---

license: apache-2.0

base_model: Qwen/Qwen2.5-Coder-7B

tags:

- gguf

- distillation

- qwen2.5

- qwen2.5-coder

- code

- adi

- advanced-data-intelligence

- text-generation

- tool-calling

language:

- en

pipeline_tag: text-generation

library_name: gguf

---

<img src="https://serve.thelabsource.com/u/5OJrqN.png" alt="adi-qwen2.5-coder-7b-kimi2.7-code" width="800">

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 | Q4_K_M (~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:

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.

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Built at theLAB — Learning. Algorithms. Breakthroughs.