Instructions to use techpro-saida/banking-slm-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use techpro-saida/banking-slm-v1 with Transformers:

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

pipe = pipeline("text-generation", model="techpro-saida/banking-slm-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("techpro-saida/banking-slm-v1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use techpro-saida/banking-slm-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/techpro-saida/banking-slm-v1

SGLang

How to use techpro-saida/banking-slm-v1 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 "techpro-saida/banking-slm-v1" \
    --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": "techpro-saida/banking-slm-v1",
		"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 "techpro-saida/banking-slm-v1" \
        --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": "techpro-saida/banking-slm-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use techpro-saida/banking-slm-v1 with Docker Model Runner:
```
docker model run hf.co/techpro-saida/banking-slm-v1
```

🏦 Banking-SLM-v1

Small Language Model (SLM) for Banking Customer Support — fine-tuned from DialoGPT

This model is optimized for financial and banking domain conversations, including customer support automation, FAQs, and personalized query handling.

It can handle:

Credit card and loan inquiries
Account opening and transaction queries
Policy explanation and chatbot-style interactions

🚀 Model Overview

Property	Value
Base Model	`microsoft/DialoGPT-medium`
Fine-tuned Dataset	Custom Banking & FinTech dialogues
Framework	Hugging Face Transformers
Trained On	Azure GPU VM (NVIDIA T4)
Fine-tuning Epochs	**
Learning Rate	2e-4
Tokenizer	AutoTokenizer
Total Parameters	~355.61M
Trainable Parameters (LoRA)	~**

🧠 Use Cases

Banking chatbots and conversational AI
Automated customer support (loan, account, credit cards)
FinTech knowledge assistants
Smart reply systems for banking helpdesks

🧩 Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("techpro-saida/banking-slm-v1")
model = AutoModelForCausalLM.from_pretrained("techpro-saida/banking-slm-v1")

prompt = "Hi, can you tell me my credit card limit?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

#(or) use pipeline
from transformers import pipeline

slm = pipeline(
    "text-generation",
    model="./banking-slm-v1",
    tokenizer=tokenizer,
    max_new_tokens=60,
    temperature=0.6,
    top_p=0.9
)

prompt = "How do I reset my net-banking password?"

result = slm(prompt)
print(result[0]["generated_text"])

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Base model

microsoft/DialoGPT-medium

Finetuned

(93)

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