Instructions to use QuantFactory/QwQ-14B-Math-v0.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/QwQ-14B-Math-v0.2-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/QwQ-14B-Math-v0.2-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/QwQ-14B-Math-v0.2-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 QuantFactory/QwQ-14B-Math-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/QwQ-14B-Math-v0.2-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 QuantFactory/QwQ-14B-Math-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/QwQ-14B-Math-v0.2-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/QwQ-14B-Math-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/QwQ-14B-Math-v0.2-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/QwQ-14B-Math-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/QwQ-14B-Math-v0.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/QwQ-14B-Math-v0.2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/QwQ-14B-Math-v0.2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/QwQ-14B-Math-v0.2-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/QwQ-14B-Math-v0.2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/QwQ-14B-Math-v0.2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/QwQ-14B-Math-v0.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/QwQ-14B-Math-v0.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.QwQ-14B-Math-v0.2-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/QwQ-14B-Math-v0.2-GGUF

This is quantized version of qingy2024/QwQ-14B-Math-v0.2 created using llama.cpp

Original Model Card

Uploaded model

Developed by: qingy2024
License: apache-2.0
Finetuned from model : unsloth/qwen2.5-14b-bnb-4bit

This model is a fine-tuned version of Qwen 2.5-14B, trained on QwQ 32B Preview's responses to questions from the NuminaMathCoT dataset.

Note: This model uses the standard ChatML template.

At 500 steps, the loss was plateauing so I decided to stop training to prevent excessive overfitting.

Training Details

Base Model: Qwen 2.5-14B
Fine-Tuning Dataset: Verified subset of NuminaMathCoT using Qwen 2.5 3B Instruct as a judge. (the sharegpt-verified-cleaned subset from my dataset).
QLoRA Configuration:
- Rank: 32
- Rank Stabilization: Enabled
Optimization Settings:
- Batch Size: 8
- Gradient Accumulation Steps: 2 (Effective Batch Size: 16)
- Warm-Up Steps: 5
- Weight Decay: 0.01
Training Steps: 500 steps
Hardware Information: A100-80GB

Downloads last month: 66

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

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QuantFactory
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QwQ-14B-Math-v0.2-GGUF

QuantFactory/QwQ-14B-Math-v0.2-GGUF

Original Model Card

Uploaded model

Training Details

Dataset used to train QuantFactory/QwQ-14B-Math-v0.2-GGUF