snieper121/godot-qwen-7b

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:May 25, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The snieper121/godot-qwen-7b is a 7.6 billion parameter Qwen2.5-Coder-7B-Instruct model, developed by snieper121 and fine-tuned using Unsloth and Huggingface's TRL library. This model is optimized for efficient training, having been trained 2x faster, making it suitable for applications requiring a performant yet rapidly developed Qwen-based instruction-following model. Its foundation in the Qwen2.5-Coder series suggests a strong capability in code-related tasks and general instruction adherence.

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Model Overview

The snieper121/godot-qwen-7b is a 7.6 billion parameter instruction-tuned language model, developed by snieper121. It is fine-tuned from the unsloth/qwen2.5-coder-7b-instruct-bnb-4bit base model, leveraging the Unsloth library and Huggingface's TRL for efficient training.

Key Characteristics

  • Base Model: Fine-tuned from Qwen2.5-Coder-7B-Instruct, indicating strong capabilities in code understanding and generation, as well as general instruction following.
  • Efficient Training: Utilizes Unsloth, which enabled the model to be trained approximately 2x faster than conventional methods.
  • Parameter Count: Features 7.6 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.

Use Cases

This model is particularly well-suited for applications that benefit from:

  • Code-related tasks: Given its Coder base, it's expected to perform well in code generation, completion, and explanation.
  • Instruction following: Designed to accurately respond to and execute given instructions.
  • Applications requiring efficient deployment: The optimized training process suggests a model that can be integrated into various systems with relative ease, especially where Qwen2.5 capabilities are desired.