Instructions to use tensorblock/Qwen2-Math-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/Qwen2-Math-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/Qwen2-Math-7B-Instruct-GGUF",
	filename="Qwen2-Math-7B-Instruct-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tensorblock/Qwen2-Math-7B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K

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 tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K

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 tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/Qwen2-Math-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/Qwen2-Math-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": "tensorblock/Qwen2-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K

Ollama
How to use tensorblock/Qwen2-Math-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/Qwen2-Math-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 tensorblock/Qwen2-Math-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 tensorblock/Qwen2-Math-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 tensorblock/Qwen2-Math-7B-Instruct-GGUF to start chatting

Docker Model Runner
How to use tensorblock/Qwen2-Math-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K
```

Lemonade

How to use tensorblock/Qwen2-Math-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/Qwen2-Math-7B-Instruct-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen2-Math-7B-Instruct-GGUF-Q2_K

List all available models

lemonade list

Qwen2-Math-7B-Instruct-GGUF / README.md

morriszms

Update README.md

b3f111c verified 10 months ago

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	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- chat
	- TensorBlock
	- GGUF
	base_model: Qwen/Qwen2-Math-7B-Instruct
	---

	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>

	[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co)
	[![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi)
	[![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2)
	[![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock)
	[![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock)


	## Qwen/Qwen2-Math-7B-Instruct - GGUF

	This repo contains GGUF format model files for [Qwen/Qwen2-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Math-7B-Instruct).

	The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).


	## Our projects
	<table border="1" cellspacing="0" cellpadding="10">
	<tr>
	<th colspan="2" style="font-size: 25px;">Forge</th>
	</tr>
	<tr>
	<th colspan="2">
	<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
	</th>
	</tr>
	<tr>
	<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
	</tr>
	<tr>
	<th colspan="2">
	<a href="https://github.com/TensorBlock/forge" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">🚀 Try it now! 🚀</a>
	</th>
	</tr>

	<tr>
	<th style="font-size: 25px;">Awesome MCP Servers</th>
	<th style="font-size: 25px;">TensorBlock Studio</th>
	</tr>
	<tr>
	<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
	<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
	</tr>
	<tr>
	<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
	<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
	</tr>
	<tr>
	<th>
	<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	<th>
	<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	</tr>
	</table>
	## Prompt template


	```
	<\|im_start\|>system
	{system_prompt}<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant
	```

	## Model file specification

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [Qwen2-Math-7B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q2_K.gguf) \| Q2_K \| 2.809 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [Qwen2-Math-7B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q3_K_S.gguf) \| Q3_K_S \| 3.253 GB \| very small, high quality loss \|
	\| [Qwen2-Math-7B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q3_K_M.gguf) \| Q3_K_M \| 3.547 GB \| very small, high quality loss \|
	\| [Qwen2-Math-7B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q3_K_L.gguf) \| Q3_K_L \| 3.808 GB \| small, substantial quality loss \|
	\| [Qwen2-Math-7B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q4_0.gguf) \| Q4_0 \| 4.127 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [Qwen2-Math-7B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q4_K_S.gguf) \| Q4_K_S \| 4.152 GB \| small, greater quality loss \|
	\| [Qwen2-Math-7B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q4_K_M.gguf) \| Q4_K_M \| 4.361 GB \| medium, balanced quality - recommended \|
	\| [Qwen2-Math-7B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q5_0.gguf) \| Q5_0 \| 4.950 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [Qwen2-Math-7B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q5_K_S.gguf) \| Q5_K_S \| 4.950 GB \| large, low quality loss - recommended \|
	\| [Qwen2-Math-7B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q5_K_M.gguf) \| Q5_K_M \| 5.071 GB \| large, very low quality loss - recommended \|
	\| [Qwen2-Math-7B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q6_K.gguf) \| Q6_K \| 5.825 GB \| very large, extremely low quality loss \|
	\| [Qwen2-Math-7B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/Qwen2-Math-7B-Instruct-GGUF/blob/main/Qwen2-Math-7B-Instruct-Q8_0.gguf) \| Q8_0 \| 7.542 GB \| very large, extremely low quality loss - not recommended \|


	## Downloading instruction

	### Command line

	Firstly, install Huggingface Client

	```shell
	pip install -U "huggingface_hub[cli]"
	```

	Then, downoad the individual model file the a local directory

	```shell
	huggingface-cli download tensorblock/Qwen2-Math-7B-Instruct-GGUF --include "Qwen2-Math-7B-Instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
	```

	If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try:

	```shell
	huggingface-cli download tensorblock/Qwen2-Math-7B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf'
	```