WizardLM-70B-V1.0
Maintainer: WizardLMTeam
227
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
WizardLM-70B-V1.0 is a large language model developed by the WizardLM Team. It is part of the WizardLM family of models, which also includes the WizardCoder and WizardMath models. The WizardLM-70B-V1.0 model was trained to follow complex instructions and demonstrates strong performance on tasks like open-ended conversation, reasoning, and math problem-solving.
Compared to similar large language models, the WizardLM-70B-V1.0 exhibits several key capabilities. It outperforms some closed-source models like ChatGPT 3.5, Claude Instant 1, and PaLM 2 540B on the GSM8K benchmark, achieving an 81.6 pass@1 score, which is 24.8 points higher than the current SOTA open-source LLM. Additionally, the model achieves a 22.7 pass@1 score on the MATH benchmark, 9.2 points above the SOTA open-source LLM.
Model inputs and outputs
Inputs
- Natural language instructions and prompts: The model is designed to accept a wide range of natural language inputs, from open-ended conversation to specific task descriptions.
Outputs
- Natural language responses: The model generates coherent and contextually appropriate responses to the given inputs. This can include answers to questions, elaborations on ideas, and solutions to problems.
- Code generation: The WizardLM-70B-V1.0 model has also been shown to excel at code generation, with its WizardCoder variant achieving state-of-the-art performance on benchmarks like HumanEval.
Capabilities
The WizardLM-70B-V1.0 model demonstrates impressive capabilities across a range of tasks. It is able to engage in open-ended conversation, providing helpful and detailed responses. The model also excels at reasoning and problem-solving, as evidenced by its strong performance on the GSM8K and MATH benchmarks.
One key strength of the WizardLM-70B-V1.0 is its ability to follow complex instructions and tackle multi-step problems. Unlike some language models that struggle with tasks requiring sequential reasoning, this model is able to break down instructions, generate relevant outputs, and provide step-by-step solutions.
What can I use it for?
The WizardLM-70B-V1.0 model has a wide range of potential applications. It could be used to power conversational AI assistants, provide tutoring and educational support, assist with research and analysis tasks, or even help with creative writing and ideation.
The model's strong performance on math and coding tasks also makes it well-suited for use in STEM education, programming tools, and scientific computing applications. Developers could leverage the WizardCoder variant to build intelligent code generation and autocomplete tools.
Things to try
One interesting aspect of the WizardLM-70B-V1.0 model is its ability to engage in multi-turn conversations and follow up on previous context. Try providing the model with a series of related prompts and see how it maintains coherence and builds upon the discussion.
You could also experiment with the model's reasoning and problem-solving capabilities by presenting it with complex, multi-step instructions or math problems. Observe how the model breaks down the task, generates intermediate steps, and arrives at a final solution.
Another area to explore is the model's versatility across different domains. Test its performance on a variety of tasks, from open-ended conversation to specialized technical queries, to understand the breadth of its capabilities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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