Model Description
- Base Model: Qwen/Qwen3-14B
- Type: LoRA Adapter (QLoRA 4-bit)
- Training: Fine-tuned on STEM Q&A dataset
Usage
from unsloth import FastLanguageModel # Load model with adaptermodel, tokenizer = FastLanguageModel.from_pretrained( "khadim-hussain/qwen3-14b-stem-qa", max_seq_length=2048, load_in_4bit=True,) prompt = "<|im_start|>user\nWhat is DNA?<|im_end|>\n<|im_start|>assistant\n"inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=256)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Table with columns: Metric, Value| Metric | Value |
|---|
| Train Loss | 0.461 |
| Eval Loss | 0.692 |
| Training Examples | 4,260 |
| Evaluation Examples | 474 |
| LoRA Rank | 32 |
| LoRA Alpha | 64 |
Training Configuration
- Method: QLoRA (4-bit quantization)
- Optimizer: AdamW 8-bit
- Learning Rate: 1e-4
- Batch Size: 1 (with gradient accumulation 16)
- Framework: Unsloth + TRL
GGUF Version
GGUF quantized versions are available for use with Ollama/llama.cpp:
Running the adapter in llama.cpp
Ollama 0.34.1 and later no longer load LoRA adapters, so in Ollama use the merged GGUF above. To keep the adapter separate, convert it with llama.cpp's convert_lora_to_gguf.py (pass --base-model-id Qwen/Qwen3-14B, because the adapter config points at Unsloth's 4-bit training copy) and load it with llama-server --lora on top of a Qwen3-14B GGUF. Tested steps and numbers: Running a LoRA adapter after Ollama removed support.
Acknowledgments
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}}
License
Apache 2.0 (inherited from Qwen3)