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

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

Hermes Agent

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

spb-wd commited on Apr 17

Commit

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1 Parent(s): 04e1ac9

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@@ -68,6 +68,42 @@ llama-cli -m qwen2.5-coder-7b-instruct-Q4_K_M-worthdoing.gguf -p "Your prompt he
 2. Open LM Studio -> My Models -> Import
 3. Select the GGUF file and start chatting
 ## Recommended Hardware
 | Quant | Min RAM | Recommended |

 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 |