Instructions to use sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant with Transformers:

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

pipe = pipeline("text-generation", model="sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant")
model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant

SGLang

How to use sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant 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 "sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant" \
    --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": "sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant",
		"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 "sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant" \
        --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": "sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant with Docker Model Runner:
```
docker model run hf.co/sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant
```

DialoGPT-FinTech-Investment-Banking-Assistant

Fine-tuned DialoGPT-medium for financial conversations, investment advice, and banking operations using LoRA on finance-specific dataset.

Overview

Base Model: microsoft/DialoGPT-medium (355M parameters)
Fine-tuning Method: LoRA (4-bit quantization)
Dataset: Financial Alpaca dataset (2K finance samples)
Training: 2 epochs with optimized hyperparameters

Key Features

Financial market analysis and investment guidance
Banking operations and regulatory compliance
Risk assessment and portfolio management
Conversational interface for financial queries
Optimized for UK fintech and investment banking

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant")
tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant")

# Financial conversation example
prompt = "<|user|> What are the key risks in equity trading? <|bot|>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Applications

Investment banking client advisory
Fintech customer support automation
Financial education and training
Risk management consultation
Portfolio analysis assistance

Training Details

LoRA rank: 8, alpha: 16
4-bit quantization with fp16 precision
Learning rate: 2e-4 with linear scheduling
Batch size: 4, Max length: 512 tokens
Specialized for financial domain conversations

Built for deployment in financial services and fintech environments.

Downloads last month: 89

Safetensors

Model size

0.4B params

Tensor type

F16

Model tree for sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant

Base model

microsoft/DialoGPT-medium

Adapter

(35)

this model

sweatSmile
/

DialoGPT-FinTech-Investment-Banking-Assistant