OpenBuddy/openbuddy-llama3-70b-v21.2-32k

Hugging Face
TEXT GENERATIONPricing:Input $3.5 / Cached $0.7 / Output $8.3Concurrent Unit Cost:4Model Size:70BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Jun 12, 2024License:otherArchitecture:Transformer0.0K Featherless Exclusive Warm

OpenBuddy/openbuddy-llama3-70b-v21.2-32k is a 70 billion parameter instruction-tuned multilingual chatbot developed by OpenBuddy, built upon Meta Llama 3. This model is optimized for conversational AI in both English and Chinese, leveraging a specialized prompt format for enhanced interaction. It aims to provide helpful, respectful, and honest responses across a wide range of topics.

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OpenBuddy/openbuddy-llama3-70b-v21.2-32k Overview

This model is a 70 billion parameter instruction-tuned large language model developed by the OpenBuddy team, based on Meta's Llama 3 architecture. It is specifically designed as a multilingual chatbot, with a primary focus on English and Chinese language capabilities. The model utilizes a unique prompt format, including special tokens like <|role|>, <|says|>, and <|end|>, which is defined in its tokenizer_config.json for consistent performance across platforms like transformers and vllm.

Key Capabilities

  • Multilingual Chatbot: Optimized for conversational interactions in both English and Chinese.
  • Instruction Following: Fine-tuned to follow instructions and provide helpful, respectful, and honest responses.
  • Safety and Ethics: Designed to avoid harmful, political, religious, unethical, racist, sexist, toxic, dangerous, or illegal content, ensuring socially unbiased and positive outputs.
  • Specialized Prompt Format: Employs a custom prompt structure for effective dialogue management and response generation.

Performance and Limitations

Evaluation results from the Open LLM Leaderboard show an average score of 35.18, with specific metrics including 70.10 for IFEval (0-Shot) and 49.97 for BBH (3-Shot). The model has a knowledge cutoff of April 2023. As with all LLMs, it has inherent limitations and may produce erroneous or undesirable outputs, and is not recommended for critical or high-stakes applications.

Good for

  • Developing conversational AI agents in English and Chinese.
  • Applications requiring instruction-following capabilities.
  • Research and development in multilingual LLMs based on Llama 3.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
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presence_penalty
repetition_penalty
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min_p