WizardLMTeam/WizardLM-70B-V1.0

TEXT GENERATIONConcurrency Cost:4Model Size:69BQuant:FP8Ctx Length:32kPublished:Aug 9, 2023License:llama2Architecture:Transformer0.2K Open Weights Cold

WizardLM-70B-V1.0 is a 69 billion parameter instruction-following language model developed by WizardLMTeam, based on the Llama 2 architecture. It is specifically designed to follow complex instructions and demonstrates strong performance across various benchmarks, including general language understanding, mathematical reasoning, and code generation. This model excels at multi-turn conversations and is suitable for applications requiring nuanced instruction adherence.

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WizardLM-70B-V1.0 Overview

WizardLM-70B-V1.0 is a 69 billion parameter instruction-following language model developed by WizardLMTeam. It is built upon the Llama 2 architecture and is specifically trained to enhance its ability to follow complex instructions, supporting multi-turn conversations. This model is part of a broader family of WizardLM models, which also includes specialized versions like WizardCoder for code generation and WizardMath for mathematical reasoning.

Key Capabilities & Performance

  • Instruction Following: Designed to excel at understanding and executing complex instructions, making it highly adaptable for various tasks.
  • Multi-turn Conversation: Supports and maintains context across extended dialogues, adopting the Vicuna prompt format.
  • General Benchmarks: Achieves a score of 7.78 on MT-Bench and 92.91% on AlpacaEval, indicating strong general performance.
  • Mathematical Reasoning: Demonstrates robust mathematical capabilities with 77.6% on GSM8k.
  • Code Generation: Shows proficiency in code tasks, scoring 50.6 pass@1 on HumanEval.

Good for

  • Applications requiring a large language model that can accurately follow detailed and intricate instructions.
  • Building conversational agents that need to maintain context and respond coherently over multiple turns.
  • Tasks involving general language understanding, mathematical problem-solving, and code-related queries.