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mukizone/qwen2-1.5b-customer-support-sft-lora-v1
qwen2-1.5b-customer-support-sft-lora-v1 is a text generation model from mukizone. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
This repository contains a LoRA adapter fine-tuned on Qwen2-1.5B-Instruct for structured customer support ticket classification.
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From the Hugging Face model README
This repository contains a LoRA adapter fine-tuned on Qwen2-1.5B-Instruct for structured customer support ticket classification.
⚠️ This repository contains only the LoRA adapter weights, not the base model.
The base model used: Qwen/Qwen2-1.5B-Instruct (Apache-2.0 licensed)
This model is intended for:
Not intended for:
Trained on a synthetic customer support dataset generated for research and educational purposes.
No proprietary or real customer data was used.
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "mukizone/qwen2-1.5b-customer-support-sft-lora-v1")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
prompt = """
### Instruction:
You are an customer support classifier.
Return ONLY a valid JSON object with:
- category
- priority
- response
Allowed categories:
- delivery_issue
- refund_request
- damaged_item
- product_query
You MUST choose only from these.
### Input:
My package arrived damaged and I want a replacement.
### Output:
"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
This adapter is released under Apache-2.0. The base model license applies to the underlying weights.
Users must comply with the base model’s license: https://huggingface.co/Qwen/Qwen2-1.5B-Instruct