Instructions to use tzervas/qwen2.5-coder-32b-bitnet-1.58b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tzervas/qwen2.5-coder-32b-bitnet-1.58b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tzervas/qwen2.5-coder-32b-bitnet-1.58b",
	filename="qwen-coder-32b-tq2.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 tzervas/qwen2.5-coder-32b-bitnet-1.58b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tzervas/qwen2.5-coder-32b-bitnet-1.58b
# Run inference directly in the terminal:
llama-cli -hf tzervas/qwen2.5-coder-32b-bitnet-1.58b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tzervas/qwen2.5-coder-32b-bitnet-1.58b
# Run inference directly in the terminal:
llama-cli -hf tzervas/qwen2.5-coder-32b-bitnet-1.58b

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 tzervas/qwen2.5-coder-32b-bitnet-1.58b
# Run inference directly in the terminal:
./llama-cli -hf tzervas/qwen2.5-coder-32b-bitnet-1.58b

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 tzervas/qwen2.5-coder-32b-bitnet-1.58b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tzervas/qwen2.5-coder-32b-bitnet-1.58b

Use Docker

docker model run hf.co/tzervas/qwen2.5-coder-32b-bitnet-1.58b

LM Studio
Jan

vLLM

How to use tzervas/qwen2.5-coder-32b-bitnet-1.58b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tzervas/qwen2.5-coder-32b-bitnet-1.58b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tzervas/qwen2.5-coder-32b-bitnet-1.58b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tzervas/qwen2.5-coder-32b-bitnet-1.58b

Ollama
How to use tzervas/qwen2.5-coder-32b-bitnet-1.58b with Ollama:
```
ollama run hf.co/tzervas/qwen2.5-coder-32b-bitnet-1.58b
```

Unsloth Studio

How to use tzervas/qwen2.5-coder-32b-bitnet-1.58b 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 tzervas/qwen2.5-coder-32b-bitnet-1.58b 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 tzervas/qwen2.5-coder-32b-bitnet-1.58b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tzervas/qwen2.5-coder-32b-bitnet-1.58b to start chatting

How to use tzervas/qwen2.5-coder-32b-bitnet-1.58b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tzervas/qwen2.5-coder-32b-bitnet-1.58b

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": "tzervas/qwen2.5-coder-32b-bitnet-1.58b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tzervas/qwen2.5-coder-32b-bitnet-1.58b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tzervas/qwen2.5-coder-32b-bitnet-1.58b

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 tzervas/qwen2.5-coder-32b-bitnet-1.58b

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use tzervas/qwen2.5-coder-32b-bitnet-1.58b with Docker Model Runner:
```
docker model run hf.co/tzervas/qwen2.5-coder-32b-bitnet-1.58b
```

Lemonade

How to use tzervas/qwen2.5-coder-32b-bitnet-1.58b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tzervas/qwen2.5-coder-32b-bitnet-1.58b

Run and chat with the model

lemonade run user.qwen2.5-coder-32b-bitnet-1.58b-{{QUANT_TAG}}

List all available models

lemonade list

tzervas commited on Jan 29

Commit

f26846f

verified ·

1 Parent(s): d0342f9

Upload README.md with huggingface_hub

Browse files

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README.md +6 -4

README.md CHANGED Viewed

@@ -49,7 +49,9 @@ This is an **experimental** BitNet 1.58-bit quantization of the Qwen2.5-Coder-32
 | Format | File | Description |
 |--------|------|-------------|
 | SafeTensors | `model-*.safetensors` | Sharded quantized weights + scales |
-| GGUF | `qwen-coder-32b-tq2.gguf` | llama.cpp compatible |
 ## Quantization Method
@@ -71,10 +73,10 @@ This is an **experimental** BitNet 1.58-bit quantization of the Qwen2.5-Coder-32
 ## Usage
-### With Ollama/llama.cpp
 ```bash
-# llama.cpp
-./llama-cli -m qwen-coder-32b-tq2.gguf -p "Write a Python function:"
 ```
 ### Unpacking Weights (Python)

 | Format | File | Description |
 |--------|------|-------------|
 | SafeTensors | `model-*.safetensors` | Sharded quantized weights + scales |
+| GGUF | `qwen2.5-coder-32b-TQ2_0.gguf` | llama.cpp TQ2_0 format (experimental) |
+> **GGUF Compatibility Note**: The GGUF conversion is experimental. Our BitNet quantization uses group size 64, while TQ2_0 uses 256-element blocks. This may cause compatibility issues with some inference engines. The SafeTensors format is the primary supported format.
 ## Quantization Method
 ## Usage
+### With Ollama/llama.cpp (experimental)
 ```bash
+# llama.cpp (GGUF format - experimental, may have issues)
+./llama-cli -m qwen2.5-coder-32b-TQ2_0.gguf -p "Write a Python function:"
 ```
 ### Unpacking Weights (Python)