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README.md

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+---
+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         |

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