next2.5-i1-GGUF

📖 Overview

Next 2.5 is a state-of-the-art 4-Billion parameter Vision-Language Model (VLM), built upon the powerful Qwen 3.5-4B foundation. Developed and heavily fine-tuned in Türkiye by Lamapi, Next 2.5 pushes the boundaries of what mid-sized models can achieve in 2026.

We have taken the already exceptional multimodal and reasoning capabilities of the base model and supercharged them through customized instruction tuning, culturally aware Turkish datasets, and enhanced visual-spatial reasoning tasks. Next 2.5 is designed to "think before it speaks", seamlessly analyzing complex images, videos, and intricate mathematical problems natively.

⚡ Highlights

📊 Comprehensive Benchmarks

Through rigorous SFT and DPO phases, Next 2.5 (4B) sets a new standard for the ~4B parameter weight class. It consistently outperforms modern edge models and punches far above its weight, rivaling 8B-11B models in vision and reasoning.

📝 Text, Knowledge & Reasoning (Sub-5B Class)

👁️ Vision & Multimodal Edge

Next 2.5's visual cortex allows it to rival or beat proprietary nano-models from leading labs and larger 11B parameter open-weight models.

🚀 Quickstart & Usage

Next 2.5 is fully compatible with the Hugging Face transformers ecosystem and modern serving frameworks like vLLM and SGLang. Because it is natively multimodal, you can pass images directly into the prompt.

Python (Transformers)

Make sure you have the latest transformers, torch, torchvision, and pillow installed.

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

model_id = "thelamapi/next2.5"

model = AutoModelForCausalLM.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id) # For vision.
tokenizer = AutoTokenizer.from_pretrained(model_id)


# Create a message in chat format
messages = [
  {"role": "system","content": [{"type": "text", "text": "You are Next2.5, a smart and concise AI assistant trained by Lamapi. Always respond in the user's language. Proudly made in Turkey."}]},

  {
      "role": "user","content": [
      {"type": "text", "text": "Write a highly optimized Rust function to calculate the Fibonacci sequence using memoization"}
    ]
  }
]

# Prepare input with Tokenizer
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
inputs = processor(text=prompt, return_tensors="pt")

# Remove 'mm_token_type_ids' if it's not needed for text-only generation
if "mm_token_type_ids" in inputs:
    del inputs["mm_token_type_ids"]


# Output from the model
output = model.generate(**inputs, do_sample=True, temperature=0.7, max_new_tokens=128)
print(tokenizer.decode(output[0], skip_special_tokens=True))

🧩 Model Specifications

🎯 Ideal Use Cases

Next 2.5 (4B) strikes the perfect balance between high-end reasoning and hardware efficiency. It is perfectly suited for:

• 🕵️ Complex Document Analysis: Upload massive PDFs or images of documents and extract structured, reasoned JSON outputs.
• 🎓 Educational Tutoring: Its native <think> capabilities make it an excellent tutor that explains its mathematical steps to students.
• 🤖 Autonomous Agents: Strong Tool Calling capabilities let you build desktop agents or web-browsing bots locally.
• 🇹🇷 Advanced Turkish NLP: Finally, a mid-size multimodal model that understands Turkish idioms, grammar, and context as well as it does English.

📄 License & Open Source

Next 2.5 is released under the Apache 2.0 License. We support the open-source community and encourage developers to build commercial applications, conduct research, and innovate freely using this model.

📞 Contact & Community

• 📧 Email:lamapicontact@gmail.com
• 🤗 HuggingFace: Lamapi
• 💬 Discord: Join the Lamapi Community