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

Qwen2.5-Math-1.5B-Instruct-gguf / README.md

sirunchained

Upload folder using huggingface_hub

4634e69 verified 14 days ago

preview code

Raw

History Blame Contribute Delete

1.69 kB


	---
	pretty_name: VibeThinker-1.5B GGUF
	author: siruncahined
	license: apache-2.0
	tags:
	- qwen2
	- qwen
	- gguf
	- quantized
	- VibeThinker
	- Math
	- 1.5B
	- text-generation
	- q4_k_m
	- q5_k_m
	- q6_k
	- q8_0
	widget:
	- text: Tell me about the VibeThinker-1.5B model.
	---
	# VibeThinker-1.5B GGUF

	This is a quantized version of the [WeiboAI/VibeThinker-1.5B](https://huggingface.co/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](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`):

	```bash
	./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)