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

Libraries

How to use QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF with Transformers:

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

llama-cpp-python

How to use QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF",
	filename="Qwen2.5-7B-Instruct-MathCoder.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-7B-Instruct-MathCoder-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 QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2.5-7B-Instruct-MathCoder-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 QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Qwen2.5-7B-Instruct-MathCoder-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 QuantFactory/Qwen2.5-7B-Instruct-MathCoder-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 QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF to start chatting

Pi new

How to use QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Qwen2.5-7B-Instruct-MathCoder-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": "Qwen2.5-7B-Instruct-MathCoder-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-7B-Instruct-MathCoder-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF

This is quantized version of DeepMount00/Qwen2.5-7B-Instruct-MathCoder created using llama.cpp

Original Model Card

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using Qwen/Qwen2.5-7B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Qwen/Qwen2.5-7B-Instruct
    #no parameters necessary for base model
  - model: Qwen/Qwen2.5-Math-7B-Instruct
    parameters:
      density: 0.5
      weight: 0.5
  - model: Qwen/Qwen2.5-Coder-7B-Instruct
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: Qwen/Qwen2.5-7B-Instruct
parameters:
  normalize: false
  int8_mask: true
dtype: float16

Downloads last month: 381

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF

Qwen/Qwen2.5-7B-Instruct

Qwen/Qwen2.5-Coder-7B-Instruct

Qwen/Qwen2.5-Math-7B-Instruct

Merge model

this model

Paper for QuantFactory/Qwen2.5-7B-Instruct-MathCoder-GGUF

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 18