---
license: apache-2.0
language:
- en
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>
---

# Pythia 3B SFT model

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

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

# Model Details

## Model Description

<!-- Provide a longer summary of what this model is. -->


- **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-3b-deduped-sft"

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-3b --deepspeed
```


### 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:
    - gsm8k_hard
    - webgpt
    - squad_v2
    - adversarial_qa
    - private_tuning
    - oa_translated
    - prosocial_dialogue
    - math_qa
    - wikihow
    - joke
    - gsm8k
    - ted_trans_en-hi
    - ted_trans_de-ja
    - ted_trans_nl-en
    - ted_trans_en-ja
    - ted_trans_en-es
    - ted_trans_en-ms
    - xsum:
        fraction: 0.5
    - cnn_dailymail:
        fraction: 0.5
    - multi_news:
        fraction: 0.5
    - tldr_news:
        fraction: 0.5
    - scitldr:
        fraction: 0.5
    - samsum:
        fraction: 0.5
    - debate_sum:
        fraction: 0.5
    - billsum:
        fraction: 0.5
    - wmt2019_zh-en:
        fraction: 0.9
    - wmt2019_ru-en:
        fraction: 0.9
    - wmt2019_de-en:
        fraction: 0.9
    - wmt2019_fr-de:
        fraction: 0.9
    - essay_instruction
    - reddit_eli5
    - reddit_askh
    - reddit_asks
  cache_dir: /fsx/home-theblackcat02/.cache
  loss_fn: CrossEntropyLoss
  eval_size:
  log_dir: "base"
  quantization: false
  seq2seqmodel: false
  poly_eps: 1.0
  fuse_gelu: true
  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-3b:
  learning_rate: 5e-6
  model_name: EleutherAI/pythia-2.8b-deduped
  weight_decay: 0.01
  max_length: 520
  warmup_steps: 1000
  gradient_checkpointing: false
  gradient_accumulation_steps: 24
  per_device_train_batch_size: 6
  per_device_eval_batch_size: 6

```

# Evaluation

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

## Testing Data, Factors & Metrics

### Testing Data

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

[More Information Needed]

### Factors

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

[More Information Needed]

### Metrics

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

[More Information Needed]

## Results

[More Information Needed]

### Summary



# Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

# Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

# Technical Specifications [optional]

## Model Architecture and Objective

[More Information Needed]

## Compute Infrastructure

[More Information Needed]

### Hardware

[More Information Needed]

### Software

[More Information Needed]

# Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

# Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

# 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]