Instructions to use sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant with Transformers:

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

pipe = pipeline("text-generation", model="sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-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-Financial-Market-Sentiment-Trading-Assistant")
model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-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-Financial-Market-Sentiment-Trading-Assistant with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant

SGLang

How to use sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-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-Financial-Market-Sentiment-Trading-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-Financial-Market-Sentiment-Trading-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-Financial-Market-Sentiment-Trading-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-Financial-Market-Sentiment-Trading-Assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant with Docker Model Runner:
```
docker model run hf.co/sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant
```

DialoGPT-Financial-Market-Sentiment-Trading-Assistant

Fine-tuned DialoGPT-small for financial news sentiment analysis and market sentiment interpretation for trading and investment decisions.

Overview

Base Model: microsoft/DialoGPT-small (117M parameters)
Fine-tuning Method: LoRA (4-bit quantization)
Dataset: Financial news sentiment dataset (1.5K samples)
Training: 3 epochs with optimized hyperparameters

Key Features

Real-time financial news sentiment analysis
Market sentiment interpretation for trading decisions
Bullish/Bearish/Neutral sentiment classification
Conversational interface for market analysis
Optimized for trading desks and investment research

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant")
tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant")

# Market sentiment analysis example
prompt = "<|user|> What's the market sentiment for this news: Tesla stock surges after record delivery numbers <|bot|>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Applications

Trading desk sentiment analysis
Algorithmic trading signal generation
Investment research and market analysis
Hedge fund market sentiment monitoring
Financial news interpretation for portfolio decisions

Training Details

LoRA rank: 8, alpha: 16
4-bit NF4 quantization with fp16 precision
Learning rate: 3e-4 with linear scheduling
Batch size: 8, Max length: 256 tokens
3 epochs on curated financial sentiment data

Specialized for high-frequency market sentiment analysis in trading environments.

Downloads last month: 24

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for sweatSmile/DialoGPT-Financial-Market-Sentiment-Trading-Assistant

Base model

microsoft/DialoGPT-small

Adapter

(21)

this model

sweatSmile
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DialoGPT-Financial-Market-Sentiment-Trading-Assistant