Muchai12/akili-qwen2.5-7b-continuous

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 20, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Muchai12/akili-qwen2.5-7b-continuous is a 7.6 billion parameter Qwen2.5 model, finetuned by Muchai12, leveraging Unsloth for accelerated training. This model is based on unsloth/Qwen2.5-7B-Instruct-bnb-4bit and is optimized for efficient performance. It is suitable for general language generation tasks where a balance of capability and speed is desired.

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Model Overview

Muchai12/akili-qwen2.5-7b-continuous is a 7.6 billion parameter language model developed by Muchai12. It is a finetuned version of the unsloth/Qwen2.5-7B-Instruct-bnb-4bit base model, indicating its foundation in the Qwen2.5 architecture.

Key Characteristics

  • Efficient Training: This model was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster finetuning process. This suggests an emphasis on computational efficiency during its development.
  • Base Model: It builds upon the Qwen2.5-7B-Instruct-bnb-4bit model, implying it inherits the instruction-following capabilities and general language understanding of the Qwen2.5 series, optimized for 4-bit quantization.
  • Parameter Count: With 7.6 billion parameters, it offers a substantial capacity for various natural language processing tasks.

Use Cases

This model is well-suited for applications requiring a capable language model that benefits from efficient training methodologies. Its instruction-tuned base makes it suitable for:

  • General text generation and completion.
  • Instruction-following tasks.
  • Applications where faster deployment or iteration cycles are advantageous due to its efficient training.

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