Alelcv27/Qwen3-4B-INST-Code

TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 2, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

Alelcv27/Qwen3-4B-INST-Code is a 4 billion parameter instruction-tuned causal language model developed by Alelcv27. This model is a finetuned version of unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit, optimized for faster training using Unsloth and Huggingface's TRL library. With a context length of 32768 tokens, it is designed for general instruction-following tasks, leveraging its efficient training methodology.

Loading preview...

Model Overview

Alelcv27/Qwen3-4B-INST-Code is a 4 billion parameter instruction-tuned language model developed by Alelcv27. It is based on the Qwen3 architecture and has been specifically finetuned from the unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit model. This model benefits from an optimized training process, having been trained approximately 2x faster through the integration of Unsloth and Huggingface's TRL library.

Key Characteristics

  • Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs and generating more coherent, extended outputs.
  • Efficient Training: Leverages Unsloth for accelerated finetuning, making it a potentially cost-effective and time-efficient option for deployment.

Use Cases

This model is suitable for a variety of instruction-following applications where a moderately sized, efficiently trained model is beneficial. Its instruction-tuned nature makes it adaptable for tasks such as:

  • General question answering
  • Text generation based on prompts
  • Summarization
  • Conversational AI

Finetunes
3 models