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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- HC3 |
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- chatGPT |
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- assistant |
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datasets: |
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- pszemraj/HC3-textgen-qa |
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metrics: |
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- accuracy |
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inference: false |
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base_model: EleutherAI/pythia-6.9b-deduped |
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--- |
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# pythia-6.9b-deduped for general QA |
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<a href="https://colab.research.google.com/gist/pszemraj/e19747c911697b20f3bedf6e21dee0a5/pythia-6-9b-hc3-notebook-v2.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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This model is a fine-tuned version of [EleutherAI/pythia-6.9b-deduped](https://huggingface.co/EleutherAI/pythia-6.9b-deduped) on the pszemraj/HC3-textgen-qa dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2372 |
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- Accuracy: 0.6769 |
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- perplexity: 3.446 |
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## Model description |
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Text generation model trained on the HC3 text data of human questions + chatGPT answers. |
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### Usage |
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Install necessary packages for inference (_unless you have a big boi GPU_) |
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```bash |
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pip install -U -q transformers bitsandbytes accelerate |
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``` |
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Basic inference example: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("pszemraj/pythia-6.9b-HC3") |
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model = AutoModelForCausalLM.from_pretrained( |
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"pszemraj/pythia-6.9b-HC3", load_in_8bit=True, device_map="auto" |
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) # shards are ~4GB each, there are eight total |
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prompt = "I was wondering how much wood a woodchuck could chuck? <answer>" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate( |
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**inputs, max_new_tokens=300 |
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) # default generation config (+ 300 tokens) |
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result = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] |
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result = result.split("<end_answer>")[0].strip() |
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import pprint as pp |
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pp.pprint(result) |
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``` |
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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](https://huggingface.co/docs/transformers/generation_strategies#decoding-strategies). |
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## Intended uses & limitations |
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- **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"_ |
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- 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_) |
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## Training and evaluation data |
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```yaml |
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model-index: |
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- name: pythia-6.9b-hc3-qa-assistant |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: pszemraj/HC3-textgen-qa |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.6768941789814655 |
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``` |
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## Training procedure |
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Two epochs on the `pszemraj/HC3-textgen-qa` dataset. |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.2598 | 0.99 | 79 | 1.3291 | 0.6496 | |
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| 0.7446 | 1.99 | 158 | 1.2372 | 0.6769 | |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__pythia-6.9b-HC3) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 33.33 | |
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| ARC (25-shot) | 36.52 | |
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| HellaSwag (10-shot) | 61.76 | |
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| MMLU (5-shot) | 26.94 | |
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| TruthfulQA (0-shot) | 45.05 | |
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| Winogrande (5-shot) | 60.77 | |
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| GSM8K (5-shot) | 0.0 | |
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| DROP (3-shot) | 2.23 | |
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