Instructions to use khadim-hussain/qwen3-14b-stem-qa-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use khadim-hussain/qwen3-14b-stem-qa-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 khadim-hussain/qwen3-14b-stem-qa-gguf:F16
# Run inference directly in the terminal:
llama cli -hf khadim-hussain/qwen3-14b-stem-qa-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf khadim-hussain/qwen3-14b-stem-qa-gguf:F16
# Run inference directly in the terminal:
llama cli -hf khadim-hussain/qwen3-14b-stem-qa-gguf:F16

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 khadim-hussain/qwen3-14b-stem-qa-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf khadim-hussain/qwen3-14b-stem-qa-gguf:F16

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 khadim-hussain/qwen3-14b-stem-qa-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf khadim-hussain/qwen3-14b-stem-qa-gguf:F16

Use Docker

docker model run hf.co/khadim-hussain/qwen3-14b-stem-qa-gguf:F16

LM Studio
Jan

vLLM

How to use khadim-hussain/qwen3-14b-stem-qa-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khadim-hussain/qwen3-14b-stem-qa-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": "khadim-hussain/qwen3-14b-stem-qa-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/khadim-hussain/qwen3-14b-stem-qa-gguf:F16

Ollama
How to use khadim-hussain/qwen3-14b-stem-qa-gguf with Ollama:
```
ollama run hf.co/khadim-hussain/qwen3-14b-stem-qa-gguf:F16
```

Unsloth Studio

How to use khadim-hussain/qwen3-14b-stem-qa-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 khadim-hussain/qwen3-14b-stem-qa-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 khadim-hussain/qwen3-14b-stem-qa-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for khadim-hussain/qwen3-14b-stem-qa-gguf to start chatting

How to use khadim-hussain/qwen3-14b-stem-qa-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf khadim-hussain/qwen3-14b-stem-qa-gguf:F16

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": "khadim-hussain/qwen3-14b-stem-qa-gguf:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use khadim-hussain/qwen3-14b-stem-qa-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf khadim-hussain/qwen3-14b-stem-qa-gguf:F16

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 "khadim-hussain/qwen3-14b-stem-qa-gguf:F16" \
  --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"

Docker Model Runner
How to use khadim-hussain/qwen3-14b-stem-qa-gguf with Docker Model Runner:
```
docker model run hf.co/khadim-hussain/qwen3-14b-stem-qa-gguf:F16
```

Lemonade

How to use khadim-hussain/qwen3-14b-stem-qa-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull khadim-hussain/qwen3-14b-stem-qa-gguf:F16

Run and chat with the model

lemonade run user.qwen3-14b-stem-qa-gguf-F16

List all available models

lemonade list

Hermes Agent

How to use khadim-hussain/qwen3-14b-stem-qa-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 khadim-hussain/qwen3-14b-stem-qa-gguf:F16

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 khadim-hussain/qwen3-14b-stem-qa-gguf:F16

Run Hermes

hermes

Atomic Chat

Qwen3-14B STEM Q&A (GGUF)

GGUF quantized version of Qwen3-14B fine-tuned for STEM Q&A tasks. Ready for use with Ollama, llama.cpp, LM Studio, and other GGUF-compatible tools.

Author: Khadim Hussain

Available Versions

Model	Size	Description
qwen3-14b-stem-qa	~117MB	LoRA adapter only
qwen3-14b-stem-qa-merged	~28GB	Full merged model (bf16)
qwen3-14b-stem-qa-gguf	9-30GB	GGUF quantized (this repo)

Files

Filename	Quant	Size	Description
`qwen3-14b-stem-qa-f16.gguf`	F16	29.5GB	Full 16-bit precision, best quality
`qwen3-14b-stem-qa-q4_k_m.gguf`	Q4_K_M	9GB	4-bit quantization, good balance of size/quality

Usage with Ollama

# Download the GGUF file and Modelfile
# Then create and run:
ollama create qwen3-14b-stem -f Modelfile
ollama run qwen3-14b-stem

Modelfile

FROM ./qwen3-14b-stem-qa-q4_k_m.gguf

TEMPLATE """<|im_start|>system
You are a helpful assistant specialized in STEM subjects.<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER num_ctx 4096
PARAMETER num_predict 2048

Usage with llama.cpp

./llama-cli -m qwen3-14b-stem-qa-q4_k_m.gguf \
  -p "<|im_start|>user\nWhat is DNA?<|im_end|>\n<|im_start|>assistant\n" \
  -n 512

Usage with LM Studio

Download qwen3-14b-stem-qa-q4_k_m.gguf
Import into LM Studio
Use the ChatML template

Training Details

Metric	Value
Base Model	Qwen/Qwen3-14B
Train Loss	0.461
Eval Loss	0.692
Training Examples	4,260
Evaluation Examples	474

Acknowledgments

Qwen Team (Alibaba) - Qwen3-14B base model
llama.cpp - GGUF format and tools
Unsloth - Fine-tuning framework
Hugging Face - TRL, PEFT, Transformers

Citation

If you use this model, please cite:

@misc{hussain2026qwen3-stem,
  author = {Hussain, Khadim},
  title = {Qwen3-14B STEM Q&A: Fine-tuned for Science Education},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/khadim-hussain/qwen3-14b-stem-qa-gguf}
}

License

Apache 2.0 (inherited from Qwen3)

Downloads last month: 9

GGUF

Model size

15B params

Architecture

qwen3

Hardware compatibility

4-bit

16-bit

Model tree for khadim-hussain/qwen3-14b-stem-qa-gguf

Base model

Qwen/Qwen3-14B-Base

Finetuned

Qwen/Qwen3-14B

Quantized

(203)

this model