Instructions to use QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF",
	filename="Qwen2.5-Math-14B-Instruct.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-Math-14B-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 QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-Math-14B-Instruct-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-Math-14B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-Math-14B-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 QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2.5-Math-14B-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 QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Qwen2.5-Math-14B-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 QuantFactory/Qwen2.5-Math-14B-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 QuantFactory/Qwen2.5-Math-14B-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 QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF to start chatting

Pi new

How to use QuantFactory/Qwen2.5-Math-14B-Instruct-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-Math-14B-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": "QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Qwen2.5-Math-14B-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 QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Math-14B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Qwen2.5-Math-14B-Instruct-GGUF

This is quantized version of qingy2019/Qwen2.5-Math-14B-Instruct created using llama.cpp

Original Model Card

Uploaded model

Developed by: qingy2019
License: apache-2.0
Finetuned from model : unsloth/qwen2.5-14b-instruct-bnb-4bit

This Qwen 2.5 model was trained 2x faster with Unsloth and Huggingface's TRL library.

I fine-tuned it for 400 steps on garage-bAInd/Open-Platypus with a batch size of 3.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	36.71
IFEval (0-Shot)	60.66
BBH (3-Shot)	47.02
MATH Lvl 5 (4-Shot)	28.47
GPQA (0-shot)	16.33
MuSR (0-shot)	19.63
MMLU-PRO (5-shot)	48.12

Downloads last month: 251

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

60.660
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

47.020
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

28.470
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

16.330
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

19.630
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

48.120