| --- |
| license: other |
| datasets: |
| - georgesung/wizard_vicuna_70k_unfiltered |
| --- |
| |
| # Overview |
| 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)). |
| Used QLoRA for fine-tuning. Trained for one epoch on a 24GB GPU (NVIDIA A10G) instance, took ~19 hours to train. |
|
|
| The version here is the fp16 HuggingFace model. |
|
|
| ## GGML & GPTQ versions |
| Thanks to [TheBloke](https://huggingface.co/TheBloke), he has created the GGML and GPTQ versions: |
| * https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGML |
| * https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GPTQ |
|
|
| ## Running in Ollama |
| https://ollama.com/library/llama2-uncensored |
|
|
| # Prompt style |
| The model was trained with the following prompt style: |
| ``` |
| ### HUMAN: |
| Hello |
| |
| ### RESPONSE: |
| Hi, how are you? |
| |
| ### HUMAN: |
| I'm fine. |
| |
| ### RESPONSE: |
| How can I help you? |
| ... |
| ``` |
|
|
| # Training code |
| Code used to train the model is available [here](https://github.com/georgesung/llm_qlora). |
|
|
| To reproduce the results: |
| ``` |
| git clone https://github.com/georgesung/llm_qlora |
| cd llm_qlora |
| pip install -r requirements.txt |
| python train.py configs/llama2_7b_chat_uncensored.yaml |
| ``` |
|
|
| # Fine-tuning guide |
| https://georgesung.github.io/ai/qlora-ift/ |
|
|
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_georgesung__llama2_7b_chat_uncensored) |
|
|
| | Metric | Value | |
| |-----------------------|---------------------------| |
| | Avg. | 43.39 | |
| | ARC (25-shot) | 53.58 | |
| | HellaSwag (10-shot) | 78.66 | |
| | MMLU (5-shot) | 44.49 | |
| | TruthfulQA (0-shot) | 41.34 | |
| | Winogrande (5-shot) | 74.11 | |
| | GSM8K (5-shot) | 5.84 | |
| | DROP (3-shot) | 5.69 | |
