Amirkhan184/Qwen2.5-1.5B-Instruct-Intent-Classifier-QLoRA

Model Details

Intended Use

This model is designed for customer support systems that require automatic routing of user requests.

Example use cases:

• —FAQ routing
• —Account support classification
• —Order-related request detection
• —Human escalation detection

Example use cases

• —FAQ / knowledge-base routing
• —Account / billing / order request classification
• —Automatic human-escalation detection
• —Urgency-based prioritization of tickets

Supported output format

{
  "intent": "order_status | refund_request | account_access | ...",
  "urgency": "low | medium | high"
}

Training

The model was fine-tuned using QLoRA with:

• —Unsloth
• —Hugging Face Transformers
• —TRL SFTTrainer
• —PEFT LoRA adapters

Key hyperparameters | Hyperparameter | Value | |---|---| | Quantization | 4-bit (QLoRA) | | LoRA Rank (r) | 32 | | LoRA Alpha | 32 | | LoRA Dropout | 0.1 | | Target Modules | qproj, kproj, vproj, oproj, gateproj, upproj, downproj | | Max Sequence Length | 1024 | | Learning Rate | 5e-5 | | Optimizer | adamw8bit | | Effective Batch Size | 8 | | Precision | bf16 | | Epochs | 3 |

Dataset

The model was fine-tuned on a combination of:

• —Bitext Customer Support Dataset
• —Custom curated customer-support examples created for this project

Evaluation

The model was evaluated on a held-out test set.

Test Samples: 65

Note: The test set is relatively small (65 samples). Results should be interpreted with caution and may not fully represent real-world performance.

Usage

This model is a LoRA adapter and requires the base model:

Qwen/Qwen2.5-1.5B-Instruct

Example:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = "Qwen/Qwen2.5-1.5B-Instruct"
adapter_model = "Amirkhan184/Qwen2.5-1.5B-Instruct-Intent-Classifier-QLoRA"

tokenizer = AutoTokenizer.from_pretrained(base_model)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto"
)

model = PeftModel.from_pretrained(
    model,
    adapter_model
)

Example predictions

Limitations

• —This adapter is optimized for customer support intent classification.
• —It may not generalize well to unrelated domains.
• —Multi-intent detection is not currently supported.
• —The model performance depends on the quality and coverage of the provided intent categories.

Related Project

This LoRA adapter is part of the Smart Support AI project, an AI-powered customer support system featuring:

• —LangGraph-based workflow orchestration
• —Retrieval-Augmented Generation (RAG) with FAISS
• —QLoRA fine-tuned intent classification
• —Structured JSON responses
• —Automated evaluation pipeline

GitHub Repository: https://github.com/AmirKhan2400/smart-support-ai