Instructions to use tensorblock/Qwen2.5-Math-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/Qwen2.5-Math-7B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tensorblock/Qwen2.5-Math-7B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/Qwen2.5-Math-7B-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/Qwen2.5-Math-7B-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 tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/Qwen2.5-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 serve -hf tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/Qwen2.5-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/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/Qwen2.5-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/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/Qwen2.5-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/Qwen2.5-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/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K

SGLang

How to use tensorblock/Qwen2.5-Math-7B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tensorblock/Qwen2.5-Math-7B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tensorblock/Qwen2.5-Math-7B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tensorblock/Qwen2.5-Math-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K
```

Unsloth Studio

How to use tensorblock/Qwen2.5-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/Qwen2.5-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/Qwen2.5-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/Qwen2.5-Math-7B-Instruct-GGUF to start chatting

How to use tensorblock/Qwen2.5-Math-7B-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 tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K

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": "tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tensorblock/Qwen2.5-Math-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K
```

Lemonade

How to use tensorblock/Qwen2.5-Math-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen2.5-Math-7B-Instruct-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use tensorblock/Qwen2.5-Math-7B-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 tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K

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 tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tensorblock/Qwen2.5-Math-7B-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 tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K

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 "tensorblock/Qwen2.5-Math-7B-Instruct-GGUF:Q2_K" \
  --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"

morriszms commited on Nov 16, 2024

Commit

ad5c137

verified ·

1 Parent(s): 7147009

Update README.md

Browse files

Files changed (1) hide show

README.md +20 -12

README.md CHANGED Viewed

@@ -29,8 +29,16 @@ This repo contains GGUF format model files for [Qwen/Qwen2.5-Math-7B-Instruct](h
 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).
 ## Prompt template
 ```
 <|im_start|>system
 {system_prompt}<|im_end|>
@@ -43,18 +51,18 @@ The files were quantized using machines provided by [TensorBlock](https://tensor
 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
-| [Qwen2.5-Math-7B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q2_K.gguf) | Q2_K | 2.809 GB | smallest, significant quality loss - not recommended for most purposes |
-| [Qwen2.5-Math-7B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3.253 GB | very small, high quality loss |
-| [Qwen2.5-Math-7B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q3_K_M.gguf) | Q3_K_M | 3.547 GB | very small, high quality loss |
-| [Qwen2.5-Math-7B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q3_K_L.gguf) | Q3_K_L | 3.808 GB | small, substantial quality loss |
-| [Qwen2.5-Math-7B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q4_0.gguf) | Q4_0 | 4.127 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [Qwen2.5-Math-7B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4.152 GB | small, greater quality loss |
-| [Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4.361 GB | medium, balanced quality - recommended |
-| [Qwen2.5-Math-7B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q5_0.gguf) | Q5_0 | 4.950 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
-| [Qwen2.5-Math-7B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q5_K_S.gguf) | Q5_K_S | 4.950 GB | large, low quality loss - recommended |
-| [Qwen2.5-Math-7B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5.071 GB | large, very low quality loss - recommended |
-| [Qwen2.5-Math-7B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q6_K.gguf) | Q6_K | 5.825 GB | very large, extremely low quality loss |
-| [Qwen2.5-Math-7B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/tree/main/Qwen2.5-Math-7B-Instruct-Q8_0.gguf) | Q8_0 | 7.542 GB | very large, extremely low quality loss - not recommended |
 ## Downloading instruction

 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).
+<div style="text-align: left; margin: 20px 0;">
+    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
+        Run them on the TensorBlock client using your local machine ↗
+    </a>
+</div>
 ## Prompt template
 ```
 <|im_start|>system
 {system_prompt}<|im_end|>
 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
+| [Qwen2.5-Math-7B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q2_K.gguf) | Q2_K | 2.809 GB | smallest, significant quality loss - not recommended for most purposes |
+| [Qwen2.5-Math-7B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3.253 GB | very small, high quality loss |
+| [Qwen2.5-Math-7B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q3_K_M.gguf) | Q3_K_M | 3.547 GB | very small, high quality loss |
+| [Qwen2.5-Math-7B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q3_K_L.gguf) | Q3_K_L | 3.808 GB | small, substantial quality loss |
+| [Qwen2.5-Math-7B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_0.gguf) | Q4_0 | 4.127 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [Qwen2.5-Math-7B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4.152 GB | small, greater quality loss |
+| [Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4.361 GB | medium, balanced quality - recommended |
+| [Qwen2.5-Math-7B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q5_0.gguf) | Q5_0 | 4.950 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [Qwen2.5-Math-7B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q5_K_S.gguf) | Q5_K_S | 4.950 GB | large, low quality loss - recommended |
+| [Qwen2.5-Math-7B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5.071 GB | large, very low quality loss - recommended |
+| [Qwen2.5-Math-7B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q6_K.gguf) | Q6_K | 5.825 GB | very large, extremely low quality loss |
+| [Qwen2.5-Math-7B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/Qwen2.5-Math-7B-Instruct-GGUF/blob/main/Qwen2.5-Math-7B-Instruct-Q8_0.gguf) | Q8_0 | 7.542 GB | very large, extremely low quality loss - not recommended |
 ## Downloading instruction