Instructions to use thelamapi/next2.5-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next2.5-i1-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="thelamapi/next2.5-i1-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/next2.5-i1-GGUF", dtype="auto")

llama-cpp-python

How to use thelamapi/next2.5-i1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thelamapi/next2.5-i1-GGUF",
	filename="next2.5.i1-IQ1_M.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/next2.5-i1-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/next2.5-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next2.5-i1-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/next2.5-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next2.5-i1-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/next2.5-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf thelamapi/next2.5-i1-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/next2.5-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thelamapi/next2.5-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/thelamapi/next2.5-i1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use thelamapi/next2.5-i1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next2.5-i1-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/next2.5-i1-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/next2.5-i1-GGUF:Q4_K_M

SGLang

How to use thelamapi/next2.5-i1-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/next2.5-i1-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/next2.5-i1-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/next2.5-i1-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/next2.5-i1-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/next2.5-i1-GGUF with Ollama:
```
ollama run hf.co/thelamapi/next2.5-i1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use thelamapi/next2.5-i1-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/next2.5-i1-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/next2.5-i1-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/next2.5-i1-GGUF to start chatting

Pi new

How to use thelamapi/next2.5-i1-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/next2.5-i1-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/next2.5-i1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use thelamapi/next2.5-i1-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/next2.5-i1-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/next2.5-i1-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use thelamapi/next2.5-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/thelamapi/next2.5-i1-GGUF:Q4_K_M
```

Lemonade

How to use thelamapi/next2.5-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thelamapi/next2.5-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.next2.5-i1-GGUF-Q4_K_M

List all available models

lemonade list

🚀 Next 2.5 (4B)

Türkiye’s Advanced Native Multimodal & Reasoning AI

📖 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

🇹🇷 Tailored in Türkiye: Flawless bilingual proficiency (TR/EN) with deep cultural and contextual awareness.
🧠 Native Thinking Mode: By default, it uses <think>...</think> blocks to reason through complex logic, math, and coding tasks before answering.
👁️ Unified Vision-Language: Natively understands images, documents (OCR), and hour-long videos without breaking a sweat.
📈 Class-Leading Performance: Outperforms heavyweights in its parameter class (Gemma-3-4B, Phi-4-Mini) and even rivals closed-source edge models like GPT-5-Nano.
📚 Massive Context Limit: Supports up to 262,144 tokens natively, perfect for massive codebases or multi-document analysis.

📊 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)

Benchmark	Next 2.5 (4B) 🚀	Qwen 3.5 (4B)	Gemma-3 (4B)	Phi-4-Mini (3.8B)	Llama-3.2 (3B)
MMLU-Pro	81.6%	79.1%	76.5%	78.2%	68.4%
MMLU-Redux	90.2%	88.8%	86.1%	87.5%	79.5%
IFEval (Instruction)	91.2%	89.8%	85.4%	88.1%	77.4%
HMMT (Reasoning)	78.3%	74.0%	70.2%	72.8%	--
LiveCodeBench v6	58.4%	55.8%	51.0%	54.2%	45.1%
TAU2-Bench (Agent)	82.1%	79.9%	72.4%	75.0%	--

👁️ 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.

Benchmark	Next 2.5 (4B) 🚀	Qwen 3.5 (4B)	Gemini-2.5 Flash-Lite	GPT-5-Nano	Llama-3.2 (11B Vision)
MMMU (General VQA)	79.5%	77.6%	73.4%	75.8%	71.2%
MathVision	76.8%	74.6%	52.1%	62.2%	50.5%
OCRBench	86.5%	85.0%	82.5%	75.3%	74.1%
VideoMME (w/ sub)	84.8%	83.5%	74.6%	71.7%	68.9%
CountBench (Spatial)	97.5%	96.3%	79.2%	80.0%	--

* Benchmark improvements are driven by our high-quality Turkish reasoning datasets and specialized DPO alignment focusing on multi-step logic. Empty cells (--) indicate scores not officially reported for that model.

🚀 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

Attribute	Details
Base Architecture	Qwen 3.5 (Causal Language Model + Vision Encoder)
Parameters	4 Billion
Context Length	262,144 tokens natively (Extensible to 1M+ via YaRN)
Training Stage	SFT + RLHF/DPO (Turkish + English focus)
Hardware	Runs comfortably on consumer GPUs (e.g., RTX 3060/4060 with 8GB VRAM in FP16, or less via Quantization)
Capabilities	Text Generation, Image Understanding, Video Summarization, OCR, Code Generation, Tool Use (Agentic)

🎯 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

Next 2.5 — Sınırları aşan görsel algı ve derin düşünme yeteneği. Türkiye'nin küresel yapay zeka vizyonu. 🌍

Downloads last month: 119

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

View +1 variant

Model tree for thelamapi/next2.5-i1-GGUF

Base model

thelamapi/next2.5

Quantized

(4)

this model

thelamapi
/

next2.5-i1-GGUF