--- license: other datasets: - georgesung/wizard_vicuna_70k_unfiltered --- [](https://hf.co/QuantFactory) # QuantFactory/llama2_7b_chat_uncensored-GGUF This is quantized version of [georgesung/llama2_7b_chat_uncensored](https://huggingface.co/georgesung/llama2_7b_chat_uncensored) created using llama.cpp # Original Model Card # 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 |