README.md

---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- mistral
- chatbot
---
#  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](https://huggingface.co/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
        )