arcee-ai/Arcee-Blitz

Hugging Face
TEXT GENERATIONConcurrency Cost:2Model Size:24BQuant:FP8Ctx Length:32kPublished:Feb 7, 2025License:apache-2.0Architecture:Transformer0.1K Open Weights Warm

Arcee-Blitz is a 24 billion parameter Mistral-based model developed by arcee-ai, distilled from DeepSeek-V3 logits. Optimized for efficiency and speed, it serves as a practical workhorse model for various tasks. It demonstrates significant improvements in world knowledge, particularly on MMLU-Pro, and enhanced performance across coding and reasoning benchmarks compared to Mistral-Small-3. This model is designed for general-purpose applications requiring a balance of performance and resource efficiency.

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Arcee-Blitz: A Fast and Efficient 24B Mistral-Based Model

Arcee-Blitz is a 24 billion parameter model built on the Mistral architecture, developed by arcee-ai. It is distinguished by its distillation from DeepSeek-V3 logits, a process that imbues it with enhanced capabilities while maintaining efficiency. Designed as a practical "workhorse" model, Arcee-Blitz aims to handle a diverse range of tasks without the computational overhead typically associated with larger models.

Key Capabilities & Performance

  • Improved World Knowledge: Arcee-Blitz shows substantial gains in world knowledge, reflected by a significant increase in MMLU-Pro scores (from 44.70% to 60.20%) compared to Mistral-Small-3.
  • Enhanced Reasoning: Demonstrates better performance on reasoning tasks, with improvements in BBH and GPQA benchmarks.
  • Strong Coding Performance: Outperforms Mistral-Small-3 across various BigCodeBench metrics, including Complete, Instruct, and Complete-hard categories.
  • Mathematical Proficiency: Achieves a notable increase in Math Level 5 scores (from 12.00% to 38.60%).
  • Efficient Architecture: Leverages a Mistral-based architecture, optimized for speed and resource efficiency.

Use Cases & Considerations

Arcee-Blitz is well-suited for applications requiring a capable yet efficient language model. Its improved world knowledge and coding abilities make it versatile for tasks such as content generation, code assistance, and complex question answering. The model has a context length of 32,768 tokens and is released under the Apache-2.0 License, allowing for broad commercial and non-commercial use. Users should be aware of its knowledge cut-off around June 2024 and potential risks of generating biased content, common to all large language models.

Popular Sampler Settings

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

temperature
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top_k
frequency_penalty
presence_penalty
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