build-small-hackathon/deal_sft_4B_hard

TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 9, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The build-small-hackathon/deal_sft_4B_hard is a 4 billion parameter Qwen3-based instruction-tuned causal language model. Developed by build-small-hackathon, it was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for efficient performance due to its accelerated training methodology.

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Overview

The build-small-hackathon/deal_sft_4B_hard is a 4 billion parameter language model based on the Qwen3 architecture. It was developed by build-small-hackathon and fine-tuned from the unsloth/Qwen3-4B-Instruct-2507 model.

Key Characteristics

  • Architecture: Qwen3-based, a causal language model.
  • Parameter Count: 4 billion parameters.
  • Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
  • License: Released under the Apache-2.0 license.

Potential Use Cases

This model is suitable for applications requiring a compact yet capable instruction-tuned model, especially where training efficiency is a priority. Its Qwen3 foundation suggests strong general language understanding and generation capabilities, making it a candidate for tasks such as:

  • Instruction following and conversational AI.
  • Text summarization and generation.
  • Code assistance or generation (given the base model's potential).

The accelerated training process highlights its potential for rapid iteration and deployment in development environments.

Finetunes
1 models