Instructions to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2",
	filename="Qwen2.5-Coder-7B-Instruct-DatasetGen-v2-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 AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 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 AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2: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 AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2: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 AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M

Use Docker

docker model run hf.co/AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M

Ollama
How to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 with Ollama:
```
ollama run hf.co/AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M
```

Unsloth Studio

How to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 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 AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 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 AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 to start chatting

How to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2: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": "AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2: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 AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 with Docker Model Runner:
```
docker model run hf.co/AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M
```

Lemonade

How to use AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AronDaron/Qwen2.5-Coder-7B-Instruct-DatasetGen-v2:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-7B-Instruct-DatasetGen-v2-Q4_K_M

List all available models

lemonade list

Qwen2.5-Coder-7B-Instruct-DatasetGen-v2 / README.md

AronDaron

Update README.md

eb97c3a verified 2 months ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-Coder-7B-Instruct
	tags:
	- code
	- fine-tune
	- qwen
	- coding-assistant
	- gguf
	language:
	- en
	pipeline_tag: text-generation
	datasets:
	- AronDaron/dataset-gen-v2
	---

	# Qwen2.5-Coder-7B-Instruct — Dataset Generator V2 Fine-tune

	Fine-tuned version of [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
	trained on [Dataset Generator V2](https://huggingface.co/datasets/AronDaron/dataset-gen-v2)
	— synthetic coding dataset generated with [Dataset Generator](https://github.com/AronDaron/dataset-generator).

	## Benchmark Results

	\| Model \| HumanEval \| HumanEval+ \|
	\|---\|---\|---\|
	\| Base Qwen2.5-Coder-7B-Instruct \| 55.5% (±2.1) \| 49.0% (±1.9) \|
	\| This model (FT V2) \| 60.0% (±0.9) \| 54.0% (±1.8) \|

	+4.5pp on HumanEval, +5.0pp on HumanEval+ vs base — error bars don't
	overlap, statistically significant improvement (5 runs averaged).

	<img src="./benchmark-v2.png" alt="Benchmark" width="600">

	## Training

	- Method: QLoRA fine-tuning via Unsloth
	- Base model: Qwen2.5-Coder-7B-Instruct
	- Dataset: Dataset Generator V2 (1,135 multi-turn examples)
	- Hardware: RTX 4070 Ti 12GB
	- Quantization: Q4_K_M GGUF (quantized by Unsloth)
	- Chat template: ChatML (embedded in GGUF)
	- Context length: 32,768 tokens
	- Evaluation: 5 runs on HumanEval/HumanEval+ at temp 0.2

	Training logs and exact hyperparameters were not preserved — this was
	an exploratory fine-tune.

	## Training Data

	Trained on [Dataset Generator V2](https://huggingface.co/datasets/AronDaron/dataset-gen-v2)
	— 1,135 multi-turn conversations across 8 coding categories:

	- Code Generation & Debugging
	- API, DevOps & Infrastructure
	- Architecture, Testing & Refactoring
	- Terminal, CLI & Tooling
	- Algorithms & Data Manipulation
	- Data Processing & Transformation
	- Code Reasoning & Review
	- Practical Multi-step Problem Solving

	See the [dataset card](https://huggingface.co/datasets/AronDaron/dataset-gen-v2)
	for full details including generation models and methodology.

	## Limitations

	- Optimized for algorithmic coding and reasoning — shows measurable
	improvement on HumanEval/HumanEval+
	- Not optimized for library-heavy workflows (pandas, numpy, requests) —
	for those use cases, train on a dataset with library-focused categories
	using [Dataset Generator](https://github.com/AronDaron/dataset-generator)
	- Multi-turn conversational style — produces explanations alongside code

	## Support

	If this helped you:
	- Ko-fi: https://ko-fi.com/arondaron
	- ETH: 0xA6910bDa2a89ee38cA42883e365BB2DdFba3C2A1
	- BTC: bc1qamarkursch3x8399qaly4md32ck5xgthnr9jpl
	- SOL: 797jTzFRm9dd4joHPqvUjryeXi5rPbMwG6Rqj3wJrgMt

	## License

	Apache-2.0 — inherited from base model Qwen2.5-Coder-7B-Instruct.

	Built with [Dataset Generator](https://github.com/AronDaron/dataset-generator).