Modelpublic

AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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Model Card

adi-qwen2.5-coder-7b-kimi2.7-code

Part of the ADI (Advanced Data Intelligence) model line — ADI Qwen2.5 series.

A small, fully local coding model that writes code like a frontier teacher. Built by distilling kimi-k2.7-code coding responses into a Qwen2.5-Coder-7B student with a 4-bit QLoRA fine-tune, then merged, converted, and quantized to GGUF. The student base retains native tool calling and a long context window.

Capabilities

Size	Context	Input	Output	Tools
4.4 GB	128K	🅣 Text	Text	✅


Base model	Qwen/Qwen2.5-Coder-7B
Teacher	kimi-k2.7-code (responses distilled, thinking disabled)
Method	4-bit QLoRA SFT (rank 16) → merge → GGUF
Quantization	Q4KM (~4.4 GB)
License	Apache-2.0 (inherited from Qwen2.5-Coder-7B)
Context	128K (inherited from base)
Tool calling	Supported (inherited from base)

Run it

Pull directly into Ollama:

bash

ollama run hf.co/AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

Or download the .gguf and point any llama.cpp-based runtime at it.

What this model is

This is a knowledge distillation: a strong coding teacher (kimi-k2.7-code) generated high-quality solutions across ~2,000 diverse programming prompts, and the Qwen2.5-Coder-7B student was fine-tuned to imitate them. The result writes and explains code noticeably more like its teacher, while staying small enough to run on a single consumer GPU.

What distillation does — and doesn't do. It transfers the teacher's coding style and solution quality, not net-new knowledge of every library or API. A 7B model won't memorize all of PyPI. What you get here is a 7B that structures, explains, and writes code more like a much larger model on tasks it already partly knows.

Training

Metric	Value
Training pairs	2,000
Teacher tokens generated	~1.58M
Epochs	3
Steps	750
Final train loss	0.7623
LoRA rank / alpha	16 / 16
Trainable params	40.4M (0.53% of 7.66B)
Precision	4-bit QLoRA
Hardware	single RTX 5060 Ti (16 GB)
Training time	2h 01m

The seed prompts were drawn from the glaive-code-assistant dataset (filtered by length and deduplicated). The teacher was queried with thinking disabled so the student learns clean, direct solutions.

Notes for re-builders

—Qwen2.5-Coder trains cleanly in 4-bit QLoRA. Unlike the Mamba-hybrid Qwen3.5, the standard Qwen2 architecture quantizes well for training; QLoRA uses ~12 GB on a 7B — comfortable on a 16 GB card.
—GGUF conversion was done with llama.cpp's convert_hf_to_gguf.py. Qwen2.5-Coder is a long-supported standard architecture, so conversion is straightforward.
—The merged model preserves the Qwen2.5 chat template with tool-calling support.

Intended use

Local coding assistant: code generation, explanation, debugging, refactoring, and tool-calling workflows where a small, private, offline-capable model is preferred over a hosted API.

License

Apache-2.0, inherited from the Qwen2.5-Coder-7B base model. You are free to use, modify, and redistribute under the terms of that license. Distilled training data was generated using kimi-k2.7-code; users should review the teacher model's terms for their own use case.

Built at [theLAB](https://thelabsource.com) — Learning. Algorithms. Breakthroughs.