A Qwen3.6 27B LoRA fine-tune focused on Python/Django coding-agent workflows, released as a GGUF Q4KM quantization for local inference. This model was trained to improve local coding-agent behavior on Python repositories, with emphasis on Django project development, traceback-driven debugging, and iterative code repair. The release includes a GGUF Q4KM quantization intended for local inference with llama.cpp-compatible tools and coding agents. — Model name: qwen3.6-27b-django-coder-gguf — Base model: Qwen/Qwen3.6-27B — Fine-tuning method: LoRA — Release format: GGUF — Quantization: Q4KM — Primary focus: Python and Django coding-agent workflows — Recommended use: local coding assistants, repository-level debugging, Django backend implementation, iterative code repair This model is intended for local coding-agent workflows, especially when working with Python and Django repositories. — building Django applications, — adding backend features to existing Django projects, — debugging Django runtime errors from logs and tracebacks, — fixing issues across models, views, URLs, templates, forms, and admin configuration, — performing iterative code repair inside a local development loop.…
Модальности:
Генерация текста
Области применения:
Генерация кода Диалог / чат
Языки программирования:
Python
Задача: Генерация текста
Автор: Tendrun
Теги: gguf, django, python, qwen, qwen3, qwen3.6, q4_k_m, lora
Лайков: 3 | Загрузок: 79
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
