Instructions to use Dinesh-Kumar/gemma3-1b-finetuned-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dinesh-Kumar/gemma3-1b-finetuned-v3 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-1b-it")
model = PeftModel.from_pretrained(base_model, "Dinesh-Kumar/gemma3-1b-finetuned-v3")

Transformers

How to use Dinesh-Kumar/gemma3-1b-finetuned-v3 with Transformers:

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

pipe = pipeline("text-generation", model="Dinesh-Kumar/gemma3-1b-finetuned-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dinesh-Kumar/gemma3-1b-finetuned-v3")
model = AutoModelForCausalLM.from_pretrained("Dinesh-Kumar/gemma3-1b-finetuned-v3", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Dinesh-Kumar/gemma3-1b-finetuned-v3 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0
# Run inference directly in the terminal:
llama cli -hf Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0
# Run inference directly in the terminal:
llama cli -hf Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0

Use Docker

docker model run hf.co/Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0

LM Studio
Jan

vLLM

How to use Dinesh-Kumar/gemma3-1b-finetuned-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dinesh-Kumar/gemma3-1b-finetuned-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dinesh-Kumar/gemma3-1b-finetuned-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0

SGLang

How to use Dinesh-Kumar/gemma3-1b-finetuned-v3 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 "Dinesh-Kumar/gemma3-1b-finetuned-v3" \
    --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": "Dinesh-Kumar/gemma3-1b-finetuned-v3",
		"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 "Dinesh-Kumar/gemma3-1b-finetuned-v3" \
        --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": "Dinesh-Kumar/gemma3-1b-finetuned-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Dinesh-Kumar/gemma3-1b-finetuned-v3 with Ollama:
```
ollama run hf.co/Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0
```

Unsloth Studio

How to use Dinesh-Kumar/gemma3-1b-finetuned-v3 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 Dinesh-Kumar/gemma3-1b-finetuned-v3 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 Dinesh-Kumar/gemma3-1b-finetuned-v3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dinesh-Kumar/gemma3-1b-finetuned-v3 to start chatting

Atomic Chat new
Docker Model Runner
How to use Dinesh-Kumar/gemma3-1b-finetuned-v3 with Docker Model Runner:
```
docker model run hf.co/Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0
```

Lemonade

How to use Dinesh-Kumar/gemma3-1b-finetuned-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dinesh-Kumar/gemma3-1b-finetuned-v3:Q8_0

Run and chat with the model

lemonade run user.gemma3-1b-finetuned-v3-Q8_0

List all available models

lemonade list

Product Information Extractor (Fine-tuned Gemma 1B IT)

This model is a fine-tuned version of unsloth/gemma-3-1b-it, optimized for extracting structured product information from noisy or unstructured descriptions.
It outputs a valid JSON object containing:

brand
product (2–3 words)
keywords (5 core keywords without quantity info)
quantity (normalized to number + unit)

Built as part of feature engineering for the Amazon ML Challenge 2025, it converts messy e-commerce listings into clean, ML-ready records.

⚡ Highlights

🧩 Base model: unsloth/gemma-3-1b-it
⚙️ Fine-tuning: LoRA + SFT using PEFT and TRL
🚀 VRAM usage: < 2 GB (VRAM-efficient)
⚡ Speed: Very fast inference
🧠 Output: Strict, deterministic JSON schema
🛍 Use case: Product data cleaning & feature extraction

🧩 Example

Input

[
  {
    "role": "system",
    "content": "You are given a task of extracting information from the product description.\n\nYou must always return this as a valid JSON that follows this structure.\n\n{\n    \"brand\": \"brand_name\",\n    \"product\": \"specifying what product is that (2-3 words max)\",\n    \"keywords\": [\"keyword1\", \"keyword2\", \"keyword3\", \"keyword4\", \"keyword5\"] (Keywords must not include any details regarding quantity or packs or amount),\n    \"quantity\": \"specify the quantity mentioned {format : 'Number' 'any of the allowed quantity'}\"\n}\n\nAllowed units for quantity: ['Gram', 'Kilogram', 'Ounce', 'Pound', 'Fluid Ounce', 'Milliliter', 'Liter', 'Gallon', 'Count', 'Capsule', 'Tea Bag', 'Bag', 'Bottle', 'Box', 'Bucket', 'Can', 'Carton', 'Case', 'Container', 'Jar', 'Kit', 'Pack', 'Packet', 'Pouch', 'Sachet', 'Tin', 'Centimeter', 'Foot', 'Inch', 'Meter', 'Square Foot', 'Na']\n\nRules:\n- Return ONLY valid JSON, nothing else (no explanations or text before/after).\n- Extract 5 keywords that define the product.\n- Keep all text lowercase, without punctuation.\n\nDescription:"
  },
  {
    "role": "user",
    "content": "Item Name: Rani Garam Masala Indian 11 Whole Spices Blend 28oz (800g) ~ All Natural, Salt-Free | Vegan | No Colors | Gluten Friendly | NON-GMO | Kosher | Indian Origin. Bullet Point 1: You'll LOVE our Garam Masala Whole Spice by Rani Brand--Here's Why: ❤️Now KOSHER! 100% Natural, Non-GMO, No Preservatives, Vegan, Gluten Friendly PREMIUM Gourmet Food Grade Spice. ❤️Packed in a no barrier Plastic Jar, let us tell you how important that is when using Indian Spices! Rani is a USA based company selling spices for over 40 years, buy with confidence! ❤️NO FILLERS in any Rani Brand Spices (fillers are commonly used in spices to make them free flowing or lessen the costs of production) usually sodium or like product. Net Wt. 10oz (283g), Product of India ❤️ The whole version of our the Highest rated Garam Masala on Amazon! Product Description: Rani Garam Masala Traditionally from Northern India, garam masala is a staple spice in Indian cookery. Garam, when translated means 'warm' best describes the properties of the blend. Warming ingredients such as cinnamon, mace and ginger give garam masala a mellow, appealing aroma. It is an essential ingredient when making sauces for meat and poultry dishes, as well as a great companion to vegetables and lentils recipes. Garam masala is often used towards the end of the cooking process as an added seasoning and it is often sprinkled over finished dishes to enhance the flavor. Product Type: Masala Whole Spices Blend. Packaging: Plastic Bag. Product of India. Rani's Spices are all natural, fresh & premium quality spices. Value: 28.0 Unit: Ounce."
  }
]

Output (before fine-tuning)

{
  "brand": "Rani",
  "product": "Garam Masala Whole Spices Blend",
  "keywords": ["rani", "garam masala", "whole spices", "28oz", "non gmo", "kosher", "vegan", "gluten friendly"],
  "quantity": "28oz"
}

Output (after fine-tuning)

{
  "brand": "rani",
  "product": "garam masala blend",
  "keywords": ["indian", "spice", "masala", "vegan", "kosher"],
  "quantity": "28 ounce"
}

✅ Fine-tuned version understands semantic intent, removes redundancy, and normalizes units.

🚀 Quick Start

from transformers import pipeline

extractor = pipeline(
    "text-generation",
    model="Dinesh-Kumar/gemma3-1b-finetuned-v3",
    device="cuda"
)

description = """Item Name: Rani Garam Masala Indian 11 Whole Spices Blend 28oz (800g) ~ All Natural, Salt-Free | Vegan | No Colors | Gluten Friendly | NON-GMO | Kosher | Indian Origin\r\nBullet Point 1: You'll LOVE our Garam Masala Whole Spice by Rani Brand--Here's Why:\r\nBullet Point 2: ❤️Now KOSHER! 100% Natural, Non-GMO, No Preservatives, Vegan, Gluten Friendly PREMIUM Gourmet Food Grade Spice.\r\nBullet Point 3: ❤️Packed in a no barrier Plastic Jar, let us tell you how important that is when using Indian Spices! Rani is a USA based company selling spices for over 40 years, buy with confidence!\r\nBullet Point 4: ❤️NO FILLERS in any Rani Brand Spices (fillers are commonly used in spices to make them free flowing or lessen the costs of production) usually sodium or like product. Net Wt. 10oz (283g), Product of India\r\nBullet Point 5: ❤️ The whole version of our the Highest rated Garam Masala on Amazon!\r\nProduct Description: <p><b>Rani Garam Masala</b> Traditionally from Northern Indian, garam masala is a staple spice in Indian cookery. Garam, when translated means “warm” best describes the properties of the blend. Warming ingredients such as cinnamon, mace and ginger give garam masala a mellow, appealing aroma. It is an essential ingredient when making sauces for meat and poultry dishes, as well as a great companion to vegetables and lentils recipes. Garam masala is often used towards the end of the cooking process as an added seasoning and it is often sprinkled over finished dishes to enhance the flavor.</p> Product Type: Masala Whole Spices Blend <br> Packaging: Plastic Bag<br> Product of India <br><br>Did you know most products sold in the U.S. go through 3-7 different supply chains before they are sold to you? This means much more cost & product that is older by the time it reaches you, the end consumer. At Rani (Rani's World Foods) we are the manufacturer, distributor & retailer of Rani Brand products. We are vertically integrated, means only 1 supply chain for best pricing & freshness, makes Rani & Rani Brand products one of kind here on Amazon. <br><br> Rani's Spices are all natural, fresh & premium quality spices... taste the difference in all your dishes. At Rani Foods, we comb the planet for the best ingredients, rather it be high quality coriander from Orissa, or the best Cumin from Gujrat. We go throughout the world purchasing the finest quality spices available, and fully guarantee our products. We are a 100% Family Owned and have been in the Indian food trade for over 50 years. Our goal is your happiness and health. Give our products a try, and experience the difference.\r\nValue: 28.0\r\nUnit: Ounce\r\n"}, {"role": "assistant", "content": "{\"keywords\": [\"rani\", \"garam masala\", \"whole spices\", \"28oz\", \"non gmo\", \"kosher\", \"vegan\", \"gluten friendly\"]}"}]}"""
output = extractor([
    {"role": "system", "content": "Extract product info and return JSON."},
    {"role": "user", "content": description}
], max_new_tokens=256, return_full_text=False)

print(output[0]["generated_text"])

🧠 Training Details

Setting	Value
Base Model	unsloth/gemma-3-1b-it
Fine-tuning	LoRA + SFT
Frameworks	PEFT, TRL, Transformers
Dataset	Derived from Amazon ML Challenge 2025
Objective	Structured JSON extraction
Epochs	3
Learning Rate	2e-4
LoRA Rank	16
Target Modules	q_proj, v_proj
Precision	bfloat16

🧾 JSON Schema

{
  "brand": "brand_name",
  "product": "short product name",
  "keywords": ["keyword1", "keyword2", "keyword3", "keyword4", "keyword5"],
  "quantity": "number unit"
}

Allowed units: ['Gram', 'Kilogram', 'Ounce', 'Pound', 'Fluid Ounce', 'Milliliter', 'Liter', 'Gallon', 'Count', 'Capsule', 'Tea Bag', 'Bag', 'Bottle', 'Box', 'Bucket', 'Can', 'Carton', 'Case', 'Container', 'Jar', 'Kit', 'Pack', 'Packet', 'Pouch', 'Sachet', 'Tin', 'Centimeter', 'Foot', 'Inch', 'Meter', 'Square Foot', 'Na']

💡 Intended Use

E-commerce product metadata extraction
Feature engineering for ML models
Product categorization and deduplication
Lightweight inference environments

🧰 Framework Versions

PEFT: 0.17.1
TRL: 0.23.0
Transformers: 4.56.2
PyTorch: 2.8.0 + cu126
Datasets: 4.2.0
Tokenizers: 0.22.1

📚 Citation

If you use this model, please cite:

@misc{dinesh2025gemma1bextractor,
  title        = {Product Information Extractor (Fine-tuned Gemma 1B IT)},
  author       = {Dinesh Kumar},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/Dinesh-Kumar/gemma3-1b-finetuned-v3}},
  note         = {Fine-tuned for Amazon ML Challenge 2025}
}

Also cite TRL:

@misc{vonwerra2022trl,
  title        = {{TRL: Transformer Reinforcement Learning}},
  author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
  year         = 2020,
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/huggingface/trl}}
}

🏁 Summary

A lightweight, low-VRAM, fine-tuned Gemma 1B IT model capable of producing clean, structured product data directly from raw descriptions — purpose-built for the Amazon ML Challenge 2025 feature-engineering pipeline.

Downloads last month: 4

Safetensors

Model size

1.0B params

Tensor type

BF16

Model tree for Dinesh-Kumar/gemma3-1b-finetuned-v3

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Finetuned

unsloth/gemma-3-1b-it

Adapter

(29)

this model