初始化项目，由ModelHub XC社区提供模型

Model: georgesung/llama3_8b_chat_uncensored
Source: Original Platform

README.md

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+---
+license: other
+datasets:
+- georgesung/wizard_vicuna_70k_unfiltered
+---
+
+# Overview
+Fine-tuned [Llama-3 8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) with an uncensored/unfiltered Wizard-Vicuna conversation dataset.
+Used QLoRA for fine-tuning.
+
+The model here includes the fp32 HuggingFace version, plus a quantized 4-bit q4_0 [gguf version](https://huggingface.co/georgesung/llama3_8b_chat_uncensored/resolve/main/llama3_8b_chat_uncensored_q4_0.gguf?download=true).
+
+# 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/llama3_8b_chat_uncensored.yaml
+```
+
+# Fine-tuning guide
+https://georgesung.github.io/ai/qlora-ift/
+
+# Ollama inference
+First, install [Ollama](https://ollama.com/). Based on instructions [here](https://github.com/ollama/ollama/blob/main/README.md#import-from-gguf), run the following:
+```
+cd $MODEL_DIR_OF_CHOICE
+wget https://huggingface.co/georgesung/llama3_8b_chat_uncensored/resolve/main/llama3_8b_chat_uncensored_q4_0.gguf
+```
+
+Create a file called `llama3-uncensored.modelfile` with the following:
+```
+FROM ./llama3_8b_chat_uncensored_q4_0.gguf
+TEMPLATE """{{ .System }}
+
+### HUMAN:
+{{ .Prompt }}
+
+### RESPONSE:
+"""
+PARAMETER stop "### HUMAN:"
+PARAMETER stop "### RESPONSE:"
+```
+
+Then run:
+```
+ollama create llama3-uncensored -f llama3-uncensored.modelfile
+ollama run llama3-uncensored
+```