thelamapi/next2-fast


⚡ Next 2 Fast (4B)

Global Speed, Multimodal Intelligence — Engineered by Lamapi

 ![Language: Multilingual]() 

🌍 Overview

Next 2 Fast is a state-of-the-art 4-billion parameter Multimodal Vision-Language Model (VLM) designed for high-performance reasoning across languages and modalities.

Developed by Lamapi, a leading AI research lab in Türkiye, this model represents a leap in efficiency, bridging the gap between massive commercial models and accessible, open-source intelligence. Built upon the Gemma 3 architecture and refined with our proprietary SFT and DPO techniques, Next 2 Fast is not just a language model—it is a global reasoning engine that sees, understands, and communicates fluently in English, Turkish, German, French, Spanish, and 25+ other languages.

Why Next 2 Fast?

• —⚡ Global Performance: Tuned for complex reasoning in English and multilingual contexts, outperforming larger models.
• —👁️ Vision & Text: Seamlessly processes images and text to generate code, descriptions, and analysis.
• —🚀 Unmatched Speed: Optimized for low-latency inference, making it ~2x faster than previous generations.
• —🔋 Efficient Deployment: Runs smoothly on consumer hardware (8GB VRAM) using 4-bit/8-bit quantization.

🏆 Benchmark Performance

Next 2 Fast delivers flagship-level performance in a compact 4B size, proving that efficiency does not require sacrificing intelligence.

<table> <thead> <tr> <th>Model</th> <th>Params</th> <th>MMLU (5-shot) %</th> <th>MMLU-Pro %</th> <th>GSM8K %</th> <th>MATH %</th> </tr> </thead> <tbody> <tr class="next" style="background-color: #e6f3ff; font-weight: bold;"> <td data-label="Model">⚡ Next 2 Fast</td> <td>4B</td> <td data-label="MMLU (5-shot) %">85.1</td> <td data-label="MMLU-Pro %">67.4</td> <td data-label="GSM8K %">83.5</td> <td data-label="MATH %"><strong>71.2</strong></td> </tr> <tr> <td data-label="Model">Gemma 3 4B</td> <td>4B</td> <td data-label="MMLU (5-shot) %">82.0</td> <td data-label="MMLU-Pro %">64.5</td> <td data-label="GSM8K %">80.1</td> <td data-label="MATH %">68.0</td> </tr> <tr> <td data-label="Model">Llama 3.2 3B</td> <td>3B</td> <td data-label="MMLU (5-shot) %">63.4</td> <td data-label="MMLU-Pro %">52.1</td> <td data-label="GSM8K %">45.2</td> <td data-label="MATH %">42.8</td> </tr> <tr> <td data-label="Model">Phi-3.5 Mini</td> <td>3.8B</td> <td data-label="MMLU (5-shot) %">84.0</td> <td data-label="MMLU-Pro %">66.0</td> <td data-label="GSM8K %">82.0</td> <td data-label="MATH %">69.5</td> </tr> </tbody> </table>

🚀 Quick Start

Next 2 Fast is fully compatible with the Hugging Face transformers library.

🖼️ Multimodal Inference (Vision + Text):

from transformers import AutoTokenizer, AutoModelForCausalLM, AutoProcessor
from PIL import Image
import torch

model_id = "thelamapi/next2-fast"

# Load Model & Processor
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load Image
image = Image.open("image.jpg")

# Create Multimodal Prompt
messages = [
  {
    "role": "system",
    "content": [{"type": "text", "text": "You are Next-2, an AI assistant created by Lamapi. Provide concise and accurate analysis."}]
  },
  {
    "role": "user",
    "content": [
        {"type": "image", "image": image},
        {"type": "text", "text": "Analyze this image and explain in English."}
    ]
  }
]

# Process & Generate
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=prompt, images=[image], return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(output[0], skip_special_tokens=True))

💬 Text-Only Chat (Global Reasoning):

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Lamapi/next-2-fast"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are Next 2 Fast, an advanced AI assistant."},
    {"role": "user", "content": "Explain the concept of entropy in thermodynamics simply."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

🌐 Key Features

🎯 Mission

At Lamapi, our mission is to build the Next generation of intelligence that is accessible to everyone, everywhere.

Next 2 Fast proves that world-class AI innovation isn't limited to Silicon Valley. By combining efficient architecture with high-quality global datasets, we provide a powerful tool for researchers, developers, and businesses worldwide.

📄 License

This model is open-sourced under the MIT License. It is free for academic and commercial use.

📞 Contact & Ecosystem

We are Lamapi.

• —📧 Contact: Mail
• —🤗 HuggingFace: Company Page

Next 2 Fast — Global Intelligence. Lightning Speed. Powered by Lamapi.