Instructions to use ngbaoan/intent-banking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ngbaoan/intent-banking with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-7b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "ngbaoan/intent-banking")

Transformers

How to use ngbaoan/intent-banking with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ngbaoan/intent-banking")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ngbaoan/intent-banking", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ngbaoan/intent-banking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ngbaoan/intent-banking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ngbaoan/intent-banking",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ngbaoan/intent-banking

SGLang

How to use ngbaoan/intent-banking with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ngbaoan/intent-banking" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ngbaoan/intent-banking",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ngbaoan/intent-banking" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ngbaoan/intent-banking",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use ngbaoan/intent-banking with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ngbaoan/intent-banking to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ngbaoan/intent-banking to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ngbaoan/intent-banking to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ngbaoan/intent-banking",
    max_seq_length=2048,
)

Docker Model Runner
How to use ngbaoan/intent-banking with Docker Model Runner:
```
docker model run hf.co/ngbaoan/intent-banking
```

Qwen2.5-7B Banking Intent Classification

This is a LoRA adapter fine-tuned on the BANKING77 dataset to perform fine-grained intent classification in the banking domain. The model is based on unsloth/Qwen2.5-7B and trained using the Unsloth library for highly efficient training.

Model Details

Model Type: Causal Language Model with LoRA adapter
Developer: ngbaoan
Base Model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
Language: English
Task: Intent Classification
Dataset: BANKING77 (77 distinct banking-related intents)

Performance

The model was evaluated on the test set and achieved the following results:

Accuracy: 92.29% (0.9229)
Macro F1-Score: 0.85
Weighted F1-Score: 0.92

(Note: Some labels in the dataset subset might have 0 support, which affects the macro average. For supported intents, the F1 score ranges from 0.80 to 1.00).

Intended Use

This model is designed to classify user queries related to banking operations (e.g., card activation, lost cards, top-up failures, exchange rates, etc.) into one of 77 specific intents.

Example Input:

"I tried to top up my account using a card but it failed, what should I do?"

Example Output:

top_up_failed

Training Details

The model was fine-tuned efficiently using Unsloth with 4-bit quantization and LoRA.

Training Hyperparameters

LoRA Rank (r): 64
LoRA Alpha: 64
Batch Size: 2 (per device)
Gradient Accumulation Steps: 4
Learning Rate: 5.0e-5
Optimizer: adamw_8bit
LR Scheduler: cosine
Warmup Steps: 20
Weight Decay: 0.01
Epochs: 6
Max Sequence Length: 512

How to Get Started with the Model

Since this is a LoRA adapter, you need to load the base model and then apply these PEFT weights. The easiest way is using the unsloth library or standard transformers.

from unsloth import FastLanguageModel
import torch

max_seq_length = 512

# 1. Load the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "ngbaoan/intent-banking", # Your Hugging Face repo
    max_seq_length = max_seq_length,
    dtype = None,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

# 2. Format your prompt
prompt = """Instruct: Classify the following banking query into the correct intent.
Query: I lost my card yesterday and I need a replacement.
Intent: """

inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")

# 3. Generate the response
outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
print(tokenizer.batch_decode(outputs, skip_special_tokens = True)[0])

Framework Versions

PEFT 0.18.1
Transformers
Unsloth
TRL

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