RikudouSage/WizardLM-2-7B-GGUF

Description

This repo contains GGUF files for the original model.

Files

• 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 (3.16 GB) - very small, high quality loss
• WizardLM-2-7B_Q3_K_M.gguf (3.52 GB) - very small, high quality loss
• WizardLM-2-7B_Q3_K_L.gguf (3.82 GB) - small, substantial quality loss
• WizardLM-2-7B_Q4_K_S.gguf (4.14 GB) - small, greater quality loss
• WizardLM-2-7B_Q4_K_M.gguf (4.37 GB) - medium, balanced quality - recommended
• WizardLM-2-7B_Q5_K_S.gguf (5 GB) - large, low quality loss - recommended
• WizardLM-2-7B_Q5_K_M.gguf (5.13 GB) - large, very low quality loss - recommended
• WizardLM-2-7B_Q6_K.gguf (5.94 GB) - very large, extremely low quality loss
• 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 and upcoming paper.

Model Details

• Model name: WizardLM-2 7B
• Developed by: WizardLM@Microsoft AI
• Base model: mistralai/Mistral-7B-v0.1
• Parameters: 7B
• Language(s): Multilingual
• Blog: Introducing WizardLM-2
• Repository: 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.

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.

Method Overview

We built a fully AI powered synthetic training system to train WizardLM-2 models, please refer to our blog for more details of this system.

Usage

❗Note for model system prompts usage:

WizardLM-2 adopts the prompt format from Vicuna 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>......

Inference WizardLM-2 Demo Script

We provide a WizardLM-2 inference demo code on our github.