pythia-6.9b-deduped for general QA

This model is a fine-tuned version of EleutherAI/pythia-6.9b-deduped on the pszemraj/HC3-textgen-qa dataset. It achieves the following results on the evaluation set:

• Loss: 1.2372
• Accuracy: 0.6769
• perplexity: 3.446

Model description

Text generation model trained on the HC3 text data of human questions + chatGPT answers.

Usage

Install necessary packages for inference (unless you have a big boi GPU)

pip install -U -q transformers bitsandbytes accelerate

Basic inference example:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/pythia-6.9b-HC3")

model = AutoModelForCausalLM.from_pretrained(
    "pszemraj/pythia-6.9b-HC3", load_in_8bit=True, device_map="auto"
)  # shards are ~4GB each, there are eight total

prompt = "I was wondering how much wood a woodchuck could chuck? <answer>"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs, max_new_tokens=300
)  # default generation config (+ 300 tokens)
result = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
result = result.split("<end_answer>")[0].strip()

import pprint as pp

pp.pprint(result)

The defautl GenerationConfig uses contrastive search with top_k=4 and penalty_alpha=0.6. For more information on inference and parameters to use, see the transformers docs.

Intended uses & limitations

• Intended use: research/exploration into comparing RLHF tuning vs. "guided"/specific tuning on "quality" datasets/responses of "what the human would want as answer anyway"
• This is not trained/fine-tuned with RLHF and therefore will not be as helpful/generalizable/safe as chatGPT (outside of the fact that this model is ~30x smaller)

Training and evaluation data

model-index:
- name: pythia-6.9b-hc3-qa-assistant
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: pszemraj/HC3-textgen-qa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6768941789814655

Training procedure

Two epochs on the pszemraj/HC3-textgen-qa dataset.

Training results

Open LLM Leaderboard Evaluation Results

Detailed results can be found here