Instructions to use bartowski/Qwen2.5-Coder-32B-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 bartowski/Qwen2.5-Coder-32B-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/Qwen2.5-Coder-32B-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/Qwen2.5-Coder-32B-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/Qwen2.5-Coder-32B-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/Qwen2.5-Coder-32B-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": "bartowski/Qwen2.5-Coder-32B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Ollama
How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with Ollama:
```
ollama run hf.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use bartowski/Qwen2.5-Coder-32B-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/Qwen2.5-Coder-32B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-32B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bartowski/Qwen2.5-Coder-32B-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 bartowski/Qwen2.5-Coder-32B-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 bartowski/Qwen2.5-Coder-32B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bartowski/Qwen2.5-Coder-32B-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 bartowski/Qwen2.5-Coder-32B-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 "bartowski/Qwen2.5-Coder-32B-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"

bartowski commited on Nov 10, 2024

Commit

d280347

verified ·

1 Parent(s): c1c3fbc

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README.md +1 -11

README.md CHANGED Viewed

@@ -1,17 +1,6 @@
 ---
 quantized_by: bartowski
 pipeline_tag: text-generation
-language:
-- en
-license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE
-tags:
-- code
-- codeqwen
-- chat
-- qwen
-- qwen-coder
-base_model: Qwen/Qwen2.5-Coder-32B-Instruct
-license: apache-2.0
 ---
 ## Llamacpp imatrix Quantizations of Qwen2.5-Coder-32B-Instruct
@@ -60,6 +49,7 @@ Run them in [LM Studio](https://lmstudio.ai/)
 | [Qwen2.5-Coder-32B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-Q3_K_S.gguf) | Q3_K_S | 14.39GB | false | Low quality, not recommended. |
 | [Qwen2.5-Coder-32B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-IQ3_XS.gguf) | IQ3_XS | 13.71GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
 | [Qwen2.5-Coder-32B-Instruct-Q2_K_L.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-Q2_K_L.gguf) | Q2_K_L | 13.07GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
 | [Qwen2.5-Coder-32B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-Q2_K.gguf) | Q2_K | 12.31GB | false | Very low quality but surprisingly usable. |
 | [Qwen2.5-Coder-32B-Instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-IQ2_M.gguf) | IQ2_M | 11.26GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
 | [Qwen2.5-Coder-32B-Instruct-IQ2_S.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-IQ2_S.gguf) | IQ2_S | 10.39GB | false | Low quality, uses SOTA techniques to be usable. |

 ---
 quantized_by: bartowski
 pipeline_tag: text-generation
 ---
 ## Llamacpp imatrix Quantizations of Qwen2.5-Coder-32B-Instruct
 | [Qwen2.5-Coder-32B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-Q3_K_S.gguf) | Q3_K_S | 14.39GB | false | Low quality, not recommended. |
 | [Qwen2.5-Coder-32B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-IQ3_XS.gguf) | IQ3_XS | 13.71GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
 | [Qwen2.5-Coder-32B-Instruct-Q2_K_L.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-Q2_K_L.gguf) | Q2_K_L | 13.07GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
+| [Qwen2.5-Coder-32B-Instruct-IQ3_XXS.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-IQ3_XXS.gguf) | IQ3_XXS | 12.84GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
 | [Qwen2.5-Coder-32B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-Q2_K.gguf) | Q2_K | 12.31GB | false | Very low quality but surprisingly usable. |
 | [Qwen2.5-Coder-32B-Instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-IQ2_M.gguf) | IQ2_M | 11.26GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
 | [Qwen2.5-Coder-32B-Instruct-IQ2_S.gguf](https://huggingface.co/bartowski/Qwen2.5-Coder-32B-Instruct-GGUF/blob/main/Qwen2.5-Coder-32B-Instruct-IQ2_S.gguf) | IQ2_S | 10.39GB | false | Low quality, uses SOTA techniques to be usable. |