README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - qwen2.5
  - fine-tuned
  - lora
  - chemistry
base_model: Qwen/Qwen2.5-14B-Instruct

qwen-quantum

This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct using LoRA (Low-Rank Adaptation) on a chemistry dataset.

Model Description

Fine-tuned Qwen2.5-14B model for chemistry domain tasks.

Available Formats

• GGUF: qwen_quantum_merged-q4_k_m.gguf - Quantized for efficient inference with llama.cpp

Usage

Using GGUF (with llama.cpp, Ollama, LM Studio, etc.)

# Download the GGUF file
huggingface-cli download Kylan12/qwen-quantum qwen_quantum_merged-q4_k_m.gguf

# Use with llama.cpp
./llama.cpp/build/bin/llama-cli -m qwen_quantum_merged-q4_k_m.gguf -p "Your prompt here"

Using HuggingFace Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Kylan12/qwen-quantum")
tokenizer = AutoTokenizer.from_pretrained("Kylan12/qwen-quantum")

prompt = "What is the IUPAC name for..."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0]))

Training Details

• Base Model: Qwen/Qwen2.5-14B-Instruct
• Training Method: LoRA (Low-Rank Adaptation)
• Dataset: camel-ai/chemistry
• LoRA Rank: 16
• LoRA Alpha: 16
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Limitations

This model inherits the limitations of the base Qwen2.5-14B-Instruct model and may have additional domain-specific limitations due to the fine-tuning dataset.

Citation

If you use this model, please cite:

@misc{qwen_quantum,
  author = {Your Name},
  title = {qwen-quantum},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Kylan12/qwen-quantum}
}

License

This model is released under the Apache 2.0 license, consistent with the base Qwen model.