💫 Community Model> WizardLM-2-7B by Microsoft
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Model creator: Microsoft
Original model: WizardLM-2-7B
GGUF quantization: provided by bartowski based on llama.cpp release b2675
Model Summary:
WizardLM 2 7B is a followup model to the original and highly successful WizardLM line of models. This model is trained to excel at multi-turn conversations, and does so very successfully, outclassing models more than twice its size.
This model should be used for general conversation and world knowledge, but as with most models these days will be relatively competent at coding and reasoning as well.
Prompt Template:
For now, you'll need to make your own template. Choose the LM Studio Blank Preset in your LM Studio.
Then, set the system prompt to whatever you'd like (check the recommended one below), and set the following values:
System Message Suffix: ''
User Message Prefix: ' USER: '
User Message Suffix: ' ASSISTANT: '
Under the hood, the model will see a prompt that's formatted like so:
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: {prompt} ASSISTANT: </s>
Use case and examples
WizardLM 2 was tuned for improved performance on complex chat, multilingual, reasoning and agent tasks. This makes it a great model to use when wanting to chat back and forth and have reasoning based discussions.
World knowledge:
Conversational:
Coding:
Technical Details
WizardLM 2 applies several new methods for training compared to the original iteration, truly showing just how much the Open Source AI world has advanced since their intial offerings.
The first of which is Progress Learning. Rather than applying all training data at once, the team applied stage-by-stage training by partitioning the data into multiple sections and training on each one after the other.
AI Align AI (AAA) is another new process, whereby various state-of-the-art LLMs are allowed to co-teach and improve from each other, using simulated chats, quality judging, and improvement suggestions. They also participate in self-teaching in a similar manor.
The model then underwent Supervised Learning, Stage-DPO, and Evol-Instruct and Instruction&Process Supervised Reinforcement Learning (RLEIF) which uses an instruction quality reward model and a supervision reward model for more precise correctness.
The results are a model that performs exceptionally well on the automatic MT-Bench evaluation.
For more information, check the WizardLM2 blog post here
Special thanks
🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.
🙏 Special thanks to Kalomaze for his dataset (linked here) that was used for calculating the imatrix for these quants, which improves the overall quality!
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