---
license: mit
---

# Pythia 12B SFT

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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

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## Model Sources [optional]

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# Uses

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## Direct Use

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## Downstream Use [optional]

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## Out-of-Scope Use

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# Bias, Risks, and Limitations

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## Recommendations

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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.

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# Training Details

## Training Data

Trainining data includes 2023-02-10 openassistant unfiltered conversation tree dump

## Training Procedure 

```
deepspeed trainer_sft.py --configs defaults pythia-80 --deepspeed
```

### Preprocessing [optional]

[More Information Needed]


### Training Hyperparameters

deepspeed stage 2

config are as follows:

```
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
  loss_fn: CrossEntropyLoss
  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-80:
  learning_rate: 5e-6
  model_name: EleutherAI/pythia-12b-deduped
  weight_decay: 0.01
  max_length: 520
  warmup_steps: 1000
  gradient_checkpointing: false
  gradient_accumulation_steps: 20
  per_device_train_batch_size: 6
  per_device_eval_batch_size: 6

```

### Speeds, Sizes, Times [optional]

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# Evaluation

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## Testing Data, Factors & Metrics

### Testing Data

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### Factors

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### Metrics

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## Results

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### Summary



# Model Examination [optional]

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# Technical Specifications [optional]

## Model Architecture and Objective

Pythia 12B deduppped model

## Compute Infrastructure

Stability AWS Slurm Cluster

### Hardware

8 x A100 80G 

### Software

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# Citation [optional]

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**BibTeX:**

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**APA:**

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# Glossary [optional]

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# More Information [optional]

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# Model Card Authors [optional]

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# Model Card Contact

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