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souvik18/
Roy-v1

Name: Roy-v1
Availability: InStock

text-generation-inference

endpoints_compatible

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Roy-v1

Roy is a personal AI assistant model created and fine-tuned by Souvik Pramanick.
Designed to be helpful, conversational, and practical for everyday tasks such as learning, coding, problem solving, and general assistance.

Creator

Founder & Trainer:
Souvik Pramanick
GitHub: https://github.com/Souvik18p
HuggingFace: https://huggingface.co/souvik18

Roy is an independent project built with the vision of creating a smart, friendly, and customizable AI assistant.

What Roy Can Do

Roy is capable of:

Natural conversation and assistance
Answering general knowledge questions
Solving math and logical problems
Helping with coding and debugging
Writing emails, stories, and content
Explaining concepts in simple language
Brainstorming ideas and learning support

Model Details

Model Name: Roy-v1
Parameters: 7B
Architecture: LLaMA-based
Tensor Type: F16
Format: Safetensors
License: Open for community usage

Base Model: souvik18/Roy-v1

Quantized Versions (Community)

Thanks to @mradermacher for providing GGUF quants of Roy-v1:

https://huggingface.co/mradermacher/Roy-v1-GGUF

These versions allow Roy to run on:

CPU only systems
Low VRAM GPUs
Mobile / local apps via llama.cpp, ollama, koboldcpp

Quick Usage

Using HuggingFace Transformers

python

!pip install -U transformers datasets accelerate bitsandbytes peft huggingface_hub

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

MODEL_ID = "souvik18/Roy-v1"

# 4bit config – works best on Kaggle
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

print(" Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
tokenizer.pad_token = tokenizer.eos_token

print(" Loading model (4bit)...")
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    quantization_config=bnb_config,
    device_map="auto"
)

print("\n Roy-v1 Loaded Successfully!")

while True:
    text = input("You: ")
    if text.lower() in ["exit","quit"]:
        break

    prompt = f"[INST] {text} [/INST]"

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        out = model.generate(
            **inputs,
            max_new_tokens=200,
            temperature=0.7,
            top_p=0.9,
            do_sample=True
        )