| --- |
| 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 |
| base_model: |
| - thelamapi/next2-fast |
| --- |
| |
|  |
|
|
| [](https://discord.gg/XgH4EpyPD2) |
|
|
| # ⚡ Next 2 Fast (4B) |
|
|
| ### *Global Speed, Multimodal Intelligence — Engineered by Lamapi* |
|
|
| [](https://opensource.org/licenses/MIT) |
| []() |
| [](https://huggingface.co/Lamapi/next-2-fast) |
|
|
| --- |
|
|
| ## 🌍 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): |
|
|
| ```python |
| 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): |
|
|
| ```python |
| 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. |
|
|
| --- |
|
|
| ## 📄 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](mailto:lamapicontact@gmail.com) |
| * 🤗 **HuggingFace:** [Company Page](https://huggingface.co/thelamapi) |
|
|
| --- |
|
|
| > **Next 2 Fast** — *Global Intelligence. Lightning Speed. Powered by Lamapi.* |
|
|
| [](https://huggingface.co/Lamapi) |
