Pythia 12B SFT

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Model Details

Model Description

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

Model Sources [optional]

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• Repository: Open Assistant

Uses

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Direct Use

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Bias, Risks, and Limitations

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just read pythia

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.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "theblackcat102/pythia-12b-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

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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[More Information Needed]

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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[More Information Needed]

Factors

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[More Information Needed]

Metrics

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[More Information Needed]

Results

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Summary

Model Examination [optional]

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[More Information Needed]

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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Acknowledgements

• LAION & EleutherAI
• Stability.ai : 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]

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Model Card Contact

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