GGUF Quants created using an imatrix calculated on
this calibration data by barowski1182
Attention: split Files in the main branch are split using the UNIX split command, not gguf-split;
they need to be manually concaternated via e.g.
cat WizardLM-2-8x22B-imatrix-Q6_K-* > WizardLM-2-8x22B-imatrix-Q6_K.gguf
All split files in branch gguf-split uploaded after June 1st, 12:00h GMT can be loaded directly by pointing llama.cpp to the first part, it will load the following parts automagically.
They can be reassabled to a single file via the gguf-split --merge command, just concaternating as the UNIX split files WON'T WORK.
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See here for the WizardLM-2-7B re-upload.
News 🔥🔥🔥 [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 name: WizardLM-2 8x22B
- Developed by: WizardLM@Microsoft AI
- Model type: Mixture of Experts (MoE)
- Base model: mistral-community/Mixtral-8x22B-v0.1
- Parameters: 141B
- 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.
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