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

Libraries

How to use minhthien/banking-intent-unsloth-checkpoint with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "minhthien/banking-intent-unsloth-checkpoint")

Transformers

How to use minhthien/banking-intent-unsloth-checkpoint with Transformers:

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

pipe = pipeline("text-generation", model="minhthien/banking-intent-unsloth-checkpoint")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use minhthien/banking-intent-unsloth-checkpoint with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "minhthien/banking-intent-unsloth-checkpoint"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minhthien/banking-intent-unsloth-checkpoint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/minhthien/banking-intent-unsloth-checkpoint

SGLang

How to use minhthien/banking-intent-unsloth-checkpoint 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 "minhthien/banking-intent-unsloth-checkpoint" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minhthien/banking-intent-unsloth-checkpoint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "minhthien/banking-intent-unsloth-checkpoint" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minhthien/banking-intent-unsloth-checkpoint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use minhthien/banking-intent-unsloth-checkpoint 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 minhthien/banking-intent-unsloth-checkpoint 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 minhthien/banking-intent-unsloth-checkpoint to start chatting

Using HuggingFace Spaces for Unsloth

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

Load model with FastModel

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

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

Banking Intent Classifier — Llama 3 8B (LoRA)

A LoRA adapter fine-tuned on top of Llama 3 8B Instruct (4-bit) for banking customer intent classification using the BANKING77 dataset.

Model Details

Base model: unsloth/llama-3-8b-Instruct-bnb-4bit
Model type: Causal LM + LoRA adapter (PEFT)
Task: Intent classification (77 classes)
Language: English
Fine-tuned by: nguyenvmthien
Repository: nguyenvmthien/banking-intent-unsloth
Framework: Unsloth + TRL (SFT) + PEFT 0.18.1

Training Details

Dataset

Sampled from the BANKING77 dataset — 77 fine-grained intent categories for banking customer service queries.

Split	Samples
Train	10,016
Test	3,084

Hyperparameters

Parameter	Value
LoRA rank (r)	16
LoRA alpha	16
LoRA dropout	0
Target modules	q, k, v, o, gate, up, down proj
Learning rate	2e-4
Batch size	2
Gradient accumulation	4
Epochs	1
Optimizer	AdamW 8-bit
LR scheduler	Linear
Max seq length	256
Quantization	4-bit (load_in_4bit)

Prompt Format

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
Classify the intent of the following banking customer request.

### Input:
{customer_message}

### Response:

Usage

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="minhthien/banking-intent-unsloth-checkpoint",
    max_seq_length=256,
    dtype=None,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Or clone the full inference/evaluation code:

git clone https://github.com/nguyenvmthien/banking-intent-unsloth.git
cd banking-intent-unsloth
pip install -r requirements.txt
python scripts/evaluate.py

Evaluation

Evaluation uses log-probability scoring over all 77 candidate intent labels (no text generation needed), which is faster and more deterministic.

Results will be updated after full evaluation run.

Model	Accuracy
Base (zero-shot)	—
Fine-tuned (this model)	—

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Model tree for minhthien/banking-intent-unsloth-checkpoint

Base model

unsloth/llama-3-8b-Instruct-bnb-4bit

Adapter

(156)

this model

minhthien
/

banking-intent-unsloth-checkpoint