Instructions to use sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sirunchained/Qwen2.5-Math-1.5B-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": "sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M

Ollama
How to use sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf with Ollama:
```
ollama run hf.co/sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf to start chatting

How to use sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-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": "sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-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 "sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf with Docker Model Runner:
```
docker model run hf.co/sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M
```

Lemonade

How to use sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Math-1.5B-Instruct-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-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 sirunchained/Qwen2.5-Math-1.5B-Instruct-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

VibeThinker-1.5B GGUF

This is a quantized version of the WeiboAI/VibeThinker-1.5B model, converted to GGUF format for use with llama.cpp and compatible tools.

Quantization Details

The model was quantized using llama.cpp to the following formats:

FP16: The original model was converted to FP16 GGUF as an intermediate step.
Q4_K_M: Quantized using the Q4_K_M method.
Q5_K_M: Quantized using the Q5_K_M method.
Q6_K: Quantized using the Q6_K method.
Q8_0: Quantized using the Q8_0 method.

Original Model Card Summary:

Model ID: WeiboAI/VibeThinker-1.5B
Original Repository: https://huggingface.co/WeiboAI/VibeThinker-1.5B

How to Use

You can use these GGUF files with llama.cpp or other tools that support the GGUF format. Download the desired quantization level and use it with your llama.cpp compatible inference engine.

Example usage with llama.cpp (replace [quant_level] with your desired quantization, e.g., q4_k_m):

./main -m VibeThinker-1.5B-[quant_level].gguf -p "Hello, what is your name?" -n 128

Files Provided

VibeThinker-1.5B-f16.gguf (FP16)
VibeThinker-1.5B-q4_k_m.gguf (Q4_K_M)
VibeThinker-1.5B-q5_k_m.gguf (Q5_K_M)
VibeThinker-1.5B-q6_k.gguf (Q6_K)
VibeThinker-1.5B-q8_0.gguf (Q8_0)

Downloads last month: 283

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit