Instructions to use venkat1701/gemma-2b-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use venkat1701/gemma-2b-finetuned with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2b-it-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "venkat1701/gemma-2b-finetuned")

Transformers

How to use venkat1701/gemma-2b-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="venkat1701/gemma-2b-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("venkat1701/gemma-2b-finetuned")
model = AutoModelForCausalLM.from_pretrained("venkat1701/gemma-2b-finetuned")
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 venkat1701/gemma-2b-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "venkat1701/gemma-2b-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "venkat1701/gemma-2b-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/venkat1701/gemma-2b-finetuned

SGLang

How to use venkat1701/gemma-2b-finetuned 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 "venkat1701/gemma-2b-finetuned" \
    --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": "venkat1701/gemma-2b-finetuned",
		"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 "venkat1701/gemma-2b-finetuned" \
        --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": "venkat1701/gemma-2b-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use venkat1701/gemma-2b-finetuned 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 venkat1701/gemma-2b-finetuned 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 venkat1701/gemma-2b-finetuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for venkat1701/gemma-2b-finetuned to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="venkat1701/gemma-2b-finetuned",
    max_seq_length=2048,
)

Docker Model Runner
How to use venkat1701/gemma-2b-finetuned with Docker Model Runner:
```
docker model run hf.co/venkat1701/gemma-2b-finetuned
```

Gemma 2B Fine-tuned with LoRA

This is a fine-tuned version of unsloth/gemma-2b-it-bnb-4bit using LoRA (Low-Rank Adaptation).

Model Details

Base Model: unsloth/gemma-2b-it-bnb-4bit
Method: LoRA fine-tuning with PEFT
Quantization: 4-bit (via bitsandbytes)
Framework: Transformers + PEFT
Model Type: Causal Language Model
Language: English
License: Apache 2.0

Usage

Installation

pip install transformers peft torch accelerate bitsandbytes

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load tokenizer and base model
tokenizer = AutoTokenizer.from_pretrained("venkat1701/gemma-2b-finetuned")
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/gemma-2b-it-bnb-4bit",
    device_map="auto",
    trust_remote_code=True
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "venkat1701/gemma-2b-finetuned")
model.eval()

# Generate text
prompt = "Explain quantum computing in simple terms:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=256,
        temperature=0.7,
        top_p=0.9,
        do_sample=True
    )

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Using Hugging Face Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/venkat1701/gemma-2b-finetuned"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({
    "inputs": "Explain quantum computing:",
    "parameters": {"max_length": 256, "temperature": 0.7}
})
print(output)

Deployment

The model includes a FastAPI server and Docker support for easy deployment. See the repository for deployment scripts.

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Model Card Authors [optional]

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Model Card Contact

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Framework versions

PEFT 0.18.0

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Safetensors

Model size

2B params

Tensor type

F32

F16

Model tree for venkat1701/gemma-2b-finetuned

Base model

unsloth/gemma-2b-it-bnb-4bit

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(13)

this model

Paper for venkat1701/gemma-2b-finetuned

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 46