hnda/qwen3-4b-alf-sft-merged
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Feb 15, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The hnda/qwen3-4b-alf-sft-merged model is a 4 billion parameter Qwen3-based language model developed by hnda, fine-tuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit. This model was trained significantly faster using Unsloth and Huggingface's TRL library, offering a 40960 token context length. It is optimized for efficient performance due to its accelerated training methodology, making it suitable for applications requiring a capable yet resource-efficient Qwen3 variant.
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hnda/qwen3-4b-alf-sft-merged Overview
This model is a 4 billion parameter Qwen3-based language model developed by hnda. It has been fine-tuned from the unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit base model.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 4 billion parameters.
- Context Length: Supports a substantial context window of 40960 tokens.
- Training Efficiency: A notable differentiator is its accelerated training process, which was achieved using Unsloth and Huggingface's TRL library. This indicates a focus on optimizing the fine-tuning speed and resource utilization.
Good For
- Resource-Efficient Applications: Its optimized training suggests it could be beneficial for scenarios where faster fine-tuning or deployment of a Qwen3-based model is critical.
- General Language Tasks: As a fine-tuned Qwen3 variant, it is expected to perform well across a range of common natural language processing tasks.
- Developers utilizing Unsloth: Users already familiar with or planning to use Unsloth for efficient model training might find this model particularly relevant due to its development methodology.