A small LoRA adapter for Qwen2.5-14B-Instruct, trained on databricks-dolly-15k.
This is a demonstration artifact. It exists to show a full open-model loop end to
end (fine-tune, merge, quantize, serve), not to be a state-of-the-art model.
Trained on dolly's concise, human-written responses, the adapter shifts the base
model away from its long multi-section default toward shorter, more direct
answers.
Prompt: Explain why the sky is blue to a ten year old.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3import torch
4
5base = "Qwen/Qwen2.5-14B-Instruct"
6tok = AutoTokenizer.from_pretrained(base)
7model = AutoModelForCausalLM.from_pretrained(base, dtype=torch.bfloat16, device_map="auto")
8model = PeftModel.from_pretrained(model, "ArgusForge/qwen2.5-14b-dolly-lora")
