64 lines
2.0 KiB
Markdown
64 lines
2.0 KiB
Markdown
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---
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license: other
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datasets:
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- georgesung/wizard_vicuna_70k_unfiltered
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---
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# Overview
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Fine-tuned [Llama-2 7B](https://huggingface.co/TheBloke/Llama-2-7B-fp16) with an uncensored/unfiltered Wizard-Vicuna conversation dataset (originally from [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)).
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Used QLoRA for fine-tuning. Trained for one epoch on a 24GB GPU (NVIDIA A10G) instance, took ~19 hours to train.
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The version here is the fp16 HuggingFace model.
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## GGML & GPTQ versions
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Thanks to [TheBloke](https://huggingface.co/TheBloke), he has created the GGML and GPTQ versions:
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* https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGML
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* https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GPTQ
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## Running in Ollama
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https://ollama.com/library/llama2-uncensored
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# Prompt style
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The model was trained with the following prompt style:
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```
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### HUMAN:
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Hello
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### RESPONSE:
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Hi, how are you?
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### HUMAN:
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I'm fine.
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### RESPONSE:
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How can I help you?
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...
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```
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# Training code
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Code used to train the model is available [here](https://github.com/georgesung/llm_qlora).
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To reproduce the results:
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```
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git clone https://github.com/georgesung/llm_qlora
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cd llm_qlora
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pip install -r requirements.txt
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python train.py configs/llama2_7b_chat_uncensored.yaml
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```
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# Fine-tuning guide
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https://www.georgesung.com/ai/qlora-ift
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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_georgesung__llama2_7b_chat_uncensored)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 43.39 |
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| ARC (25-shot) | 53.58 |
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| HellaSwag (10-shot) | 78.66 |
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| MMLU (5-shot) | 44.49 |
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| TruthfulQA (0-shot) | 41.34 |
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| Winogrande (5-shot) | 74.11 |
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| GSM8K (5-shot) | 5.84 |
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| DROP (3-shot) | 5.69 |
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