Instructions to use abhishekyadav16/gemma-2b-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhishekyadav16/gemma-2b-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="abhishekyadav16/gemma-2b-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("abhishekyadav16/gemma-2b-finetuned")
model = AutoModelForMultimodalLM.from_pretrained("abhishekyadav16/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 Settings

vLLM

How to use abhishekyadav16/gemma-2b-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abhishekyadav16/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": "abhishekyadav16/gemma-2b-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abhishekyadav16/gemma-2b-finetuned

SGLang

How to use abhishekyadav16/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 "abhishekyadav16/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": "abhishekyadav16/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 "abhishekyadav16/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": "abhishekyadav16/gemma-2b-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abhishekyadav16/gemma-2b-finetuned with Docker Model Runner:
```
docker model run hf.co/abhishekyadav16/gemma-2b-finetuned
```

🤖 Gemma 2B Fine-tuned A fine-tuned version of google/gemma-2b trained using Supervised Fine-Tuning (SFT) with the TRL library. The model is quantized to 4-bit using bitsandbytes for efficient inference.

🚀 Quick Start pythonfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch

model_id = "abhishekyadav16/gemma-2b-finetuned"

4-bit quantization config

bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, )

tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, quantization_config=bnb_config, device_map="auto", )

Inference

prompt = "What is machine learning?" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=200, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id, )

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

🏋️ Training Details ParameterValueBase Modelgoogle/gemma-2bFine-tuning MethodSFT (Supervised Fine-Tuning)LibraryTRL + TransformersQuantization4-bit (bitsandbytes)Parameters~1.1BHardwareGoogle Colab (T4 GPU)FrameworkPyTorch

📦 Model Architecture

Architecture: Gemma (LlamaForCausalLM-based) Parameters: 1,116.3M (~1.1B) Context Length: 8192 tokens Quantization: NF4 4-bit via bitsandbytes

💡 Use Cases

Conversational AI / Chatbot Text generation and completion Question answering Instruction following

⚠️ Limitations

This model is fine-tuned for research and learning purposes. May produce incorrect or biased outputs — not recommended for production use without further evaluation. Performance is limited by the base Gemma 2B capacity.

👨‍💻 Author Abhishek Yadav — AI Engineer | LLMs · RAG · Agentic AI · MCP

🌐 Portfolio: 3-d-portfolio.vercel.app 💼 LinkedIn: abhishek-yadav72 🐙 GitHub: CodeBy-Abhishek 🤗 HuggingFace: abhishekyadav16

📄 License This model is released under the Gemma License. Please review Google's terms before commercial use.

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Safetensors

Model size

1B params

Tensor type

F32

BF16

Model tree for abhishekyadav16/gemma-2b-finetuned

Base model

google/gemma-2b

Quantized

(35)

this model