Update README.md

README.md

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 ---
+base_model: microsoft/WizardLM-2-7B
+inference: false
+language:
+- en
+model_type: mistral
 license: apache-2.0
+tags:
+- gguf
+- mistral
 ---
+
+## Description
+
+This repo contains GGUF files for the original model.
+
+### Files
+
+- [WizardLM-2-7B_Q2_K.gguf](WizardLM-2-7B_Q2_K.gguf) (2.72 GB) - smallest, significant quality loss - not recommended for most purposes
+- [WizardLM-2-7B_Q3_K_S.gguf](WizardLM-2-7B_Q3_K_S.gguf) (3.16 GB) - very small, high quality loss
+- [WizardLM-2-7B_Q3_K_M.gguf](WizardLM-2-7B_Q3_K_M.gguf) (3.52 GB) - very small, high quality loss
+- [WizardLM-2-7B_Q3_K_L.gguf](WizardLM-2-7B_Q3_K_L.gguf) (3.82 GB) - small, substantial quality loss
+- [WizardLM-2-7B_Q4_K_S.gguf](WizardLM-2-7B_Q4_K_S.gguf) (4.14 GB) - small, greater quality loss
+- [WizardLM-2-7B_Q4_K_M.gguf](WizardLM-2-7B_Q4_K_M.gguf) (4.37 GB) - medium, balanced quality - recommended
+- [WizardLM-2-7B_Q5_K_S.gguf](WizardLM-2-7B_Q5_K_S.gguf) (5 GB) - large, low quality loss - recommended
+- [WizardLM-2-7B_Q5_K_M.gguf](WizardLM-2-7B_Q5_K_M.gguf) (5.13 GB) - large, very low quality loss - recommended
+- [WizardLM-2-7B_Q6_K.gguf](WizardLM-2-7B_Q6_K.gguf) (5.94 GB) - very large, extremely low quality loss
+- [WizardLM-2-7B_Q8_0.gguf](WizardLM-2-7B_Q8_0.gguf) (7.7 GB) - very large, extremely low quality loss - not recommended
+
+## Original model description
+
+We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, 
+which have improved performance on complex chat, multilingual, reasoning and agent. 
+New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.
+
+- WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works 
+and consistently outperforms all the existing state-of-the-art opensource models.
+- WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. 
+- WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.
+
+For more  details of WizardLM-2 please read our [release blog post](https://wizardlm.github.io/WizardLM2) and  upcoming paper.
+
+
+## Model Details
+
+* **Model name**: WizardLM-2 7B
+* **Developed by**: WizardLM@Microsoft AI
+* **Base model**: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+* **Parameters**: 7B
+* **Language(s)**: Multilingual
+* **Blog**: [Introducing WizardLM-2](https://wizardlm.github.io/WizardLM2)
+* **Repository**: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM)
+* **Paper**: WizardLM-2 (Upcoming)
+* **License**: Apache2.0
+
+
+
+## Model Capacities
+
+
+**MT-Bench**
+
+We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. 
+The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. 
+Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.
+
+<p align="center" width="100%">
+<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+
+**Human Preferences Evaluation**
+
+We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. 
+We report the win:loss rate without tie:
+
+- WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314.
+- WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat.
+- WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta.
+
+<p align="center" width="100%">
+<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+
+
+
+
+## Method Overview
+We built a **fully AI powered synthetic training system** to train WizardLM-2 models, please refer to our [blog](https://wizardlm.github.io/WizardLM2) for more details of this system.
+
+<p align="center" width="100%">
+<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+
+
+
+
+## Usage
+
+❗<b>Note for model system prompts usage:</b>
+
+
+<b>WizardLM-2</b>  adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:
+
+```
+A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, 
+detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>
+USER: Who are you? ASSISTANT: I am WizardLM.</s>......
+```
+
+<b> Inference WizardLM-2 Demo Script</b>
+
+We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github.
+