---
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](https://huggingface.co/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](https://ollama.com):

```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](https://huggingface.co/datasets/glaiveai/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](https://huggingface.co/Qwen/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.*