MaziyarPanahi/calme-2.3-llama3.1-70b

Hugging Face
TEXT GENERATIONConcurrency Cost:4Model Size:70BQuant:FP8Ctx Length:32kArchitecture:Transformer0.0K Warm

MaziyarPanahi/calme-2.3-llama3.1-70b is a 70 billion parameter language model fine-tuned by MaziyarPanahi from Meta-Llama-3.1-70B-Instruct, designed for advanced natural language understanding and generation. With a 32768 token context length, it aims for versatility and robustness across various benchmarks and real-world applications. This model excels in complex tasks such as advanced question-answering, intelligent chatbots, content generation, and code analysis.

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Overview

MaziyarPanahi/calme-2.3-llama3.1-70b is a 70 billion parameter language model, fine-tuned by MaziyarPanahi from the meta-llama/Meta-Llama-3.1-70B-Instruct base. The primary goal of this fine-tuning was to enhance its capabilities in natural language understanding and generation, making it a versatile and robust model for diverse applications. It supports a substantial context length of 32768 tokens and utilizes the ChatML prompt template.

Key Capabilities

  • Advanced Question-Answering: Designed to provide precise and comprehensive answers.
  • Intelligent Chatbots and Virtual Assistants: Capable of engaging in sophisticated conversational interactions.
  • Content Generation and Summarization: Efficiently creates and condenses textual content.
  • Code Generation and Analysis: Supports the creation and examination of programming code.
  • Complex Problem-Solving: Aids in decision support and tackling intricate problems.

Performance

Evaluated on the Open LLM Leaderboard, the model achieves an average score of 40.30. Notable benchmark results include 86.05 on IFEval (0-Shot), 55.59 on BBH (3-Shot), and 48.48 on MMLU-PRO (5-shot), indicating strong performance across various reasoning and knowledge-based tasks.

Ethical Considerations

Users are advised to be aware of potential biases and limitations inherent in large language models. The developer recommends implementing appropriate safeguards and human oversight when deploying this model in production environments.

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
presence_penalty
repetition_penalty
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