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Qwen2.5-1.5B-Instruct-Intent-Classifier-QLoRA
Amirkhan184/Qwen2.5-1.5B-Instruct-Intent-Classifier-QLoRA
Qwen2.5-1.5B-Instruct-Intent-Classifier-QLoRA is a text classification model from Amirkhan184. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
This model is designed for customer support systems that require automatic routing of user requests.
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Public
Updated Jul 27, 2026
Repo size
159 MB
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Jul 27, 2026
- Updated
- Jul 27, 2026
- SHA
- b578e252
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Base models
Model card
From the Hugging Face model README
Model Details
| Specification | Details |
|---|---|
| Base Model | Qwen2.5-1.5B-Instruct |
| Fine-tuning Method | QLoRA |
| Task | Intent Classification |
| Domain | Customer Support |
| Framework | Hugging Face Transformers + Unsloth |
| Language | English |
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 | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Max Sequence Length | 1024 |
| Learning Rate | 5e-5 |
| Optimizer | adamw_8bit |
| 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.
| Metric | Score |
|---|---|
| Valid JSON Rate | 100% |
| Intent Accuracy | 86.15% |
| Urgency Accuracy | 92.31% |
| Exact Match Accuracy | 86.15% |
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
| User Message | Model Output |
|---|---|
| "Where is my package? Tracking number is XYZ123" | {"intent": "order_status", "urgency": "medium"} |
| "I want a full refund, this product is broken" | {"intent": "refund_request", "urgency": "high"} |
| "How do I change my password?" | {"intent": "account_access", "urgency": "low"} |
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
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Created
- Jul 27, 2026
- Updated
- Jul 27, 2026