Instructions to use sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor with Transformers:

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

pipe = pipeline("text-generation", model="sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor")
model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor"
# 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-Financial-Wealth-Management-Advisor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor

SGLang

How to use sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor 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-Financial-Wealth-Management-Advisor" \
    --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-Financial-Wealth-Management-Advisor",
		"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-Financial-Wealth-Management-Advisor" \
        --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-Financial-Wealth-Management-Advisor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor with Docker Model Runner:
```
docker model run hf.co/sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor
```

DialoGPT-Financial-Wealth-Management-Advisor

Fine-tuned DialoGPT-small for financial advisory conversations, wealth management guidance, and comprehensive investment consultation services.

Overview

Base Model: microsoft/DialoGPT-small (117M parameters)
Fine-tuning Method: LoRA (4-bit quantization)
Dataset: Financial Q&A dataset (1K expert-level samples)
Training: 3 epochs with optimized hyperparameters

Key Features

Comprehensive financial advisory consultations
Investment portfolio analysis and recommendations
Risk assessment and management strategies
Tax planning and wealth optimization advice
Retirement and financial planning guidance
Client-focused conversational interface

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor")
tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor")

# Financial advisory consultation example
prompt = "<|user|> As my financial advisor, please help me understand: How do foreign currency fluctuations affect my international investments? <|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

Wealth management client consultations
Investment advisory services automation
Financial planning and retirement guidance
Private banking client support
Robo-advisor conversation engines
Financial education and client onboarding

Training Details

LoRA rank: 8, alpha: 16
4-bit NF4 quantization with fp16 precision
Learning rate: 2e-4 with linear scheduling
Batch size: 8, Max length: 320 tokens
3 epochs on curated financial advisory dataset

Optimized for sophisticated wealth management and investment advisory conversations in professional financial services environments.

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor

Base model

microsoft/DialoGPT-small

Adapter

(21)

this model

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DialoGPT-Financial-Wealth-Management-Advisor