
# Pythia 1.4B SFT model revision 1

<!-- Provide a quick summary of what the model is/does. -->


# Model Details

## Model Description

Model was supervised fine tuned on only [Open Assistant](https://open-assistant.io/) crowd souce platform.

- **Developed by:** Open Assistant
- **Model type:** Pythia
- **Language(s) (NLP):** English
- **License:** Apache-2.0

## Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [Open Assistant](https://github.com/LAION-AI/Open-Assistant)

# 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. -->
See the example on the right

# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[just read pythia](https://huggingface.co/EleutherAI/pythia-12b#out-of-scope-use)

## 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.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "theblackcat102/pythia-1.4b-deduped-sft-r1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).half().eval().cuda()

input_text = "<human>What's the earth population?<bot>"
inputs = tokenizer(input_text, return_tensors="pt", padding=True).to(0)
outputs = model.generate(
    **inputs,
    early_stopping=True,
    max_new_tokens=args.max_new_tokens,
    do_sample=True,
    top_k=args.top_k,
    temperature=args.temperature,
    pad_token_id=tokenizer.eos_token_id,
    # dialogue_collator.py line 36
)
output = tokenizer.decode(outputs[0], truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"])
print(output)
```

# Training Details

## Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

## Training Procedure 

```
deepspeed trainer_sft.py --configs defaults pythia-1-4b-ost --deepspeed
```

This model was trained for 200 iterations. After 200 iterations the accuracy started to drop and loss increasing which is a sign of overfitting.

### Training Hyperparameters

```
defaults:
  learning_rate: 1e-5
  gradient_checkpointing: false
  gradient_accumulation_steps: 32
  per_device_train_batch_size: 2
  per_device_eval_batch_size: 2
  weight_decay: 0.00
  warmup_steps: 600
  eval_steps: 250
  save_steps: 250
  max_length: 512
  num_train_epochs: 2
  logging_steps: 10
  max_grad_norm: 2.0
  save_total_limit: 4
  fp16: true
  eval_accumulation_steps:
  freeze_layer:
  datasets:
    - oa_private:
        data_path: .cache
        split: sft
        val_split: 0.01
        fraction: 1
        file: 2023-02-26_oasst_default.jsonl
  cache_dir: .cache
  loss_fn: CrossEntropyLoss
  eval_size:
  log_dir: "base"
  quantization: false
  seq2seqmodel: false
  poly_eps: 1.0
  fuse_gelu: false
  log_wandb: true
  samples_mixing: true # uses collator that mixes samples in the batch to create a single sample with possible multiple tasks within
  verbose: false


pythia-1-4b-ost:
  learning_rate: 1e-6
  model_name: EleutherAI/pythia-1.4b-deduped
  weight_decay: 0.01
  max_length: 1024
  warmup_steps: 100
  gradient_checkpointing: false
  gradient_accumulation_steps: 12
  per_device_train_batch_size: 5
  per_device_eval_batch_size: 6
  eval_steps: 100
  save_steps: 100
  num_train_epochs: 50
  save_total_limit: 4
```


# Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

## Testing Data, Factors & Metrics

### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

## Results



### 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]

# Acknowledgements

- [LAION](https://laion.ai/) & EleutherAI
- [Stability.ai](https://stability.ai/) : this project wouldn't be possible without their compute resource
- [Teams and contributors at Open Assistant](https://github.com/LAION-AI/Open-Assistant/graphs/contributors) : who put their time after their day job or whatever into this project
- [Huggingface](https://huggingface.co/) : For the storage and spaces here

# Model Card Authors [optional]

[More Information Needed]

# Model Card Contact

[More Information Needed]
{"language": ["en"], "license": "apache-2.0", "tags": ["sft"], "pipeline_tag": "text-generation", "widget": [{"text": "<prefix>You are a helpful assistant model trained by LAION called Aki</prefix><human>Hi, how are you?<bot>"}, {"text": "<human>What's the Earth total population<bot>"}, {"text": "<human>Write a story about future of AI development<bot>"}]}
transformers pytorch gpt_neox text-generation sft en arxiv:1910.09700 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt_neox #text-generation #sft #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# Pythia 1.4B SFT model revision 1




# Model Details

## Model Description

Model was supervised fine tuned on only Open Assistant crowd souce platform.

- Developed by: Open Assistant
- Model type: Pythia
- Language(s) (NLP): English
- License: Apache-2.0

## Model Sources [optional]



- Repository: Open Assistant

# Uses




## Direct Use


See the example on the right

# Bias, Risks, and Limitations



just read pythia

## Recommendations



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.



# Training Details

## Training Data



## Training Procedure 



This model was trained for 200 iterations. After 200 iterations the accuracy started to drop and loss increasing which is a sign of overfitting.

### Training Hyperparameters




# Evaluation



## Testing Data, Factors & Metrics

### Testing Data





### Factors





### Metrics





## Results



### Summary



# Model Examination [optional]





# Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: 

# Technical Specifications [optional]

## Model Architecture and Objective



## Compute Infrastructure



### Hardware



### Software



[optional]



BibTeX:



APA:



# Glossary [optional]





# Acknowledgements

- LAION & EleutherAI
- URL : this project wouldn't be possible without their compute resource
- Teams and contributors at Open Assistant : who put their time after their day job or whatever into this project
- Huggingface : For the storage and spaces here

# Model Card Authors [optional]



# Model Card Contact


# Pythia 1.4B SFT model revision 1 # Model Details ## Model Description

Model was supervised fine tuned on only Open Assistant crowd souce platform.

- Developed by: Open Assistant
- Model type: Pythia
- Language(s) (NLP): English
- License: Apache-2.0 ## Model Sources [optional]



- Repository: Open Assistant # Uses ## Direct Use


See the example on the right # Bias, Risks, and Limitations



just read pythia ## Recommendations



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. # Training Details ## Training Data ## Training Procedure 



This model was trained for 200 iterations. After 200 iterations the accuracy started to drop and loss increasing which is a sign of overfitting. ### Training Hyperparameters # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results ### Summary # Model Examination [optional] # Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



[optional]



BibTeX:



APA: # Glossary [optional] # Acknowledgements

- LAION & EleutherAI
- URL : this project wouldn't be possible without their compute resource
- Teams and contributors at Open Assistant : who put their time after their day job or whatever into this project
- Huggingface : For the storage and spaces here # Model Card Authors [optional] # Model Card Contact
TAGS
#transformers #pytorch #gpt_neox #text-generation #sft #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Pythia 1.4B SFT model revision 1 # Model Details ## Model Description

Model was supervised fine tuned on only Open Assistant crowd souce platform.

- Developed by: Open Assistant
- Model type: Pythia
- Language(s) (NLP): English
- License: Apache-2.0 ## Model Sources [optional]



- Repository: Open Assistant # Uses ## Direct Use


See the example on the right # Bias, Risks, and Limitations



just read pythia ## Recommendations



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. # Training Details ## Training Data ## Training Procedure 



This model was trained for 200 iterations. After 200 iterations the accuracy started to drop and loss increasing which is a sign of overfitting. ### Training Hyperparameters # Evaluation ## Testing Data, Factors & Metrics ### Testing Data ### Factors ### Metrics ## Results ### Summary # Model Examination [optional] # Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: # Technical Specifications [optional] ## Model Architecture and Objective ## Compute Infrastructure ### Hardware ### Software



[optional]



BibTeX:



APA: # Glossary [optional] # Acknowledgements

- LAION & EleutherAI
- URL : this project wouldn't be possible without their compute resource
- Teams and contributors at Open Assistant : who put their time after their day job or whatever into this project
- Huggingface : For the storage and spaces here # Model Card Authors [optional] # Model Card Contact
TAGS
#transformers #pytorch #gpt_neox #text-generation #sft #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Pythia 1.4B SFT model revision 1# Model Details## Model Description

Model was supervised fine tuned on only Open Assistant crowd souce platform.

- Developed by: Open Assistant
- Model type: Pythia
- Language(s) (NLP): English
- License: Apache-2.0## Model Sources [optional]



- Repository: Open Assistant# Uses## Direct Use


See the example on the right# Bias, Risks, and Limitations



just read pythia## Recommendations



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.# Training Details## Training Data## Training Procedure 



This model was trained for 200 iterations. After 200 iterations the accuracy started to drop and loss increasing which is a sign of overfitting.### Training Hyperparameters# Evaluation## Testing Data, Factors & Metrics### Testing Data### Factors### Metrics## Results### Summary# Model Examination [optional]# Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted:# Technical Specifications [optional]## Model Architecture and Objective## Compute Infrastructure### Hardware### Software



[optional]



BibTeX:



APA:# Glossary [optional]# Acknowledgements

- LAION & EleutherAI
- URL : this project wouldn't be possible without their compute resource
- Teams and contributors at Open Assistant : who put their time after their day job or whatever into this project
- Huggingface : For the storage and spaces here# Model Card Authors [optional]# Model Card Contact