Instructions to use thelamapi/next2-air-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next2-air-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="thelamapi/next2-air-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("thelamapi/next2-air-GGUF", dtype="auto")

llama-cpp-python

How to use thelamapi/next2-air-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thelamapi/next2-air-GGUF",
	filename="next2-air.IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use thelamapi/next2-air-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next2-air-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next2-air-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next2-air-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next2-air-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf thelamapi/next2-air-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf thelamapi/next2-air-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf thelamapi/next2-air-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thelamapi/next2-air-GGUF:Q4_K_M

Use Docker

docker model run hf.co/thelamapi/next2-air-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use thelamapi/next2-air-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next2-air-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thelamapi/next2-air-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/thelamapi/next2-air-GGUF:Q4_K_M

SGLang

How to use thelamapi/next2-air-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "thelamapi/next2-air-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thelamapi/next2-air-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "thelamapi/next2-air-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thelamapi/next2-air-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use thelamapi/next2-air-GGUF with Ollama:
```
ollama run hf.co/thelamapi/next2-air-GGUF:Q4_K_M
```

Unsloth Studio new

How to use thelamapi/next2-air-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for thelamapi/next2-air-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for thelamapi/next2-air-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thelamapi/next2-air-GGUF to start chatting

Pi new

How to use thelamapi/next2-air-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf thelamapi/next2-air-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "thelamapi/next2-air-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use thelamapi/next2-air-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf thelamapi/next2-air-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default thelamapi/next2-air-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use thelamapi/next2-air-GGUF with Docker Model Runner:
```
docker model run hf.co/thelamapi/next2-air-GGUF:Q4_K_M
```

Lemonade

How to use thelamapi/next2-air-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thelamapi/next2-air-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.next2-air-GGUF-Q4_K_M

List all available models

lemonade list

Next2-Air (2B)

Türkiye’s Fastest Lightweight Multimodal & Reasoning AI

📖 Overview

Next2-Air is a highly optimized, lightning-fast 2-Billion parameter Vision-Language Model (VLM) built on the Qwen 3.5-2B architecture. Engineered by Lamapi in Türkiye, the "Air" moniker represents its core philosophy: lightweight, incredibly fast, yet surprisingly capable.

While large models dominate cloud servers, Next2-Air is designed to bring top-tier reasoning and multimodal understanding directly to your local machines, edge devices, and everyday applications. By utilizing specialized instruction-tuning and logical reasoning datasets, we have created a 2B model that thinks deeply, processes images flawlessly, and speaks native Turkish and English.

⚡ Highlights

🇹🇷 Perfected in Türkiye: Fine-tuned with cultural nuance, ensuring natural, fluent, and highly accurate Turkish responses.
💨 "Air" Speed & Efficiency: Only 2 Billion parameters. Runs blazingly fast on MacBooks, mid-range PCs, and edge hardware without needing massive GPUs.
🧠 Native Thinking Mode: Despite its small size, it leverages Chain-of-Thought (<think>) to logically deduce answers before speaking.
👁️ Full Vision-Language Support: Analyzes images, reads documents (OCR), and understands visual context just like heavier models.
📚 Massive Context: Supports a staggering 262,144 tokens natively—perfect for summarizing long PDFs or reading extensive codebases locally.

📊 Benchmark Performance

Next2-Air (2B) redefines what is possible in the ultra-lightweight category. Through our custom DPO (Direct Preference Optimization) and SFT processes, it shows noticeable improvements over its base model and strongly competes with heavier 3B-4B models.

📝 Text, Reasoning & Instruction Following

Benchmark	Next2-Air (2B)	Qwen 3.5 (2B)	Gemma-2 (2B)	Llama-3.2 (3B)
MMLU-Pro (Thinking)	68.2%	66.5%	54.1%	68.4%
MMLU-Redux	82.1%	79.6%	75.3%	79.5%
IFEval (Instruction)	82.5%	78.6%	75.8%	77.4%
TAU2-Bench (Agent)	52.4%	48.8%	--	--

👁️ Multimodal & Vision Edge

Next2-Air features a highly capable visual encoder, allowing it to process spatial intelligence, OCR, and document understanding tasks efficiently.

Benchmark	Next2-Air (2B)	Base Qwen3.5-2B
MMMU (General VQA)	66.5%	64.2%
MathVision	78.1%	76.7%
OCRBench	86.0%	84.5%
VideoMME (w/ sub)	77.8%	75.6%

* Enhanced scores in reasoning and OCR are a direct result of Lamapi's specialized bilingual finetuning pipeline focusing on edge-case logic and structural formatting.

🚀 Quickstart & Usage

Next2-Air is fully compatible with the Hugging Face transformers ecosystem and fast inference engines like vLLM and SGLang. Because it's a VLM, you can directly pass images into your prompts.

Python (Transformers)

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

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

model_id = "thelamapi/next2-air"

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 Air, 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

Attribute	Details
Base Architecture	Qwen 3.5 (Causal Language Model + Vision Encoder)
Parameters	2 Billion (Ultra-Lightweight)
Context Length	262,144 tokens natively
Hardware	Optimized for Edge devices, MacBooks (MLX), Consumer GPUs, and low-VRAM environments.
Capabilities	Text Generation, Image Understanding, OCR, Logic & Reasoning (CoT), Bilingual (TR/EN)

🎯 Ideal Use Cases

Next2-Air is the undisputed champion of local, fast inference tasks. It is perfect for:

🔋 Mobile & Edge AI: Deploying smart assistants natively on smartphones or Raspberry Pi without relying on cloud APIs.
⚡ Real-Time OCR & Parsing: Quickly scanning receipts, invoices, or UI screenshots to extract data in milliseconds.
💬 Fast Conversational Bots: Providing instant, low-latency Turkish and English responses for customer service pipelines.
🎮 Gaming & NPC Logic: Acting as a fast reasoning engine for dynamic in-game characters.

📄 License & Open Source

Next2-Air is released under the Apache 2.0 License. We strongly believe in empowering developers, students, and enterprises with accessible, high-speed, reasoning-capable AI.

📞 Contact & Community

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

Next2-Air — Hafif, Hızlı, Akıllı. Uç cihazlardan buluta, Türkiye'nin yeni nesil çevik yapay zekası. 🌬️

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GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

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