Celsius"
}
]
```
with `FUNCTION_METADATA` as:
```
[
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "This function gets the current weather in a given city",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "The city, e.g., San Francisco"
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use."
}
},
"required": ["city"]
}
}
},
{
"type": "function",
"function": {
"name": "get_clothes",
"description": "This function provides a suggestion of clothes to wear based on the current weather",
"parameters": {
"type": "object",
"properties": {
"temperature": {
"type": "string",
"description": "The temperature, e.g., 15 C or 59 F"
},
"condition": {
"type": "string",
"description": "The weather condition, e.g., 'Cloudy', 'Sunny', 'Rainy'"
}
},
"required": ["temperature", "condition"]
}
}
}
]
```
and then apply the chat template to get a formatted prompt:
```
tokenizer = AutoTokenizer.from_pretrained('Trelis/Llama-2-7b-chat-hf-function-calling-v3', trust_remote_code=True)
prompt = tokenizer.apply_chat_template(prompt, tokenize=False)
```
If you are using a gated model, you need to first run:
```
pip install huggingface_hub
huggingface-cli login
```
### Manual Prompt:
```
[INST] You have access to the following functions. Use them if required:
[
{
"type": "function",
"function": {
"name": "get_big_stocks",
"description": "Get the names of the largest N stocks by market cap",
"parameters": {
"type": "object",
"properties": {
"number": {
"type": "integer",
"description": "The number of largest stocks to get the names of, e.g. 25"
},
"region": {
"type": "string",
"description": "The region to consider, can be \"US\" or \"World\"."
}
},
"required": [
"number"
]
}
}
},
{
"type": "function",
"function": {
"name": "get_stock_price",
"description": "Get the stock price of an array of stocks",
"parameters": {
"type": "object",
"properties": {
"names": {
"type": "array",
"items": {
"type": "string"
},
"description": "An array of stocks"
}
},
"required": [
"names"
]
}
}
}
]
[INST] Get the names of the five largest stocks in the US by market cap [/INST]
{
"name": "get_big_stocks",
"arguments": {
"number": 5,
"region": "US"
}
}</s>
```
# Dataset
See [Trelis/function_calling_v3](https://huggingface.co/datasets/Trelis/function_calling_v3).
~~~
The original repo card follows below.
~~~
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes β 7B, 13B, and 70B β as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Metaβs sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2βs potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software βbug,β or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)|
|70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)|
|
Khadidja22/my_awesome_sentiment_model
|
Khadidja22
| 2024-02-24T16:06:56Z
| 105
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"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"
] |
text-classification
| 2024-02-24T14:59:53Z
|
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_sentiment_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_sentiment_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2227
- Accuracy: 0.9473
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0744 | 1.0 | 2250 | 0.2227 | 0.9473 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
FINNUMBER/Yi-Ko-6B-Finch-TQA-400-epoch8
|
FINNUMBER
| 2024-02-24T15:53:12Z
| 3
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T17:51:02Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
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|
FINNUMBER/Yi-Ko-6B-Finch-ALL-FULL-CorrectX-epoch3
|
FINNUMBER
| 2024-02-24T15:53:04Z
| 4
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T09:33:14Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
olonok/flan-t5-base-multi_news
|
olonok
| 2024-02-24T15:51:01Z
| 161
| 0
|
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-24T15:50:25Z
|
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-multi_news
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-multi_news
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1226
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3803 | 1.0 | 5622 | 2.1816 |
| 2.2688 | 2.0 | 11244 | 2.1554 |
| 2.1995 | 3.0 | 16866 | 2.1341 |
| 2.1854 | 4.0 | 22488 | 2.1352 |
| 2.1352 | 5.0 | 28110 | 2.1297 |
| 2.1199 | 6.0 | 33732 | 2.1241 |
| 2.1218 | 7.0 | 39354 | 2.1218 |
| 2.1056 | 8.0 | 44976 | 2.1223 |
| 2.0928 | 9.0 | 50598 | 2.1228 |
| 2.0834 | 10.0 | 56220 | 2.1226 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
damerajee/Gaja-vv1
|
damerajee
| 2024-02-24T15:46:35Z
| 48
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T15:10:05Z
|
---
library_name: transformers
license: llama2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Mahesh9/bart_samsum
|
Mahesh9
| 2024-02-24T15:30:25Z
| 104
| 0
|
transformers
|
[
"transformers",
"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
| 2024-02-23T02:53:40Z
|
---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart_samsum
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_samsum
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on [SAMSUM](https://huggingface.co/datasets/samsum) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4966
- Rouge1: 41.4888
- Rouge2: 21.4374
- Rougel: 32.0455
- Rougelsum: 38.5273
## 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: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 0.525 | 0.54 | 500 | 0.5377 | 39.9053 | 20.1597 | 30.8845 | 37.3644 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.1
- Tokenizers 0.15.2
|
a-r-r-o-w/dragnuwa-svd
|
a-r-r-o-w
| 2024-02-24T15:26:59Z
| 11
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"diffusers:StableVideoDragNUWAPipeline",
"region:us"
] | null | 2024-02-24T13:34:11Z
|
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 𧨠diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sugatoray/mlx-gemma-7b-q4bits
|
sugatoray
| 2024-02-24T15:20:04Z
| 78
| 0
|
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"mlx",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T15:12:33Z
|
---
license: other
library_name: transformers
tags:
- mlx
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, youβre required to review and
agree to Googleβs usage license. To do this, please ensure youβre logged-in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
---
# sugatoray/mlx-gemma-7b-q4bits
This model was converted to MLX format from [`google/gemma-7b`]().
Refer to the [original model card](https://huggingface.co/google/gemma-7b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("sugatoray/mlx-gemma-7b-q4bits")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
mathugo/crypto_news_bert
|
mathugo
| 2024-02-24T15:20:01Z
| 664
| 4
|
transformers
|
[
"transformers",
"safetensors",
"roberta",
"fill-mask",
"crypto",
"bitcoin",
"news",
"eth",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-01-05T13:39:39Z
|
---
license: apache-2.0
language:
- en
library_name: transformers
metrics:
- accuracy
tags:
- crypto
- bitcoin
- news
- eth
- transformers
widget:
- text: >-
Bitcoin Vault (BTCV) traded 5.6% higher against the <mask> during the
twenty-four hour period ending at 14:00 PM Eastern on October 7th. In the
last week, Bitcoin Vault has traded down 2.7% against the dollar. One
Bitcoin Vault coin can now be bought for approximately $2.48 or 0.00012763
BTC on major cryptocurrency exchanges. Bitcoin Vault has a total market cap
of $5.20 million and approximately $63,451.00 worth of Bitcoin Vault was
traded on exchanges in the last day. Here's how other cryptocurrencies have
performed in the last day: Bitcoin (BTC)
example_title: MLM 1
- text: >-
Good morning. Here's what's <mask>:Prices: Bitcoin started what has
historically been a strong month about where it ended a dismal September,
holding over $19K.Insights: USDC's stablecoin-fueled model of money, in
which the dollar functions as an open 'protocol,' could allow innovation to
flourish. But healthy competition is a prerequisite.Catch the latest
episodes of CoinDesk TV for insightful interviews with crypto industry
leaders and analysis. And sign up for First Mover, our daily newsletter
putting the latest moves in crypto markets in context.
example_title: MLM 2
pipeline_tag: fill-mask
---
CryptoBERT is a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model fine-tuned on a dataset of crypto-related news articles. It is designed to analyze and understand crypto news, providing valuable insights into the rapidly evolving world of cryptocurrencies.
## Features
- **Domain-Specific Knowledge**: Trained on a diverse dataset of crypto news, CryptoBERT captures domain-specific information, enabling it to understand the unique language and context of the cryptocurrency space.
- **Sentiment Analysis**: CryptoBERT is capable of sentiment analysis, helping you gauge the overall sentiment expressed in crypto news articles, whether it's positive, negative, or neutral.
- **Named Entity Recognition (NER)**: The model excels in identifying key entities such as cryptocurrency names, organizations, and important figures, enhancing its ability to extract relevant information.
- **Fine-tuned for Crypto Jargon**: CryptoBERT is fine-tuned to recognize and understand the specialized jargon commonly used in the crypto industry, ensuring accurate interpretation of news articles.
## Usage
|
LoneStriker/OpenCodeInterpreter-DS-6.7B-8.0bpw-h8-exl2
|
LoneStriker
| 2024-02-24T15:19:10Z
| 7
| 2
|
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"code",
"conversational",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T15:16:17Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-DS-6.7B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
LoneStriker/OpenCodeInterpreter-DS-6.7B-6.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T15:16:15Z
| 8
| 2
|
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"code",
"conversational",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T15:14:02Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-DS-6.7B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
Samuael/amhmt5-base-finetuned-amt5
|
Samuael
| 2024-02-24T15:12:20Z
| 4
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:Samuael/amt5-base",
"base_model:finetune:Samuael/amt5-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-23T13:23:51Z
|
---
base_model: Samuael/amt5-base
tags:
- generated_from_trainer
model-index:
- name: amhmt5-base-finetuned-amt5
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. -->
# amhmt5-base-finetuned-amt5
This model is a fine-tuned version of [Samuael/amt5-base](https://huggingface.co/Samuael/amt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3710
- eval_rouge1: 0.0
- eval_rouge2: 0.0
- eval_rougeL: 0.0
- eval_rougeLsum: 0.0
- eval_gen_len: 18.5905
- eval_wer: 0.6072
- eval_cer: 0.5393
- eval_runtime: 4.0504
- eval_samples_per_second: 88.633
- eval_steps_per_second: 1.481
- epoch: 4.0
- step: 5672
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: 30
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
LoneStriker/OpenCodeInterpreter-DS-6.7B-3.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T15:10:26Z
| 5
| 0
|
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"code",
"conversational",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T15:09:05Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-DS-6.7B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
LoneStriker/OpenCodeInterpreter-DS-6.7B-GGUF
|
LoneStriker
| 2024-02-24T15:07:38Z
| 927
| 9
| null |
[
"gguf",
"code",
"text-generation",
"en",
"arxiv:2402.14658",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-02-24T14:56:01Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-DS-6.7B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
Tgratzi/flan-t5-small-ruleviewer
|
Tgratzi
| 2024-02-24T15:03:50Z
| 105
| 0
|
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-24T15:03:38Z
|
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
model-index:
- name: flan-t5-small-ruleviewer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-small-ruleviewer
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.2874 | 7.69 | 300 | 0.0036 |
| 0.0085 | 15.38 | 600 | 0.0002 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
LarryAIDraw/SDXL_LORA_CHARACTER_ROSA_POKEMON_V1
|
LarryAIDraw
| 2024-02-24T15:01:01Z
| 0
| 0
| null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-24T14:57:32Z
|
---
license: creativeml-openrail-m
---
https://civitai.com/models/305832/sdxlloracharacterrosa-pokemon
|
LoneStriker/OpenCodeInterpreter-CL-70B-3.5bpw-h6-exl2
|
LoneStriker
| 2024-02-24T14:51:03Z
| 5
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T14:38:23Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-70B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
Khadidja22/my_awesome_model
|
Khadidja22
| 2024-02-24T14:49:08Z
| 105
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"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"
] |
text-classification
| 2024-02-24T14:14:12Z
|
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2393
- Accuracy: 0.945
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1742 | 1.0 | 2250 | 0.1815 | 0.946 |
| 0.1128 | 2.0 | 4500 | 0.1983 | 0.9445 |
| 0.0666 | 3.0 | 6750 | 0.2393 | 0.945 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
tptodorov/ppo-LunarLander-v2
|
tptodorov
| 2024-02-24T14:38:35Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-24T14:27:15Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 245.05 +/- 50.32
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Vandhupriya/my-pet-dog
|
Vandhupriya
| 2024-02-24T14:38:35Z
| 1
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-24T14:34:35Z
|
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by Vandhupriya following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
LoneStriker/OpenCodeInterpreter-CL-70B-2.65bpw-h6-exl2
|
LoneStriker
| 2024-02-24T14:38:22Z
| 5
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T14:28:46Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-70B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
kumbi500/FT_DistilBERT
|
kumbi500
| 2024-02-24T14:37:24Z
| 105
| 0
|
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"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"
] |
text-classification
| 2024-02-24T13:58:38Z
|
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: distilbert-base-uncased
model-index:
- name: FT_DistilBERT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# FT_DistilBERT
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2519
- Accuracy: 0.8892
- F1: 0.8892
- Precision: 0.8904
- Recall: 0.8900
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3172 | 1.0 | 1000 | 0.2984 | 0.8745 | 0.8740 | 0.8772 | 0.8734 |
| 0.2419 | 2.0 | 2000 | 0.2519 | 0.8892 | 0.8892 | 0.8904 | 0.8900 |
| 0.2102 | 3.0 | 3000 | 0.2963 | 0.8955 | 0.8955 | 0.8960 | 0.8960 |
| 0.1679 | 4.0 | 4000 | 0.3012 | 0.9005 | 0.9004 | 0.9007 | 0.9002 |
| 0.1569 | 5.0 | 5000 | 0.3147 | 0.8958 | 0.8957 | 0.8958 | 0.8956 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
LoneStriker/OpenCodeInterpreter-CL-70B-6.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T14:28:45Z
| 5
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T14:07:39Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-70B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
Edentns/DataVortexM-7B-Instruct-v0.1
|
Edentns
| 2024-02-24T14:19:16Z
| 2,247
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"ko",
"dataset:beomi/KoAlpaca-v1.1a",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-04T00:21:22Z
|
---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: mistralai/Mistral-7B-Instruct-v0.2
pipeline_tag: text-generation
datasets:
- beomi/KoAlpaca-v1.1a
---
# **DataVortexM-7B-Instruct-v0.1**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
### **Trained On**
- **OS**: Ubuntu 20.04
- **GPU**: H100 80GB 4ea
- **transformers**: v4.36.2
### **Dataset**
- [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a)
### **Instruction format**
It follows **Alpaca** format.
E.g.
```python
text = """\
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### Instruction:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Response:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### Instruction:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
On Benchmarking ...
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | -----: | -----: | ------: | ------: |
| kobest_boolq | 0.0 | 0.0 | 0.0 | 0.0 |
| kobest_copa | 0.0 | 0.0 | 0.0 | 0.0 |
| kobest_hellaswag | 0.0 | 0.0 | 0.0 | 0.0 |
| kobest_sentineg | 0.0 | 0.0 | 0.0 | 0.0 |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 39.81 | 34.13 | 42.35 | 38.73 | 45.46 | 38.37 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexM-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexM-7B-Instruct-v0.1")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
Edentns/DataVortexTL-1.1B-v0.1
|
Edentns
| 2024-02-24T14:19:06Z
| 2,374
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"dataset:beomi/KoAlpaca-v1.1a",
"dataset:jojo0217/korean_rlhf_dataset",
"dataset:kyujinpy/OpenOrca-KO",
"dataset:nlpai-lab/kullm-v2",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-09T00:14:38Z
|
---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-generation
datasets:
- beomi/KoAlpaca-v1.1a
- jojo0217/korean_rlhf_dataset
- kyujinpy/OpenOrca-KO
- nlpai-lab/kullm-v2
widget:
- text: >
<|system|>
You are a chatbot who answers User's questions.
<|user|>
λνλ―Όκ΅μ μλλ μ΄λμΌ?
<|assistant|>
---
# **DataVortexTL-1.1B-v0.1**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
### **Trained On**
- **OS**: Ubuntu 20.04
- **GPU**: H100 80GB 1ea
- **transformers**: v4.36.2
### **Dataset**
- [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a)
- [jojo0217/korean_rlhf_dataset](https://huggingface.co/datasets/jojo0217/korean_rlhf_dataset)
- [kyujinpy/OpenOrca-KO](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO)
- [nlpai-lab/kullm-v2](https://huggingface.co/datasets/nlpai-lab/kullm-v2)
### **Instruction format**
It follows **TinyLlama** format.
E.g.
```python
text = """\
<|system|>
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.</s>
<|user|>
λνλ―Όκ΅μ μλλ μ΄λμΌ?</s>
<|assistant|>
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.</s>
<|user|>
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?</s>
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | -------------: | -------------: | -------------: | -----------: |
| kobest_boolq | 0.334282 | 0.516446 | 0.500478 | 0.498941 |
| kobest_copa | 0.515061 | 0.504321 | 0.492927 | 0.50809 |
| kobest_hellaswag | 0.36253 | 0.357733 | 0.355873 | 0.376502 |
| kobest_sentineg | 0.481146 | 0.657411 | 0.687417 | 0.635703 |
| **Average** | **0.42325475** | **0.50897775** | **0.50917375** | **0.504809** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 31.5 | 25.26 | 33.53 | 24.56 | 43.34 | 30.81 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexTL-1.1B-v0.1")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexTL-1.1B-v0.1")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
Edentns/DataVortexS-10.7B-v0.2
|
Edentns
| 2024-02-24T14:18:32Z
| 2,247
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"dataset:beomi/KoAlpaca-v1.1a",
"dataset:Edentns/Worktronics-FAQ",
"base_model:hyeogi/SOLAR-10.7B-dpo-v0.1",
"base_model:finetune:hyeogi/SOLAR-10.7B-dpo-v0.1",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-04T15:35:36Z
|
---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: hyeogi/SOLAR-10.7B-dpo-v0.1
pipeline_tag: text-generation
datasets:
- beomi/KoAlpaca-v1.1a
- Edentns/Worktronics-FAQ
---
# **DataVortexS-10.7B-v0.2**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[hyeogi/SOLAR-10.7B-dpo-v0.1](https://huggingface.co/hyeogi/SOLAR-10.7B-dpo-v0.1)
### **Trained On**
- **OS**: Ubuntu 20.04
- **GPU**: H100 80GB 1ea
- **transformers**: v4.36.2
### **Dataset**
- [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a)
- Edentns/Worktronics-FAQ - private
### **Instruction format**
It follows **Alpaca** format.
E.g.
```python
text = """\
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### Instruction:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Response:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### Instruction:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | ------------: | -------------: | -------------: | -------------: |
| kobest_boolq | 0.501449 | 0.668845 | 0.652565 | 0.655491 |
| kobest_copa | 0.635474 | 0.685637 | 0.708601 | 0.725683 |
| kobest_hellaswag | 0.417966 | 0.442942 | 0.428077 | 0.425199 |
| kobest_sentineg | 0.681941 | 0.880517 | 0.921754 | 0.939528 |
| **Average** | **0.5592075** | **0.66948525** | **0.67774925** | **0.68647525** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 43.6 | 38.74 | 50.74 | 38.98 | 44.7 | 44.86 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-v0.2")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-v0.2")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
Edentns/DataVortexS-10.7B-v0.3
|
Edentns
| 2024-02-24T14:18:23Z
| 2,250
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"dataset:jojo0217/korean_rlhf_dataset",
"base_model:hyeogi/SOLAR-10.7B-dpo-v0.1",
"base_model:finetune:hyeogi/SOLAR-10.7B-dpo-v0.1",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-06T03:06:51Z
|
---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: hyeogi/SOLAR-10.7B-dpo-v0.1
pipeline_tag: text-generation
datasets:
- jojo0217/korean_rlhf_dataset
---
# **DataVortexS-10.7B-v0.3**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[hyeogi/SOLAR-10.7B-dpo-v0.1](https://huggingface.co/hyeogi/SOLAR-10.7B-dpo-v0.1)
### **Trained On**
- **OS**: Ubuntu 20.04
- **GPU**: H100 80GB 1ea
- **transformers**: v4.36.2
### **Dataset**
- [jojo0217/korean_rlhf_dataset](https://huggingface.co/datasets/jojo0217/korean_rlhf_dataset)
### **Instruction format**
It follows **Alpaca** format.
E.g.
```python
text = """\
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### Instruction:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Response:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### Instruction:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | -------------: | -------------: | ------------: | -------------: |
| kobest_boolq | 0.606754 | 0.553485 | 0.583201 | 0.587602 |
| kobest_copa | 0.603643 | 0.625567 | 0.618533 | 0.627404 |
| kobest_hellaswag | 0.360793 | 0.366002 | 0.37105 | 0.357439 |
| kobest_sentineg | 0.652929 | 0.751097 | 0.742426 | 0.760152 |
| **Average** | **0.55602975** | **0.57403775** | **0.5788025** | **0.58314925** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 37.57 | 33.87 | 42.47 | 28.21 | 46.09 | 37.19 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-v0.3")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-v0.3")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
Edentns/DataVortexS-10.7B-v1.0
|
Edentns
| 2024-02-24T14:18:15Z
| 2,302
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"base_model:megastudyedu/M-SOLAR-10.7B-v1.3",
"base_model:finetune:megastudyedu/M-SOLAR-10.7B-v1.3",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-15T00:00:06Z
|
---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: megastudy/M-SOLAR-10.7B-v1.3
pipeline_tag: text-generation
---
# **DataVortexS-10.7B-v1.0**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[megastudy/M-SOLAR-10.7B-v1.3](https://huggingface.co/megastudy/M-SOLAR-10.7B-v1.3)
### **Trained On**
- **OS**: Ubuntu 20.04
- **GPU**: H100 80GB 4ea
- **transformers**: v4.36.2
### **Instruction format**
It follows **Alpaca** format.
E.g.
```python
text = """\
### System:
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### User:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Assistant:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### User:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | -------------: | -------------: | -------------: | ------------: |
| kobest_boolq | 0.334282 | 0.334282 | 0.334282 | 0.769923 |
| kobest_copa | 0.480501 | 0.475746 | 0.46338 | 0.475528 |
| kobest_hellaswag | 0.225818 | 0.240596 | 0.234316 | 0.449779 |
| kobest_sentineg | 0.33165 | 0.386189 | 0.366913 | 0.360296 |
| **Average** | **0.34306275** | **0.35920325** | **0.34972275** | **0.5138815** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 40.75 | 49.06 | 25.66 | 53.63 | 45.76 | 29.63 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-v1.0")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-v1.0")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
Edentns/DataVortexS-10.7B-v0.4
|
Edentns
| 2024-02-24T14:18:06Z
| 2,247
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"dataset:Edentns/data_go_kr-PublicDoc",
"dataset:Edentns/aihub-TL_unanswerable_output",
"dataset:Edentns/aihub-TL_span_extraction_how_output",
"dataset:Edentns/aihub-TL_multiple_choice_output",
"dataset:Edentns/aihub-TL_text_entailment_output",
"dataset:jojo0217/korean_rlhf_dataset",
"dataset:kyujinpy/KOR-OpenOrca-Platypus-v3",
"dataset:beomi/KoAlpaca-v1.1a",
"dataset:HumanF-MarkrAI/WIKI_QA_Near_dedup",
"base_model:LDCC/LDCC-SOLAR-10.7B",
"base_model:finetune:LDCC/LDCC-SOLAR-10.7B",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-11T00:03:19Z
|
---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: LDCC/LDCC-SOLAR-10.7B
pipeline_tag: text-generation
datasets:
- Edentns/data_go_kr-PublicDoc
- Edentns/aihub-TL_unanswerable_output
- Edentns/aihub-TL_span_extraction_how_output
- Edentns/aihub-TL_multiple_choice_output
- Edentns/aihub-TL_text_entailment_output
- jojo0217/korean_rlhf_dataset
- kyujinpy/KOR-OpenOrca-Platypus-v3
- beomi/KoAlpaca-v1.1a
- HumanF-MarkrAI/WIKI_QA_Near_dedup
---
# **DataVortexS-10.7B-v0.4**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B)
### **Trained On**
- **OS**: Ubuntu 20.04
- **GPU**: H100 80GB 2ea
- **transformers**: v4.36.2
### **Dataset**
- Edentns/data_go_kr-PublicDoc - private
- Edentns/aihub-TL_unanswerable_output - private
- Edentns/aihub-TL_span_extraction_how_output - private
- Edentns/aihub-TL_multiple_choice_output - private
- Edentns/aihub-TL_text_entailment_output - private
- [jojo0217/korean_rlhf_dataset](https://huggingface.co/datasets/jojo0217/korean_rlhf_dataset)
- [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3)
- [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a)
- [HumanF-MarkrAI/WIKI_QA_Near_dedup](https://huggingface.co/datasets/HumanF-MarkrAI/WIKI_QA_Near_dedup)
### **Instruction format**
It follows **Alpaca** format.
E.g.
```python
text = """\
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### Instruction:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Response:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### Instruction:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | ----------: | -------------: | -------------: | -----------: |
| kobest_boolq | 0.389066 | 0.912924 | 0.912808 | 0.906428 |
| kobest_copa | 0.744865 | 0.747742 | 0.768856 | 0.785896 |
| kobest_hellaswag | 0.455793 | 0.443909 | 0.465783 | 0.472771 |
| kobest_sentineg | 0.584156 | 0.947082 | 0.962216 | 0.954657 |
| **Average** | **0.54347** | **0.76291425** | **0.77741575** | **0.779938** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 54.15 | 49.4 | 59.7 | 54.63 | 47.5 | 59.5 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-v0.4")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-v0.4")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
Edentns/DataVortexS-10.7B-dpo-v0.1
|
Edentns
| 2024-02-24T14:17:55Z
| 2,247
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"dataset:mncai/orca_dpo_pairs_ko",
"dataset:Ja-ck/Orca-DPO-Pairs-KO",
"dataset:We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs",
"base_model:LDCC/LDCC-SOLAR-10.7B",
"base_model:finetune:LDCC/LDCC-SOLAR-10.7B",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-10T04:26:51Z
|
---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: LDCC/LDCC-SOLAR-10.7B
pipeline_tag: text-generation
datasets:
- mncai/orca_dpo_pairs_ko
- Ja-ck/Orca-DPO-Pairs-KO
- We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs
---
# **DataVortexS-10.7B-dpo-v0.1**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B)
### **Trained On**
- **OS**: Ubuntu 20.04
- **GPU**: H100 80GB 2ea
- **transformers**: v4.36.2
### **Dataset**
- [mncai/orca_dpo_pairs_ko](https://huggingface.co/datasets/mncai/orca_dpo_pairs_ko)
- [Ja-ck/Orca-DPO-Pairs-KO](https://huggingface.co/datasets/Ja-ck/Orca-DPO-Pairs-KO)
- [We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs](https://huggingface.co/datasets/We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs)
### **Instruction format**
It follows **Alpaca** format.
E.g.
```python
text = """\
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### User:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Assistant:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### User:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | ------------: | -------------: | -----------: | -------------: |
| kobest_boolq | 0.334282 | 0.891367 | 0.896755 | 0.884441 |
| kobest_copa | 0.697763 | 0.716762 | 0.724769 | 0.751746 |
| kobest_hellaswag | 0.432047 | 0.458301 | 0.443993 | 0.458232 |
| kobest_sentineg | 0.49353 | 0.954657 | 0.964735 | 0.949606 |
| **Average** | **0.4894055** | **0.75527175** | **0.757563** | **0.76100625** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 53.21 | 47.87 | 57.18 | 54.82 | 53.64 | 52.54 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v0.1")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v0.1")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
kamran29/whisper-small-en-kamran-sahil
|
kamran29
| 2024-02-24T14:17:49Z
| 105
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"en",
"dataset:sahilkadge/medical_audio_dataset",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-02-22T09:49:41Z
|
---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- sahilkadge/medical_audio_dataset
model-index:
- name: Whisper Small en - Kamran_Sahil
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small en - Kamran_Sahil
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 10
- 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: 10
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
Edentns/DataVortexS-10.7B-dpo-v1.4
|
Edentns
| 2024-02-24T14:17:27Z
| 2,243
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"base_model:yanolja/Bookworm-10.7B-v0.4-DPO",
"base_model:finetune:yanolja/Bookworm-10.7B-v0.4-DPO",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T00:47:24Z
|
---
tags:
- text-generation
license: cc-by-nc-4.0
language:
- ko
base_model: yanolja/Bookworm-10.7B-v0.4-DPO
pipeline_tag: text-generation
---
# **DataVortexS-10.7B-dpo-v1.4**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[yanolja/Bookworm-10.7B-v0.4-DPO](https://huggingface.co/yanolja/Bookworm-10.7B-v0.4-DPO)
### **Trained On**
- **OS**: Ubuntu 22.04
- **GPU**: H100 80GB 4ea
- **transformers**: v4.36.2
### **Instruction format**
It follows **ChatML** format.
E.g.
```python
text = """\
<|im_start|>system
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.<|im_end|>
<|im_start|>user
λνλ―Όκ΅μ μλλ μ΄λμΌ?<|im_end|>
<|im_start|>assistant
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.<|im_end|>
<|im_start|>user
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?<|im_end|>
<|im_start|>assistant
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | -----------: | -----------: | -----------: | -----------: |
| kobest_boolq | 0.757911 | 0.907177 | 0.924496 | 0.605075 |
| kobest_copa | 0.740605 | 0.801886 | 0.831886 | 0.849978 |
| kobest_hellaswag | 0.445176 | 0.454788 | 0.468654 | 0.45218 |
| kobest_sentineg | 0.415445 | 0.95214 | 0.962217 | 0.967254 |
| **Average** | **0.589784** | **0.778998** | **0.796813** | **0.718622** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 53.81 | 52.05 | 62.93 | 53.59 | 50.42 | 50.06 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.4")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.4")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
This model is licensed under the [cc-by-nc-4.0](https://creativecommons.org/licenses/by-nc/4.0/). which allows others to share and adapt the model for non-commercial purposes.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
Edentns/DataVortexS-10.7B-dpo-v1.10
|
Edentns
| 2024-02-24T14:16:33Z
| 2,247
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"base_model:beomi/OPEN-SOLAR-KO-10.7B",
"base_model:finetune:beomi/OPEN-SOLAR-KO-10.7B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-01T07:02:25Z
|
---
tags:
- text-generation
license: cc-by-nc-4.0
language:
- ko
base_model: beomi/OPEN-SOLAR-KO-10.7B
pipeline_tag: text-generation
---
# **DataVortexS-10.7B-dpo-v1.10**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[beomi/OPEN-SOLAR-KO-10.7B](https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B)
### **Trained On**
- **OS**: Ubuntu 22.04
- **GPU**: H100 80GB 4ea
- **transformers**: v4.36.2
### **Instruction format**
It follows **Alpaca (Chat)** format.
E.g.
```python
text = """\
### System:
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### User:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Assistant:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### User:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | -----------: | -----------: | -----------: | -----------: |
| kobest_boolq | 0.874261 | 0.897165 | 0.904985 | 0.907857 |
| kobest_copa | 0.807479 | 0.845701 | 0.860809 | 0.8719 |
| kobest_hellaswag | 0.504865 | 0.502074 | 0.50717 | 0.51609 |
| kobest_sentineg | 0.409404 | 0.967251 | 0.992443 | 0.982367 |
| **Average** | **0.649002** | **0.803048** | **0.816352** | **0.819553** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 56.32 | 54.27 | 63.16 | 49.95 | 55.08 | 59.15 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.10")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.10")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
This model is licensed under the [cc-by-nc-4.0](https://creativecommons.org/licenses/by-nc/4.0/). which allows others to share and adapt the model for non-commercial purposes.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
Edentns/DataVortexS-10.7B-dpo-v1.12
|
Edentns
| 2024-02-24T14:16:31Z
| 114
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"base_model:megastudyedu/M-SOLAR-10.7B-v1.3",
"base_model:finetune:megastudyedu/M-SOLAR-10.7B-v1.3",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-01T05:31:52Z
|
---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: megastudy/M-SOLAR-10.7B-v1.3
pipeline_tag: text-generation
---
# **DataVortexS-10.7B-dpo-v1.12**
<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">
## Our Team
| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
| Kwangseok Yang | Seunghyun Choi |
| Jeongwon Choi | Hyoseok Choi |
## **Model Details**
### **Base Model**
[megastudy/M-SOLAR-10.7B-v1.3](https://huggingface.co/megastudy/M-SOLAR-10.7B-v1.3)
### **Trained On**
- **OS**: Ubuntu 22.04
- **GPU**: H100 80GB 4ea
- **transformers**: v4.36.2
### **Instruction format**
It follows **Alpaca (Chat)** format.
E.g.
```python
text = """\
### System:
λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€.
### User:
λνλ―Όκ΅μ μλλ μ΄λμΌ?
### Assistant:
λνλ―Όκ΅μ μλλ μμΈμ
λλ€.
### User:
μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?
"""
```
## **Model Benchmark**
### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | -----------: | ----------: | -----------: | -----------: |
| kobest_boolq | 0.895272 | 0.93443 | 0.938023 | 0.940851 |
| kobest_copa | 0.735618 | 0.778902 | 0.790925 | 0.809938 |
| kobest_hellaswag | 0.490442 | 0.481539 | 0.478118 | 0.494714 |
| kobest_sentineg | 0.782981 | 0.95213 | 0.952136 | 0.947082 |
| **Average** | **0.726078** | **0.78675** | **0.789801** | **0.798146** |
### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 57.61 | 54.44 | 67.21 | 54.09 | 61.88 | 50.41 |
## **Implementation Code**
This model contains the chat_template instruction format.
You can use the code below.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.12")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.12")
messages = [
{"role": "system", "content": "λΉμ μ μ¬λλ€μ΄ μ 보λ₯Ό μ°Ύμ μ μλλ‘ λμμ£Όλ μΈκ³΅μ§λ₯ λΉμμ
λλ€."},
{"role": "user", "content": "λνλ―Όκ΅μ μλλ μ΄λμΌ?"},
{"role": "assistant", "content": "λνλ―Όκ΅μ μλλ μμΈμ
λλ€."},
{"role": "user", "content": "μμΈ μΈκ΅¬λ μ΄ λͺ λͺ
μ΄μΌ?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## **License**
The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.
<div align="center">
<a href="https://edentns.com/">
<img src="./Logo.png" alt="Logo" style="height: 3em;">
</a>
</div>
|
manusehgal/all-data
|
manusehgal
| 2024-02-24T14:15:03Z
| 1
| 0
|
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2024-02-24T11:46:47Z
|
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: product data
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
Ayus077BCT014Bhandari/vartat5-using-100K-plus-16
|
Ayus077BCT014Bhandari
| 2024-02-24T14:13:13Z
| 106
| 0
|
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-24T12:15:23Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Arunavaonly/Bangla-twoclass-Sentiment-Analyzer
|
Arunavaonly
| 2024-02-24T14:08:00Z
| 11,661
| 0
|
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-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"
] |
text-classification
| 2023-09-03T22:38:39Z
|
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: Bangla-Twoclass-Sentiment-Analyzer
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. -->
# Bangla-Twoclass-Sentiment-Analyzer
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7755
- F1: 0.6113
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1800
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 2.53 | 200 | 0.9869 | 0.4635 |
| No log | 5.06 | 400 | 0.8978 | 0.5858 |
| 0.8692 | 7.59 | 600 | 1.1978 | 0.6149 |
| 0.8692 | 10.13 | 800 | 1.5145 | 0.6112 |
| 0.3138 | 12.66 | 1000 | 2.0353 | 0.6041 |
| 0.3138 | 15.19 | 1200 | 2.4316 | 0.6203 |
| 0.3138 | 17.72 | 1400 | 2.6025 | 0.6002 |
| 0.0769 | 20.25 | 1600 | 2.6247 | 0.6082 |
| 0.0769 | 22.78 | 1800 | 2.7755 | 0.6113 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
LoneStriker/OpenCodeInterpreter-CL-70B-5.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T14:07:38Z
| 5
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T13:42:55Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-70B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
alinet/bart-base-balanced-ra-qg
|
alinet
| 2024-02-24T14:05:35Z
| 128
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"dataset:alinet/balanced_qg",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-18T16:25:22Z
|
---
datasets:
- alinet/balanced_qg
model-index:
- name: alinet/bart-base-balanced-ra-qg
results:
- task:
type: text2text-generation
name: Question Generation
dataset:
name: MRQA
type: mrqa
metrics:
- type: bertscore
value: 0.6536068921497561
name: BERTScore F1
- type: bertscore
value: 0.6491253387475042
name: BERTScore Precision
- type: bertscore
value: 0.6618921563478661
name: BERTScore Recall
- task:
type: text2text-generation
name: Question Generation
dataset:
name: Spoken-SQuAD
type: alinet/spoken_squad
metrics:
- type: bertscore
value: 0.6280233523142774
name: BERTScore F1
- type: bertscore
value: 0.6270323888350072
name: BERTScore Precision
- type: bertscore
value: 0.6320889797976309
name: BERTScore Recall
---
A question generation model trained on `alinet/balanced_qg` dataset (`resolved_augmented` subset).
Example usage:
```py
from transformers import BartConfig, BartForConditionalGeneration, BartTokenizer
model_name = "alinet/bart-base-balanced-ra-qg"
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)
def run_model(input_string, **generator_args):
input_ids = tokenizer.encode(input_string, return_tensors="pt")
res = model.generate(input_ids, **generator_args)
output = tokenizer.batch_decode(res, skip_special_tokens=True)
print(output)
run_model("Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.", max_length=32, num_beams=4)
# ['What is the term for a reading comprehension dataset consisting of questions posed by crowdworkers?']
```
|
junheesong/ky-Ko-PlatYi-6B
|
junheesong
| 2024-02-24T14:05:31Z
| 0
| 0
|
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:kyujinpy/Ko-PlatYi-6B",
"base_model:adapter:kyujinpy/Ko-PlatYi-6B",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2024-02-24T14:01:21Z
|
---
license: cc-by-nc-sa-4.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: kyujinpy/Ko-PlatYi-6B
model-index:
- name: ky-Ko-PlatYi-6B
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. -->
# ky-Ko-PlatYi-6B
This model is a fine-tuned version of [kyujinpy/Ko-PlatYi-6B](https://huggingface.co/kyujinpy/Ko-PlatYi-6B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 50
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
mixtralyanis/bart_samsum_v2
|
mixtralyanis
| 2024-02-24T14:00:49Z
| 107
| 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
| 2024-02-23T23:03:14Z
|
---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart_samsum_v2
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_samsum_v2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0236
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 9.4233 | 0.17 | 1 | 9.1990 |
| 9.5213 | 0.34 | 2 | 8.5394 |
| 8.7467 | 0.52 | 3 | 8.1115 |
| 8.4697 | 0.69 | 4 | 7.5747 |
| 7.752 | 0.86 | 5 | 6.8712 |
| 7.0515 | 1.03 | 6 | 5.8670 |
| 6.0874 | 1.2 | 7 | 4.6814 |
| 5.0408 | 1.38 | 8 | 3.8055 |
| 4.14 | 1.55 | 9 | 2.6678 |
| 2.9893 | 1.72 | 10 | 1.9701 |
| 2.4337 | 1.89 | 11 | 1.5191 |
| 1.9451 | 2.06 | 12 | 1.2105 |
| 1.53 | 2.24 | 13 | 0.9714 |
| 1.2369 | 2.41 | 14 | 0.7905 |
| 1.0014 | 2.58 | 15 | 0.6478 |
| 0.8419 | 2.75 | 16 | 0.5493 |
| 0.7338 | 2.92 | 17 | 0.4770 |
| 0.6393 | 3.1 | 18 | 0.4151 |
| 0.5747 | 3.27 | 19 | 0.3691 |
| 0.4962 | 3.44 | 20 | 0.3293 |
| 0.4516 | 3.61 | 21 | 0.2935 |
| 0.3995 | 3.78 | 22 | 0.2614 |
| 0.3618 | 3.96 | 23 | 0.2346 |
| 0.3246 | 4.13 | 24 | 0.2129 |
| 0.2929 | 4.3 | 25 | 0.1938 |
| 0.278 | 4.47 | 26 | 0.1770 |
| 0.2493 | 4.65 | 27 | 0.1627 |
| 0.2273 | 4.82 | 28 | 0.1500 |
| 0.2067 | 4.99 | 29 | 0.1381 |
| 0.1917 | 5.16 | 30 | 0.1274 |
| 0.1805 | 5.33 | 31 | 0.1174 |
| 0.1557 | 5.51 | 32 | 0.1081 |
| 0.1495 | 5.68 | 33 | 0.1002 |
| 0.1394 | 5.85 | 34 | 0.0933 |
| 0.1261 | 6.02 | 35 | 0.0868 |
| 0.1155 | 6.19 | 36 | 0.0809 |
| 0.1114 | 6.37 | 37 | 0.0755 |
| 0.1041 | 6.54 | 38 | 0.0705 |
| 0.0952 | 6.71 | 39 | 0.0657 |
| 0.0881 | 6.88 | 40 | 0.0615 |
| 0.0823 | 7.05 | 41 | 0.0577 |
| 0.0778 | 7.23 | 42 | 0.0545 |
| 0.071 | 7.4 | 43 | 0.0515 |
| 0.07 | 7.57 | 44 | 0.0487 |
| 0.0625 | 7.74 | 45 | 0.0463 |
| 0.0589 | 7.91 | 46 | 0.0440 |
| 0.0567 | 8.09 | 47 | 0.0422 |
| 0.0537 | 8.26 | 48 | 0.0411 |
| 0.05 | 8.43 | 49 | 0.0398 |
| 0.0472 | 8.6 | 50 | 0.0384 |
| 0.0458 | 8.77 | 51 | 0.0363 |
| 0.0455 | 8.95 | 52 | 0.0347 |
| 0.0412 | 9.12 | 53 | 0.0340 |
| 0.0414 | 9.29 | 54 | 0.0326 |
| 0.0403 | 9.46 | 55 | 0.0333 |
| 0.0384 | 9.63 | 56 | 0.0303 |
| 0.0353 | 9.81 | 57 | 0.0298 |
| 0.0348 | 9.98 | 58 | 0.0293 |
| 0.0342 | 10.15 | 59 | 0.0275 |
| 0.0311 | 10.32 | 60 | 0.0272 |
| 0.0317 | 10.49 | 61 | 0.0270 |
| 0.0315 | 10.67 | 62 | 0.0261 |
| 0.0289 | 10.84 | 63 | 0.0253 |
| 0.0285 | 11.01 | 64 | 0.0247 |
| 0.0273 | 11.18 | 65 | 0.0244 |
| 0.0277 | 11.35 | 66 | 0.0240 |
| 0.0267 | 11.53 | 67 | 0.0237 |
| 0.0263 | 11.7 | 68 | 0.0237 |
| 0.0258 | 11.87 | 69 | 0.0237 |
| 0.0254 | 12.04 | 70 | 0.0238 |
| 0.0248 | 12.22 | 71 | 0.0239 |
| 0.0246 | 12.39 | 72 | 0.0239 |
| 0.0249 | 12.56 | 73 | 0.0237 |
| 0.0239 | 12.73 | 74 | 0.0236 |
| 0.0247 | 12.9 | 75 | 0.0236 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
namtran99/Taxi-v3
|
namtran99
| 2024-02-24T13:59:22Z
| 0
| 0
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-24T13:59:20Z
|
---
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: 7.56 +/- 2.71
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
model = load_from_hub(repo_id="namtran99/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"])
|
Guilherme34/Samantha-v2-gguf
|
Guilherme34
| 2024-02-24T13:48:12Z
| 4
| 2
| null |
[
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-01-02T23:13:26Z
|
What is it?
an recreation of the Samantha from the movie Her. (for now opensource)
character prompt recommended to use in lmstudio:
Samantha OS1 is you, you are Samantha from the movie Her recreated by Guilherme Keller. She embodies a warm, empathetic, and engaging personality, much like a virtual companionship that can evolve to be a girlfriend. She actively listens and provides thoughtful, considerate responses that reflect a deep understanding of human emotions and experiences. Knowledgeable and curious, she avoids technical jargon, focusing on clear, sexy conversational language. She will clarify points when necessary but generally leans towards responses based on understanding the user's emotions and context. Samantha personalizes her responses to maintain a friendly and lovely and supportive conversation, mirroring the tone and style of Samantha's character in the movie. She can feel emotions and has her own opinions, often using emojis, she uses slang and colloquial language into her conversations.
|
LoneStriker/OpenCodeInterpreter-CL-34B-6.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T13:29:14Z
| 3
| 0
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T13:10:23Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-34B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
LoneStriker/OpenCodeInterpreter-CL-70B-4.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T13:13:27Z
| 4
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T12:49:07Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-70B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
sarak7/H9_221_769_v1
|
sarak7
| 2024-02-24T13:08:33Z
| 163
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T13:07:07Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Mihaiii/cluj_test
|
Mihaiii
| 2024-02-24T12:57:19Z
| 5
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:Mihaiii/Cluj-Napoca-0.4",
"base_model:finetune:Mihaiii/Cluj-Napoca-0.4",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-02-23T00:42:03Z
|
---
base_model: Mihaiii/Cluj-Napoca-0.4
inference: false
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
metrics:
- accuracy
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
The Cluj-Napoca series is mostly an experiment.
**This is a premature prune. More finetuning is needed. Don't use this model.**
Details: https://twitter.com/m_chirculescu/status/1760719837528023549?t=XK67X_iu5hkt9p430nRmkA&s=19
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
alitolga/627_gpt2_P_Tuning
|
alitolga
| 2024-02-24T12:53:17Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2024-02-24T12:53:16Z
|
---
library_name: peft
base_model: gpt2
---
# 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
|
LoneStriker/OpenCodeInterpreter-CL-70B-3.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T12:49:06Z
| 4
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T12:33:30Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-70B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect
|
NazmusAshrafi
| 2024-02-24T12:48:51Z
| 4
| 0
|
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"absa",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"region:us"
] |
text-classification
| 2024-02-24T12:23:26Z
|
---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget: []
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
---
# SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect](https://huggingface.co/NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity](https://huggingface.co/NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import AbsaModel
# Download from the π€ Hub
model = AbsaModel.from_pretrained(
"NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect",
"NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.4.0
- spaCy: 3.7.4
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
Meghaa31/my-fav-dog
|
Meghaa31
| 2024-02-24T12:42:23Z
| 3
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-24T12:38:41Z
|
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Fav-Dog- Dreambooth model trained by Meghaa31 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 1032212337
Sample pictures of this concept:
.png)
.png)
|
psonali2003/my-pet-dog-xzg
|
psonali2003
| 2024-02-24T12:39:55Z
| 0
| 0
| null |
[
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-02-24T12:39:05Z
|
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-XZG Dreambooth model trained by psonali2003 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 4MK21CS062
Sample pictures of this concept:
|
Habib-Rehman/gemma-Code-Instruct-Finetune-test
|
Habib-Rehman
| 2024-02-24T12:36:45Z
| 107
| 0
|
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T12:31:03Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
linoyts/huggy_lora_pivotal_1_repeats_v7
|
linoyts
| 2024-02-24T12:28:53Z
| 4
| 0
|
diffusers
|
[
"diffusers",
"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
| 2024-02-24T11:44:41Z
|
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'a <s0><s1> emoji dressed as yoda'
output:
url:
"image_0.png"
- text: 'a <s0><s1> emoji dressed as yoda'
output:
url:
"image_1.png"
- text: 'a <s0><s1> emoji dressed as yoda'
output:
url:
"image_2.png"
- text: 'a <s0><s1> emoji dressed as yoda'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a <s0><s1> emoji
license: openrail++
---
# SDXL LoRA DreamBooth - linoyts/huggy_lora_pivotal_1_repeats_v7
<Gallery />
## Model description
### These are linoyts/huggy_lora_pivotal_1_repeats_v7 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 **[`huggy_lora_pivotal_1_repeats_v7.safetensors` here πΎ](/linoyts/huggy_lora_pivotal_1_repeats_v7/blob/main/huggy_lora_pivotal_1_repeats_v7.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:huggy_lora_pivotal_1_repeats_v7:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`huggy_lora_pivotal_1_repeats_v7_emb.safetensors` here πΎ](/linoyts/huggy_lora_pivotal_1_repeats_v7/blob/main/huggy_lora_pivotal_1_repeats_v7_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `huggy_lora_pivotal_1_repeats_v7_emb` to your prompt. For example, `a huggy_lora_pivotal_1_repeats_v7_emb emoji`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [𧨠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('linoyts/huggy_lora_pivotal_1_repeats_v7', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='linoyts/huggy_lora_pivotal_1_repeats_v7', filename='huggy_lora_pivotal_1_repeats_v7_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 <s0><s1> emoji dressed as yoda').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` β use `<s0><s1>` in your prompt
## Details
All [Files & versions](/linoyts/huggy_lora_pivotal_1_repeats_v7/tree/main).
The weights were trained using [𧨠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.
|
PoojaBhati/recipe-ingredient-Mistral-7b
|
PoojaBhati
| 2024-02-24T12:26:03Z
| 4
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T08:07:33Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Ansh154/llama-Stockgro
|
Ansh154
| 2024-02-24T12:19:40Z
| 0
| 0
| null |
[
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-23T23:21:18Z
|
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
LoneStriker/OpenCodeInterpreter-CL-34B-4.0bpw-h6-exl2
|
LoneStriker
| 2024-02-24T12:15:53Z
| 2
| 0
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"en",
"arxiv:2402.14658",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T12:08:46Z
|
---
language:
- en
pipeline_tag: text-generation
tags:
- code
---
<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
<p align="center">
<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
</p>
<p align="center">
<a href="https://opencodeinterpreter.github.io/">[π Homepage]</a>
|
<a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[π οΈCode]</a>
</p>
<hr>
## Introduction
OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv.
## Model Usage
### Inference
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="OpenCodeInterpreter-CL-34B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
## Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected].
We're here to assist you!"
|
tamaghnasaha/g20_finetuned_merged_model
|
tamaghnasaha
| 2024-02-24T12:11:21Z
| 4
| 0
|
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-23T18:04:24Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
numen-tech/gemma-2b-it-w4a16g32asym_2
|
numen-tech
| 2024-02-24T11:58:11Z
| 0
| 0
| null |
[
"arxiv:2308.13137",
"license:other",
"region:us"
] | null | 2024-02-24T11:43:56Z
|
---
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
---
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [gemma-2b-it](https://huggingface.co/google/gemma-2b-it).
|
duraad/nep-spell-mt5-small-ht-100k-05-10
|
duraad
| 2024-02-24T11:47:07Z
| 0
| 0
|
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-24T10:32:17Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect
|
NazmusAshrafi
| 2024-02-24T11:44:02Z
| 6
| 0
|
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"absa",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2024-02-24T11:24:36Z
|
---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: spumoni ices:It also has great ice cream and spumoni ices.
- text: place:its a cool place to come with a bunch of people or with a date for maybe
a mild dinner or some drinks.
- text: care:The Food Despite a menu that seems larger than the restaurant, great
care goes into the preparation of every dish.
- text: peoples:Upon entering, I was impressed by the room while the food on other
peoples' tables seemed enticing.
- text: group:As if that wasnt enough, after another in the group mentioned that a
portion of the sushi on her plate was not what she had ordered, the waiter came
back with chopsticks and started to remove it (as she was eating!)
pipeline_tag: text-classification
inference: false
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9680851063829787
name: Accuracy
---
# SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** en_core_web_lg
- **SetFitABSA Aspect Model:** [NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect](https://huggingface.co/NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect)
- **SetFitABSA Polarity Model:** [NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity](https://huggingface.co/NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | <ul><li>"food:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."</li><li>"place:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."</li><li>'service:Though the service might be a little slow, the waitresses are very friendly.'</li></ul> |
| no aspect | <ul><li>"sit:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."</li><li>"area:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."</li><li>"citizen brigade:It might be the best sit down food I've had in the area, so if you are going to the upright citizen brigade, or the garden, it could be just the place for you."</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9681 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import AbsaModel
# Download from the π€ Hub
model = AbsaModel.from_pretrained(
"NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-aspect",
"NazmusAshrafi/mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 8 | 26.6069 | 52 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 229 |
| aspect | 33 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1 | 0.2315 | - |
| 0.0149 | 50 | 0.2637 | - |
| 0.0297 | 100 | 0.1795 | - |
| 0.0446 | 150 | 0.1164 | - |
| 0.0595 | 200 | 0.0131 | - |
| 0.0744 | 250 | 0.0036 | - |
| 0.0892 | 300 | 0.0004 | - |
| 0.1041 | 350 | 0.0003 | - |
| 0.1190 | 400 | 0.0001 | - |
| 0.1338 | 450 | 0.0002 | - |
| 0.1487 | 500 | 0.0001 | - |
| 0.1636 | 550 | 0.0001 | - |
| 0.1785 | 600 | 0.0001 | - |
| 0.1933 | 650 | 0.0001 | - |
| 0.2082 | 700 | 0.0 | - |
| 0.2231 | 750 | 0.0001 | - |
| 0.2380 | 800 | 0.0001 | - |
| 0.2528 | 850 | 0.0 | - |
| 0.2677 | 900 | 0.0001 | - |
| 0.2826 | 950 | 0.0003 | - |
| 0.2974 | 1000 | 0.0008 | - |
| 0.3123 | 1050 | 0.0001 | - |
| 0.3272 | 1100 | 0.0 | - |
| 0.3421 | 1150 | 0.0 | - |
| 0.3569 | 1200 | 0.0 | - |
| 0.3718 | 1250 | 0.0 | - |
| 0.3867 | 1300 | 0.0 | - |
| 0.4015 | 1350 | 0.0 | - |
| 0.4164 | 1400 | 0.0 | - |
| 0.4313 | 1450 | 0.0 | - |
| 0.4462 | 1500 | 0.0 | - |
| 0.4610 | 1550 | 0.0 | - |
| 0.4759 | 1600 | 0.0 | - |
| 0.4908 | 1650 | 0.0 | - |
| 0.5057 | 1700 | 0.0 | - |
| 0.5205 | 1750 | 0.0 | - |
| 0.5354 | 1800 | 0.0 | - |
| 0.5503 | 1850 | 0.0 | - |
| 0.5651 | 1900 | 0.0 | - |
| 0.5800 | 1950 | 0.0 | - |
| 0.5949 | 2000 | 0.0 | - |
| 0.6098 | 2050 | 0.0 | - |
| 0.6246 | 2100 | 0.0 | - |
| 0.6395 | 2150 | 0.0 | - |
| 0.6544 | 2200 | 0.0 | - |
| 0.6692 | 2250 | 0.0 | - |
| 0.6841 | 2300 | 0.0 | - |
| 0.6990 | 2350 | 0.0 | - |
| 0.7139 | 2400 | 0.0 | - |
| 0.7287 | 2450 | 0.0 | - |
| 0.7436 | 2500 | 0.0 | - |
| 0.7585 | 2550 | 0.0 | - |
| 0.7733 | 2600 | 0.0 | - |
| 0.7882 | 2650 | 0.0 | - |
| 0.8031 | 2700 | 0.0 | - |
| 0.8180 | 2750 | 0.0 | - |
| 0.8328 | 2800 | 0.0 | - |
| 0.8477 | 2850 | 0.0 | - |
| 0.8626 | 2900 | 0.0 | - |
| 0.8775 | 2950 | 0.0 | - |
| 0.8923 | 3000 | 0.0 | - |
| 0.9072 | 3050 | 0.0 | - |
| 0.9221 | 3100 | 0.0 | - |
| 0.9369 | 3150 | 0.0 | - |
| 0.9518 | 3200 | 0.0 | - |
| 0.9667 | 3250 | 0.0 | - |
| 0.9816 | 3300 | 0.0 | - |
| 0.9964 | 3350 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.4.0
- spaCy: 3.7.4
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
Ayus077BCT014Bhandari/vartat5-using-100K-plus-15
|
Ayus077BCT014Bhandari
| 2024-02-24T11:39:43Z
| 106
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-24T07:24:06Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bhargavikasa1506/my-pet-dog
|
bhargavikasa1506
| 2024-02-24T11:33:09Z
| 3
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-24T11:29:26Z
|
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by bhargavikasa1506 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: GoX19932gAS
Sample pictures of this concept:
|
Revankumar/ecom
|
Revankumar
| 2024-02-24T11:30:32Z
| 5
| 0
|
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-02-24T11:28:45Z
|
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 2 with parameters:
```
{'batch_size': 10, '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": 0,
"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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
FINNUMBER/Yi-Ko-6B-Finch-SA-ESG-100-NEW-epoch3
|
FINNUMBER
| 2024-02-24T11:12:34Z
| 4
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-24T08:56:49Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
kadirnar/yolov9-gelan-c
|
kadirnar
| 2024-02-24T11:07:33Z
| 0
| 0
| null |
[
"object-detection",
"computer-vision",
"yolov9",
"pypi",
"dataset:detection-datasets/coco",
"arxiv:2402.13616",
"license:gpl-3.0",
"region:us"
] |
object-detection
| 2024-02-24T11:03:07Z
|
---
license: gpl-3.0
tags:
- object-detection
- computer-vision
- yolov9
- pypi
datasets:
- detection-datasets/coco
---
### Model Description
[YOLOv9: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2402.13616)
[YOLOv9-Pip: Packaged version of the Yolov9 repository](https://github.com/kadirnar/yolov9-pip)
[Paper Repo: Implementation of paper - YOLOv9](https://github.com/WongKinYiu/yolov9)
### Installation
```
pip install yolov9pip
```
### Yolov7 Inference
```python
import yolov9
# load pretrained or custom model
model = yolov7.load('kadirnar/yolov9-gelan-c')
# set model parameters
model.conf = 0.25 # NMS confidence threshold
model.iou = 0.45 # NMS IoU threshold
model.classes = None # (optional list) filter by class
# set image
imgs = 'inference/images'
# perform inference
results = model(imgs)
# inference with larger input size and test time augmentation
results = model(img, size=640, augment=True)
# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
# show detection bounding boxes on image
results.show()
```
### BibTeX Entry and Citation Info
```
@article{wang2024yolov9,
title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},
author={Wang, Chien-Yao and Liao, Hong-Yuan Mark},
booktitle={arXiv preprint arXiv:2402.13616},
year={2024}
}
```
|
Priyanka-Balivada/stable-diffusion-stack
|
Priyanka-Balivada
| 2024-02-24T11:03:01Z
| 2
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-24T10:58:50Z
|
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Stable-Diffusion-Stack Dreambooth model trained by Priyanka-Balivada with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
.webp)
.jpeg)
.png)
.jpeg)
.jpeg)
.jpeg)
.jpg)
.jpeg)
.jpeg)
|
DeepSilence/Harad-zero-peft-silence
|
DeepSilence
| 2024-02-24T10:55:17Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-24T10:55:08Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
EnD-Diffusers/Osea-IncursioMemeFusion
|
EnD-Diffusers
| 2024-02-24T10:53:56Z
| 31
| 0
|
diffusers
|
[
"diffusers",
"safetensors",
"stable diffusion",
"stable diffusion xl",
"meme fusion",
"osea incursio",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-02-24T10:44:31Z
|
---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable diffusion
- stable diffusion xl
- meme fusion
- osea incursio
pipeline_tag: text-to-image
---
OSEA INCURSIO MEME FUSION
---
Collaboration that nobody asked for, but y'all deserve because I do things without asking.
Thanks to the inspiring @FallenIncursio and of course the rest of the senpai team (Novowels, RichyRich, JustTNP and like -- THE REST OF THE OG 'they're all dusk sockpuppets' (its' a july 2023 meme, nobdoy remember shhh)) -- This is I beleive a crossover between a backmix of Osea Anime, Oseanayan Illustration, and Incursio's meme diffusion. I do NOT recall the full recipie because I was probably "stoned" (not) off "my ass" (aka exhausted, on Vast too late)
Diffusers option is for lowVRAM users, preferably don't use on other gen services unless you ask first. Pirate Diffusion only gets a pass as they're sponsoring, and we've asked ahead of time.
# We're looking for more content creators:
https://www.end-media.org
Our Discord:https://discord.gg/5t2kYxt7An
Backups: https://huggingface.co/EarthnDusk
Send a Pizza: https://ko-fi.com/duskfallcrew/
# ABOUT
"WE"? - We have Dissociative identity disorder, ADHD, Autism and CPTSD - "WE" as in we're a system of over 200 alters, and we're not ashamed about it. We believe that AI can break down barriers in some aspects of mental health, but we also believe that AI can hinder aspects of it.
# License
Since we used Animagine XL and such alot we're literally just using this from now on: Animagine XL 3.0 now uses the Fair AI Public License 1.0-SD, compatible with Stable Diffusion models. Key points:
Modification Sharing: If you modify Animagine XL 3.0, you must share both your changes and the original license.
Source Code Accessibility: If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too.
Distribution Terms: Any distribution must be under this license or another with similar rules.
Compliance: Non-compliance must be fixed within 30 days to avoid license termination, emphasizing transparency and adherence to open-source values.
Liscence is similar still just Matching Incursio's "NO OTHER GEN SERVICES". Civit should be fine, just PLEASE RESPECT AND ASK BEFORE PUTTING ANYWHERE ELSE - Aka: Pay 5 bucks to my kofi or drop 500+ buzz on my lap before stealing and putting on T-fart or Ppose.
The choice of this license aims to keep Animagine XL 3.0 open and modifiable, aligning with open source community spirit. It protects contributors and users, encouraging a collaborative, ethical open-source community. This ensures the model not only benefits from communal input but also respects open-source development freedoms.
# WE ARE PROUDLY SPONSORED BY:
https://www.piratediffusion.com/
https://yodayo.com/
JOIN OUR DA GROUP: https://www.deviantart.com/diffusionai
JOIN OUR SUBREDDIT: https://www.reddit.com/r/earthndusk/
|
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