Instructions to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF",
	filename="adi-qwen2.5-coder-7b-kimi-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

Ollama
How to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with Ollama:
```
ollama run hf.co/AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M
```

Unsloth Studio

How to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF to start chatting

How to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with Docker Model Runner:
```
docker model run hf.co/AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M
```

Lemonade

How to use AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AdvancedDataIntelligence/adi-qwen2.5-coder-7b-kimi2.7-code-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.adi-qwen2.5-coder-7b-kimi2.7-code-GGUF-Q4_K_M

List all available models

lemonade list

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.


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.

Built at theLAB — Learning. Algorithms. Breakthroughs.

Downloads last month: 213

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Quantized

(43)

this model