README.md

metadata

language:
  - en
  - tr
  - de
  - fr
  - es
  - it
  - pt
  - ru
  - zh
  - ja
  - ko
  - hi
  - ar
  - nl
  - pl
  - uk
  - vi
  - th
  - id
  - cs
license: mit
tags:
  - global-ai
  - multilingual
  - vision-language-model
  - multimodal
  - lamapi
  - next-2-fast
  - next-series
  - 4b
  - efficient
  - gemma-3
  - transformer
  - text-generation
  - reasoning
  - artificial-intelligence
  - nlp
pipeline_tag: image-text-to-text
datasets:
  - mlabonne/FineTome-100k
  - ITCL/FineTomeOs
  - Gryphe/ChatGPT-4o-Writing-Prompts
  - dongguanting/ARPO-SFT-54K
  - OpenSPG/KAG-Thinker-training-dataset
  - uclanlp/Brief-Pro
  - CognitiveKernel/CognitiveKernel-Pro-SFT
  - QuixiAI/dolphin-r1
library_name: transformers

⚡ Next 2 Fast (4B)

Global Speed, Multimodal Intelligence — Engineered by Lamapi

---

🌍 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.

Model	Params	MMLU (5-shot) %	MMLU-Pro %	GSM8K %	MATH %
⚡ Next 2 Fast	4B	85.1	67.4	83.5	71.2
Gemma 3 4B	4B	82.0	64.5	80.1	68.0
Llama 3.2 3B	3B	63.4	52.1	45.2	42.8
Phi-3.5 Mini	3.8B	84.0	66.0	82.0	69.5

---

🚀 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

Feature	Description
🌍 True Multilingualism	Fluent in English, Turkish, German, French, Spanish, and more. No "translation-ese."
🧠 Visual Intelligence	Can read charts, identify objects, and reason about visual scenes effectively.
⚡ High Efficiency	Designed for speed. Ideal for edge devices, local deployment, and real-time apps.
💻 Code & Math	Strong capabilities in Python coding, debugging, and solving mathematical problems.
🛡️ Global Alignment	Fine-tuned with a diverse dataset to ensure safety and neutrality across cultures.

---

🎯 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.

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📄 License

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

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📞 Contact & Ecosystem

We are Lamapi.

• 📧 Contact: Mail
• 🤗 HuggingFace: Company Page

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