Instructions to use prxshetty/gemma-3-finance-mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prxshetty/gemma-3-finance-mix with Transformers:

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

pipe = pipeline("text-generation", model="prxshetty/gemma-3-finance-mix")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prxshetty/gemma-3-finance-mix", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prxshetty/gemma-3-finance-mix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prxshetty/gemma-3-finance-mix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prxshetty/gemma-3-finance-mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prxshetty/gemma-3-finance-mix

SGLang

How to use prxshetty/gemma-3-finance-mix 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 "prxshetty/gemma-3-finance-mix" \
    --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": "prxshetty/gemma-3-finance-mix",
		"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 "prxshetty/gemma-3-finance-mix" \
        --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": "prxshetty/gemma-3-finance-mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use prxshetty/gemma-3-finance-mix 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 prxshetty/gemma-3-finance-mix 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 prxshetty/gemma-3-finance-mix to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prxshetty/gemma-3-finance-mix to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="prxshetty/gemma-3-finance-mix",
    max_seq_length=2048,
)

Docker Model Runner
How to use prxshetty/gemma-3-finance-mix with Docker Model Runner:
```
docker model run hf.co/prxshetty/gemma-3-finance-mix
```

Gemma-3 Finance Mix

A lightweight Gemma-3 270M model fine-tuned for financial Q&A, causal-lm/finance, news-headline sentiment and retail-investor discourse.

Overview

Item	Details
Base checkpoint	`google/gemma-3-270m-it`
Fine-tune method	LoRA (PEFT) with Unsloth
Training run	1 epoch • 325,528 blended examples • 100 steps
Trainable params	30.4 M / 298 M (10.18 %)
Loss	4.11 → 2.74
Hardware	2 × T4-16GB (Collab Free Tier)
License	Apache-2.0
Intended use	Educational & research

Datasets

Dataset	Size	Focus
`gbharti/finance-alpaca`	52 k	Instruction Q-A on corporate finance & investing
`Balaji173/finance_news_sentiment`	217 k	Bullish/bearish labels for news headlines
`winddude/reddit_finance_43_250k`	250 k	Reddit finance post–comment pairs
`causal-lm/finance`	31 k	Analytical prompts & causal reasoning in economics/markets

All shards were concatenated and wrapped with the Gemma chat template before training.

Responsible use

Disclose AI assistance, double-check outputs, and do not rely on this model for trading decisions. The author and base-model creators accept no liability for financial losses.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for prxshetty/gemma-3-finance-mix

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Adapter

(69)

this model

prxshetty
/

gemma-3-finance-mix

Gemma-3 Finance Mix

Overview

Datasets

Responsible use

Model tree for prxshetty/gemma-3-finance-mix

Datasets used to train prxshetty/gemma-3-finance-mix