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PROUDLY PRESENTS         

WizardLM-2-8x22B-exl2-rpcal

Quantized using 200 samples of 8192 tokens from an RP-oriented PIPPA dataset.

Branches:

  • main -- measurement.json
  • 4.5b6h -- 4.5bpw, 6bit lm_head
  • 4b6h -- 4bpw, 6bit lm_head
  • 3.5b6h -- 3.5bpw, 6bit lm_head
  • 2.5b6h -- 2.5bpw, 6bit lm_head

Original model link: (reuploaded, original source got taken down) alpindale/WizardLM-2-8x22B

Quanter's notes

I like this. On the main-branch, I added a few of the various settings I use in ST. I tend to mix and match these, so try them all to see which works best for you and your cards.

Original model README below.


🏠 WizardLM-2 Release Blog

πŸ€— HF Repo β€’πŸ± Github Repo β€’ 🐦 Twitter β€’ πŸ“ƒ [WizardLM] β€’ πŸ“ƒ [WizardCoder] β€’ πŸ“ƒ [WizardMath]

πŸ‘‹ Join our Discord

See here for the WizardLM-2-7B re-upload.

News πŸ”₯πŸ”₯πŸ”₯ [2024/04/15]

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 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.

MTBench

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.

Win

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.

Method

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.

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