JARVIS

🚀 J.A.R.V.I.S.

J.A.R.V.I.S., standing for "Just a Rather Very Intelligent System", is an advanced AI language model inspired by the iconic assistant in Iron Man. It offers context - aware and high - fidelity natural language processing capabilities for diverse applications.

⚠️ Important Note

This is only the base model and a lighter version for public release. To use the more advanced version, please use the available space provided.

✨ Features

Powerful Text Generation Engine

J.A.R.V.I.S. is the core of an AI solution that can generate high - quality, natural, and creative text, helping you create inspiring and engaging content.

Seamless Integration, Limitless Possibilities

With high flexibility, J.A.R.V.I.S. can be seamlessly integrated into various platforms and systems you use, such as web applications, chatbots, agent modes, tools, system integrations, and automated workflows, adapting well to available resource capacities.

Scalable & Resource - Aware

Regardless of your computing environment, from lightweight devices to enterprise - class infrastructure, J.A.R.V.I.S. can be optimized for optimal performance, ensuring both efficiency and responsiveness.

Versatile Use Cases

It is suitable for a wide range of needs, including creative content creation, writing assistance, and developing interactive text - based applications, giving you unlimited innovation freedom.

Agentic Use

For example usage, please refer to the Qwen3 Documentation and the [Qwen - Agent GitHub repository](https://github.com/QwenLM/Qwen - Agent).

J.A.R.V.I.S. is not just an AI model; it is an intelligent partner that can transform your ideas into words with intelligence and high flexibility. For more detailed information about this model, you can explore all its advantages by referring to the explanation of the underlying base model here!

🔧 Technical Details

Discover the amazing power of J.A.R.V.I.S., your smart and reliable AI companion. It can confidently answer your toughest questions, explain complex ideas simply, translate languages smoothly, write clean and efficient code, summarize lengthy information into understandable points, spark your creativity with original content, and guide you through study or research with expert advice. This is just the beginning of what J.A.R.V.I.S. can do for you.

📚 Documentation

Notebook

Although this model has a large size and many parameters, you can run it on Google Colab for free using the optimized script. Click here to access it.

Ollama

The source of the J.A.R.V.I.S. model on Ollama can be found here.

ollama run hadad/JARVIS

# 4-bit (Q4_K_M)
ollama run hadad/JARVIS:4bit

🚀 Quick Start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

# Model
model = "hadadrjt/JARVIS"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(
    model,
    torch_dtype="auto",
    device_map="auto"
)

# User input
input = "Tell me about yourself" # Insert your message here.

# Build chat-style prompt
messages = [{"role": "user", "content": input}]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)

# Tokenize input
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Streaming setup
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

# Generate response with streaming
with torch.inference_mode():
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=32768,
        temperature=0.6,
        top_p=0.95,
        min_p=0,
        top_k=20,
        repetition_penalty=1.0,
        streamer=streamer
    )

📄 License

This project is licensed under the Apache - 2.0 license.

💡 Usage Tip

Although J.A.R.V.I.S. delivers impressive results, it may reflect biases from its training data and occasionally produce incorrect outputs. It is not intended for real - time safety - critical applications without human oversight. It is recommended to implement human review workflows, monitor outputs for fairness and accuracy, and update the model with domain - specific data over time.

Acknowledgments and Contact

This work builds upon the Qwen3 - 14B open - source foundation. For feedback, collaboration, or support, please reach out to Hadad Darajat at hadad@linuxmail.org. Contributions that help J.A.R.V.I.S. continue to evolve are welcome.

Citation

@misc{jarvis,
  title   = {J.A.R.V.I.S.},
  author  = {Hadad Darajat},
  year    = {2025},
  license = {Apache - 2.0},
  base    = {Qwen/Qwen3-14B}
}