LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 24, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA is a 7.6 billion parameter causal decoder-only language model, based on the Qwen2 architecture and fine-tuned with LoRA by LadiesMan69. This model is specifically optimized as a FastAPI documentation assistant, excelling at generating code, answering questions, and explaining concepts related to the FastAPI framework. It leverages a 32768 token context length to provide accurate and idiomatic responses for FastAPI development.

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Qwen2.5-Coder-7B-FastAPI-LoRA: Your Dedicated FastAPI Assistant

This model is a LoRA fine-tune of Qwen2.5-Coder-7B-Instruct, developed by LadiesMan69, specifically designed to act as a FastAPI documentation assistant. It is built upon the Qwen2 architecture and utilizes a 4-bit quantized base model from Unsloth, enabling efficient fine-tuning and deployment.

Key Capabilities

  • FastAPI-Specific Expertise: Provides accurate and idiomatic answers, code generation, and explanations for the FastAPI framework.
  • Comprehensive Topic Coverage: Trained on a curated dataset covering core concepts (routing, parameters, response models), advanced topics (WebSockets, middleware), security (OAuth2/JWT), and testing (TestClient).
  • Efficient Fine-tuning: Leverages LoRA (Low-Rank Adaptation) and Unsloth's optimized kernels for faster and memory-efficient training.
  • ChatML Support: Designed to work with ChatML-style templates, allowing for clear system and user message interactions.

What Makes This Model Different?

While general-purpose code models can be broad, this model stands out due to its hyper-specialization in FastAPI. It addresses the common issue of general models being imprecise or outdated for framework-specific APIs by providing a focused, up-to-date knowledge base for FastAPI development. This makes it an ideal lightweight, deployable assistant for developers working exclusively with FastAPI.

Intended Use Cases

  • Answering questions about FastAPI concepts, patterns, and best practices.
  • Generating FastAPI route handlers, Pydantic models, and dependency-injected services.
  • Explaining and debugging FastAPI-related code snippets.
  • Serving as an in-editor or chat-based documentation assistant for FastAPI developers.

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