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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use worthdoing/Qwen2.5-Coder-7B-Instruct-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 worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf worthdoing/Qwen2.5-Coder-7B-Instruct-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 worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf worthdoing/Qwen2.5-Coder-7B-Instruct-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 worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf worthdoing/Qwen2.5-Coder-7B-Instruct-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 worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "worthdoing/Qwen2.5-Coder-7B-Instruct-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": "worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

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

Unsloth Studio

How to use worthdoing/Qwen2.5-Coder-7B-Instruct-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 worthdoing/Qwen2.5-Coder-7B-Instruct-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 worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use worthdoing/Qwen2.5-Coder-7B-Instruct-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 worthdoing/Qwen2.5-Coder-7B-Instruct-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 worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF: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 "worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF: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 worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use worthdoing/Qwen2.5-Coder-7B-Instruct-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Qwen2.5-Coder-7B-Instruct-GGUF / README.md

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	---
	language:
	- en
	- fr
	- multilingual
	license: apache-2.0
	tags:
	- gguf
	- quantized
	- mac
	- apple-silicon
	- local-inference
	- worthdoing
	base_model: Qwen/Qwen2.5-Coder-7B-Instruct
	quantized_by: worthdoing
	pipeline_tag: text-generation
	---

	<p align="center">
	<img src="https://raw.githubusercontent.com/Worth-Doing/brand-assets/main/png/variants/04-horizontal.png" alt="worthdoing" width="400"/>
	</p>
	<p align="center"><strong>Author: Simon-Pierre Boucher</strong></p>

	<p align="center">
	<img src="https://img.shields.io/badge/Format-GGUF-blue?style=for-the-badge" alt="GGUF"/>
	<img src="https://img.shields.io/badge/Params-7B-orange?style=for-the-badge" alt="Parameters"/>
	<img src="https://img.shields.io/badge/Platform-Apple_Silicon-black?style=for-the-badge&logo=apple" alt="Apple Silicon"/>
	<img src="https://img.shields.io/badge/License-Apache_2.0-green?style=for-the-badge" alt="License"/>
	<img src="https://img.shields.io/badge/Quantized_by-worthdoing-purple?style=for-the-badge" alt="worthdoing"/>
	</p>
	<p align="center">
	<img src="https://img.shields.io/badge/Q4__K__M-3.7_GB-brightgreen?style=flat-square" alt="Q4_K_M"/>
	<img src="https://img.shields.io/badge/Q5__K__M-4.3_GB-yellow?style=flat-square" alt="Q5_K_M"/>
	<img src="https://img.shields.io/badge/Q8__0-6.5_GB-red?style=flat-square" alt="Q8_0"/>
	</p>

	# Qwen2.5-Coder-7B-Instruct - GGUF Quantized by worthdoing

	> Quantized for local Mac inference (Apple Silicon / Metal) by worthdoing

	## About

	This is a GGUF quantized version of [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct), optimized for running locally on Apple Silicon Macs with `llama.cpp`, `Ollama`, or `LM Studio`.

	- Original model: [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
	- Parameters: 7B
	- Quantized by: worthdoing
	- Pipeline: corelm-model v1.0

	## Description

	Qwen's dedicated coding model. Top-tier code generation and understanding.

	## Available Quantizations

	\| File \| Quant \| BPW \| Size \| Use Case \|
	\|------\|-------\|-----\|------\|----------\|
	\| `qwen2.5-coder-7b-instruct-Q4_K_M-worthdoing.gguf` \| Q4_K_M \| 4.58 \| ~3.7 GB \| Recommended - Best quality/size ratio \|
	\| `qwen2.5-coder-7b-instruct-Q5_K_M-worthdoing.gguf` \| Q5_K_M \| 5.33 \| ~4.3 GB \| Higher quality, still fast \|
	\| `qwen2.5-coder-7b-instruct-Q8_0-worthdoing.gguf` \| Q8_0 \| 7.96 \| ~6.5 GB \| Near-original quality \|

	## How to Use

	### With Ollama
	```bash
	# Create a Modelfile
	cat > Modelfile <<'MODELEOF'
	FROM ./qwen2.5-coder-7b-instruct-Q4_K_M-worthdoing.gguf
	MODELEOF

	ollama create qwen2.5-coder-7b-instruct -f Modelfile
	ollama run qwen2.5-coder-7b-instruct
	```

	### With llama.cpp
	```bash
	llama-cli -m qwen2.5-coder-7b-instruct-Q4_K_M-worthdoing.gguf -p "Your prompt here" -ngl 99
	```

	### With LM Studio
	1. Download the GGUF file
	2. Open LM Studio -> My Models -> Import
	3. Select the GGUF file and start chatting

	## Quantization Method

	Our quantization pipeline (corelm-model v1.0) follows a rigorous multi-step process to ensure maximum quality and compatibility:

	### Step 1 — Download & Validation
	- Model weights are downloaded from HuggingFace Hub in SafeTensors format (`.safetensors`)
	- Legacy formats (`.bin`, `.pt`) are excluded to ensure clean, verified weights
	- Tokenizer, configuration, and all metadata are preserved

	### Step 2 — Conversion to GGUF F16 Baseline
	- The original model is converted to GGUF format at FP16 precision using `convert_hf_to_gguf.py` from [llama.cpp](https://github.com/ggml-org/llama.cpp)
	- This lossless baseline preserves the full original model quality
	- Architecture-specific tensors (attention, FFN, embeddings, MoE routing) are mapped to their GGUF equivalents

	### Step 3 — K-Quant Quantization
	- The F16 baseline is quantized using `llama-quantize` with k-quant methods
	- K-quants use a mixed-precision approach: more important layers (attention, output) retain higher precision, while less sensitive layers (FFN) are compressed more aggressively
	- Each quantization level offers a different quality/size tradeoff:

	\| Method \| Bits per Weight \| Strategy \|
	\|--------\|----------------\|----------\|
	\| Q4_K_M \| ~4.58 bpw \| Mixed 4/5-bit. Attention & output layers use Q5_K, FFN layers use Q4_K. Best balance of quality and size. \|
	\| Q5_K_M \| ~5.33 bpw \| Mixed 5/6-bit. Attention & output layers use Q6_K, FFN layers use Q5_K. Higher quality with moderate size increase. \|
	\| Q8_0 \| ~7.96 bpw \| Uniform 8-bit. All layers quantized to 8-bit. Near-lossless quality, largest file size. \|

	### Step 4 — Metadata Injection
	- Custom metadata is embedded directly in each GGUF file:
	- `general.quantized_by`: worthdoing
	- `general.quantization_version`: corelm-1.0
	- This ensures full traceability and provenance of every quantized file

	### Tools & Environment
	- llama.cpp: Used for both conversion and quantization — the industry-standard open-source LLM inference engine
	- Target platform: Apple Silicon Macs (M1/M2/M3/M4) with Metal GPU acceleration
	- Inference runtimes: Compatible with `llama.cpp`, `Ollama`, `LM Studio`, `koboldcpp`, and any GGUF-compatible runtime

	## Recommended Hardware

	\| Quant \| Min RAM \| Recommended \|
	\|-------\|---------\|-------------\|
	\| Q4_K_M \| 4 GB \| Mac with 8 GB+ RAM \|
	\| Q5_K_M \| 5 GB \| Mac with 8 GB+ RAM \|
	\| Q8_0 \| 8 GB \| Mac with 12 GB+ RAM \|

	## Tags

	`coding`, `code-generation`, `code-review`

	---

	Quantized with corelm-model pipeline by worthdoing* on 2026-04-17*