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 Desktop
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
```

prxshetty commited on Sep 2, 2025

Commit

c847f2b

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@@ -1,22 +1,59 @@
 ---
-base_model: unsloth/gemma-3-270m-it
 tags:
-- text-generation-inference
-- transformers
 - unsloth
-- gemma3_text
-- trl
-license: apache-2.0
 language:
 - en
 ---
-# Uploaded  model
-- **Developed by:** prxshetty
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gemma-3-270m-it
-This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+license: apache-2.0
+library_name: transformers
+base_model: google/gemma-3-270m-it
 tags:
+- gemma
+- finance
+- lora
 - unsloth
+- text-generation
+- instruction-following
+datasets:
+- gbharti/finance-alpaca
+- Balaji173/finance_news_sentiment
+- winddude/reddit_finance_43_250k
+- causal-lm/finance
+pipeline_tag: text-generation
 language:
 - en
 ---
+# Gemma-3 Finance Mix
+A lightweight **Gemma-3 270M** model fine-tuned for *financial Q&A, 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.
+---