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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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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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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# pythia-6.9b-deduped for general QA |
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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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## 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 |
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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(**inputs, max_new_tokens=300) # default generation config (+ 300 tokens) |
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result = tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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import pprint as pp |
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pp.pprint(result[0]) |
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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. |
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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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### 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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