Text Generation
GGUF
English
qwen3
quantized
q4_k_m
f16
stem
science
education
ollama
llama-cpp
conversational
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
- llama.cpp
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
- Pi
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
Upload README.md with huggingface_hub
Browse files
README.md
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| 1 |
+
---
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| 2 |
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base_model: Qwen/Qwen3-14B
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tags:
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- qwen3
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- gguf
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- quantized
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- q4_k_m
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- f16
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- stem
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- science
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| 11 |
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- education
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- ollama
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- llama-cpp
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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---
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| 19 |
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# Qwen3-14B STEM Q&A (GGUF)
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| 21 |
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| 22 |
+
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.
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| 23 |
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| 24 |
+
**Author:** Khadim Hussain
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| 25 |
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|
| 26 |
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## Available Versions
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| 27 |
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| 28 |
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| Model | Size | Description |
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| 29 |
+
|-------|------|-------------|
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| 30 |
+
| [qwen3-14b-stem-qa](https://huggingface.co/khadim-hussain/qwen3-14b-stem-qa) | ~117MB | LoRA adapter only |
|
| 31 |
+
| [qwen3-14b-stem-qa-merged](https://huggingface.co/khadim-hussain/qwen3-14b-stem-qa-merged) | ~28GB | Full merged model (bf16) |
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| 32 |
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| **qwen3-14b-stem-qa-gguf** | 9-30GB | GGUF quantized (this repo) |
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| 33 |
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## Files
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| 35 |
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| 36 |
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| Filename | Quant | Size | Description |
|
| 37 |
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|----------|-------|------|-------------|
|
| 38 |
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| `qwen3-14b-stem-qa-f16.gguf` | F16 | 29.5GB | Full 16-bit precision, best quality |
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| 39 |
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| `qwen3-14b-stem-qa-q4_k_m.gguf` | Q4_K_M | 9GB | 4-bit quantization, good balance of size/quality |
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| 40 |
+
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| 41 |
+
## Usage with Ollama
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| 42 |
+
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| 43 |
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```bash
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| 44 |
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# Download the GGUF file and Modelfile
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| 45 |
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# Then create and run:
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| 46 |
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ollama create qwen3-14b-stem -f Modelfile
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| 47 |
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ollama run qwen3-14b-stem
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```
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### Modelfile
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| 51 |
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```
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FROM ./qwen3-14b-stem-qa-q4_k_m.gguf
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| 54 |
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| 55 |
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TEMPLATE """<|im_start|>system
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| 56 |
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You are a helpful assistant specialized in STEM subjects.<|im_end|>
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| 57 |
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<|im_start|>user
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| 58 |
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{{ .Prompt }}<|im_end|>
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| 59 |
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<|im_start|>assistant
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| 60 |
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"""
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| 61 |
+
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PARAMETER stop "<|im_end|>"
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| 63 |
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PARAMETER temperature 0.7
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| 64 |
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PARAMETER top_p 0.9
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| 65 |
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PARAMETER num_ctx 4096
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| 66 |
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PARAMETER num_predict 2048
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| 67 |
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```
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| 68 |
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| 69 |
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## Usage with llama.cpp
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| 70 |
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| 71 |
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```bash
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| 72 |
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./llama-cli -m qwen3-14b-stem-qa-q4_k_m.gguf \
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| 73 |
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-p "<|im_start|>user\nWhat is DNA?<|im_end|>\n<|im_start|>assistant\n" \
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| 74 |
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-n 512
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```
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| 76 |
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## Usage with LM Studio
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| 78 |
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| 79 |
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1. Download `qwen3-14b-stem-qa-q4_k_m.gguf`
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| 80 |
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2. Import into LM Studio
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| 81 |
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3. Use the ChatML template
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| 82 |
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## Training Details
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| 84 |
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| 85 |
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| Metric | Value |
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| 86 |
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|--------|-------|
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| 87 |
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| Base Model | Qwen/Qwen3-14B |
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| Train Loss | 0.461 |
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| Eval Loss | 0.692 |
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| Training Examples | 4,260 |
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| Evaluation Examples | 474 |
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| 92 |
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## Acknowledgments
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| 94 |
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| 95 |
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- [Qwen Team (Alibaba)](https://github.com/QwenLM/Qwen) - Qwen3-14B base model
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| 96 |
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- [llama.cpp](https://github.com/ggerganov/llama.cpp) - GGUF format and tools
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| 97 |
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- [Unsloth](https://github.com/unslothai/unsloth) - Fine-tuning framework
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- [Hugging Face](https://huggingface.co) - TRL, PEFT, Transformers
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| 99 |
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## Citation
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If you use this model, please cite:
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| 103 |
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```bibtex
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| 105 |
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@misc{hussain2026qwen3-stem,
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| 106 |
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author = {Hussain, Khadim},
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title = {Qwen3-14B STEM Q&A: Fine-tuned for Science Education},
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| 108 |
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year = {2026},
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| 109 |
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publisher = {Hugging Face},
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url = {https://huggingface.co/khadim-hussain/qwen3-14b-stem-qa-gguf}
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| 111 |
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}
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| 112 |
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```
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## License
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| 115 |
+
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Apache 2.0 (inherited from Qwen3)
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