khadim-hussain/qwen3-14b-stem-qa

Qwen3-14B STEM Q&A (LoRA Adapter)

This is a LoRA adapter fine-tuned on Qwen3-14B for STEM Q&A tasks.

Author: Khadim Hussain

For the merged full model (larger download, standalone use), see: khadim-hussain/qwen3-14b-stem-qa-merged

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 adapter
model, 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

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:

• —See: khadim-hussain/qwen3-14b-stem-qa-gguf

Acknowledgments

• —Qwen Team (Alibaba) - Qwen3-14B base model
• —Unsloth - Fast 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}
}

License

Apache 2.0 (inherited from Qwen3)