Instructions to use souvik18/Roy-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use souvik18/Roy-v1 with Transformers:

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

pipe = pipeline("text-generation", model="souvik18/Roy-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("souvik18/Roy-v1")
model = AutoModelForCausalLM.from_pretrained("souvik18/Roy-v1")
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 souvik18/Roy-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "souvik18/Roy-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "souvik18/Roy-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/souvik18/Roy-v1

SGLang

How to use souvik18/Roy-v1 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 "souvik18/Roy-v1" \
    --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": "souvik18/Roy-v1",
		"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 "souvik18/Roy-v1" \
        --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": "souvik18/Roy-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use souvik18/Roy-v1 with Docker Model Runner:
```
docker model run hf.co/souvik18/Roy-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

!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
        )

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

F16

Model tree for souvik18/Roy-v1

Quantizations

1 model