Arcee Spark: A Compact & Efficient 7B Parameter Language Model

Looking for proof that Small is the new Big when it comes to language models? Look no further than the model we've just dropped here at Arcee AI: you get top-notch results with just 7B parameters.

Arcee Spark: A Compact & Efficient 7B Parameter Language Model

Arcee Spark is a 7B parameter language model designed to deliver high performance in a compact package – demonstrating that smaller models can achieve competitive results when compared to their larger counterparts.

It is the highest-scoring model by far in the 7B-15B range, outperforming Mixtral-8x7B and Llama-3-8B-Instruct. Arcee Spark also outperforms bigger models including GPT 3.5 and Claude 2.1 on MT-Bench, which is a benchmark heavily correlated with performance on lmsys' chatbot arena.

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• Arcee Spark Model Card
• Quantized models
• Hugging Face Space

Key Features of Arcee Spark

  • 7B parameters
  • Initialized from Qwen2
  • Fine-tuned on 1.8 million samples
  • Merged with Qwen2-7B-Instruct using Arcee's MergeKit
  • Further refined using Direct Preference Optimization (DPO).

Performance

Arcee Spark has shown strong performance across key benchmarks:

  • EQ-Bench: 71.4
  • GPT4All Evaluation: Average of 69.37 across diverse language tasks.
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Potential Applications

The model's combination of compact size and strong performance makes it suitable for:

  • Real-time applications like chatbots and customer service automation
  • Edge computing scenarios
  • Cost-effective AI implementation across organizations
  • Rapid prototyping of AI-powered features
  • On-premise deployment for enhanced data privacy.

Efficiency and Flexibility

Arcee Spark offers:

  • Faster inference times compared to larger models
  • Lower computational requirements
  • Adaptability for fine-tuning to specific domains or tasks.

Availability

The model is available in three main versions:

  • GGUF quantized versions for efficiency and easy deployment
  • BF16 version (main repository)
  • FP32 version for maximum performance, scoring slightly higher on benchmarks.

Conclusion

Arcee Spark demonstrates the groundbreaking potential of optimized smaller models in the field of natural language processing (nlp). It offers a balance of performance and efficiency, making it a viable option for numerous AI applications.Try it out and tell us what you think!