Sachin016/llama-Nemotron-Personas-India-finetuned

LLaMA 3.2 3B — Nemotron Personas India Fine-tuned

A parameter-efficient fine-tuned version of LLaMA 3.2 3B, trained using the NVIDIA Nemotron Personas dataset with Indian contextual and conversational style tuning.

This model is built using Unsloth for faster and memory-efficient LoRA fine-tuning.

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Model Overview

This model is designed to generate:

• More natural conversational responses
• Indian contextual understanding
• Persona-aware dialogue
• Human-like assistant interactions
• Better response personalization

The model is fine-tuned using LoRA adapters on top of:

unsloth/Llama-3.2-3B-bnb-4bit

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Quick Start

Install Requirements

pip install unsloth
pip install torch transformers peft accelerate bitsandbytes

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Load Model

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Sachin016/llama-Nemotron-Personas-India-finetuned",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)

FastLanguageModel.for_inference(model)

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Run Inference

alpaca_prompt = """### Instruction:
{}

### Input:
{}

### Response:
{}"""

question = "Explain AI in simple words"

inputs = tokenizer(
    [alpaca_prompt.format(question, "", "")],
    return_tensors = "pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens = 128,
    use_cache = True
)

response = tokenizer.batch_decode(outputs)[0].split("### Response:")[1].strip()

print(response)

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Model Details

Property	Value
Base Model	unsloth/Llama-3.2-3B-bnb-4bit
Fine-tuning Method	LoRA
Quantization	4-bit
Framework	Unsloth + HuggingFace PEFT
Max Sequence Length	2048
Language	English
Training Type	Instruction Fine-tuning
Domain	Conversational AI
Specialization	Persona-based responses

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Dataset

This model was fine-tuned using:

• NVIDIA Nemotron Personas Dataset

The dataset helps improve:

• Conversational quality
• Personality consistency
• Human-like dialogue generation
• Contextual response handling

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Training Configuration

TrainingArguments(
    per_device_train_batch_size = 2,
    gradient_accumulation_steps = 4,
    warmup_steps = 5,
    num_train_epochs = 3,
    learning_rate = 2e-4,
    optim = "adamw_8bit",
)

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Prompt Format

This model uses the Alpaca instruction format.

### Instruction:
<your instruction>

### Input:
<optional context>

### Response:
<model output>

---

Upload to Hugging Face

from huggingface_hub import login

login(token="YOUR_HUGGINGFACE_TOKEN")

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "/content/drive/MyDrive/llama_project/finetuned_model",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = True,
)

model.push_to_hub(
    "Sachin016/llama-Nemotron-Personas-India-finetuned",
    token="YOUR_HUGGINGFACE_TOKEN"
)

tokenizer.push_to_hub(
    "Sachin016/llama-Nemotron-Personas-India-finetuned",
    token="YOUR_HUGGINGFACE_TOKEN"
)

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Recommended Hardware

Recommended GPUs:

• NVIDIA T4
• NVIDIA A100
• RTX 3060+
• Any CUDA GPU with 8GB+ VRAM

Can be trained easily on Google Colab using Unsloth.

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Limitations

• May generate hallucinated information
• Performance depends on prompt quality
• Not intended for medical/legal critical systems
• Knowledge limited to base model training cutoff

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License

This project is released under the Apache 2.0 License.

The base model follows Meta's LLaMA 3.2 Community License.

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Acknowledgements

• Unsloth — optimized LLM fine-tuning
• Meta AI — LLaMA 3.2 base model
• NVIDIA — Nemotron Personas dataset
• Hugging Face — model hosting platform

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Author

Made with ❤️ by Sachin016

Hugging Face: https://huggingface.co/Sachin016