sylvester-francis/typescript-slm-7b-reasoning-full
sylvester-francis/typescript-slm-7b-reasoning-full is a 7.6 billion parameter DeepSeek-based causal language model, fine-tuned by Sylvester Francis for step-by-step TypeScript reasoning. It excels at explaining TypeScript bugs, refactoring code, and generating strongly-typed solutions for frameworks like React, Next.js, Angular, and Node.js. This model is specifically optimized for code reasoning and debugging tasks within the TypeScript ecosystem, providing clear reasoning traces before final answers.
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Overview
TypeScript-SLM-7B-Reasoning-Full is a 7.6 billion parameter model developed by Sylvester Francis, built upon the deepseek-ai/DeepSeek-R1-Distill-Qwen-7B base. It has been fine-tuned using LoRA adapters specifically for advanced TypeScript reasoning tasks. The model is designed to provide detailed, step-by-step explanations and solutions for complex coding challenges, and includes GGUF quantization for efficient local inference with tools like Ollama.
Key Capabilities
- TypeScript Reasoning & Debugging: Explains bugs, suggests fixes, and provides clear reasoning traces.
- Code Generation: Generates strongly-typed code for popular frameworks including React, Next.js, Angular, and Node.js.
- Refactoring & API Design: Assists with code refactoring and discussions on API design choices.
- Framework-Aware: Produces solutions that adhere to framework best practices.
Intended Uses
This model is primarily intended for TypeScript-related tasks such as debugging, refactoring, and guided code generation. It is not designed for general natural language chat, safety-sensitive applications, or factual tasks outside of its specialized domain. Users should review generated code for correctness and security before deployment.