Instructions to use thelamapi/next2-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next2-0.8b with Transformers:

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

pipe = pipeline("image-text-to-text", model="thelamapi/next2-0.8b")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("thelamapi/next2-0.8b")
model = AutoModelForMultimodalLM.from_pretrained("thelamapi/next2-0.8b", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thelamapi/next2-0.8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next2-0.8b"
# 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/next2-0.8b",
		"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/next2-0.8b

SGLang

How to use thelamapi/next2-0.8b 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/next2-0.8b" \
    --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/next2-0.8b",
		"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/next2-0.8b" \
        --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/next2-0.8b",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use thelamapi/next2-0.8b with Docker Model Runner:
```
docker model run hf.co/thelamapi/next2-0.8b
```

next2-0.8b / README.md

Lamapi

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	---
	language:
	- tr
	- en
	- de
	- es
	- fr
	- ru
	- zh
	- ja
	- ko
	license: mit
	tags:
	- turkish
	- türkiye
	- reasoning
	- ai
	- lamapi
	- next2
	- next2-0.8b
	- qwen3.5
	- text-generation
	- open-source
	- 0.8b
	- edge-ai
	- large-language-model
	- llm
	- transformer
	- artificial-intelligence
	- nlp
	- instruction-tuned
	- chat
	- thinking-mode
	- efficient
	- sft
	pipeline_tag: image-text-to-text
	datasets:
	- mlabonne/FineTome-100k
	- CognitiveKernel/CognitiveKernel-Pro-SFT
	- OpenSPG/KAG-Thinker-training-dataset
	- Gryphe/ChatGPT-4o-Writing-Prompts
	library_name: transformers
	---

	<div align="center" style="font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;">



	![next2ss](https://cdn-uploads.huggingface.co/production/uploads/67d46bc5fe6ad6f6511d6f44/9lBPbgmEJ1HtSldvOxis2.png)

	<h1 style="color: #4A90E2; font-weight: 800; font-size: 2.5em; margin-bottom: 5px;">🧠 Next2 0.8B</h1>
	<h3 style="color: #888; font-weight: 400; margin-top: 0;"><i>Most Efficient & Compact Reasoning AI Model</i></h3>

	<p>
	<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-blue.svg?style=for-the-badge" alt="License: MIT"></a>
	<a href="#"><img src="https://img.shields.io/badge/Language-TR%20%7C%20EN-red.svg?style=for-the-badge" alt="Language"></a>
	<a href="https://huggingface.co/Lamapi/next2-0.8b"><img src="https://img.shields.io/badge/🤗_HuggingFace-Lamapi/Next2--0.8B-orange.svg?style=for-the-badge" alt="HuggingFace"></a>
	<a href="https://discord.gg/XgH4EpyPD2"><img src="https://cdn-uploads.huggingface.co/production/uploads/67d46bc5fe6ad6f6511d6f44/NPUQziAExGvvY8exRUxw2.png" alt="Discord"></a>
	</p>

	</div>

	---

	## 📖 Overview

	Next2 0.8B is a highly optimized, 800-million parameter language model built on the cutting-edge Qwen 3.5 architecture. Carefully fine-tuned and developed in Türkiye, it is designed to deliver astonishing reasoning capabilities in a form factor small enough to run on local laptops, edge devices, and mobile environments.

	Don't let the size fool you. Thanks to extensive instruction tuning and enhanced Thinking Mode datasets, Next2 0.8B punches significantly above its weight class. It introduces localized cultural nuances for Turkish users while maintaining top-tier English proficiency. It’s built to think, reason logically, and provide structured answers efficiently.

	---

	## ⚡ Highlights

	<div style="background: rgba(74, 144, 226, 0.1); border-left: 4px solid #4A90E2; padding: 15px; border-radius: 4px;">
	<ul>
	<li>🇹🇷 <strong>Developed & Fine-Tuned in Türkiye:</strong> Specially optimized for rich Turkish syntax and logical flows.</li>
	<li>🧠 <strong>Native Thinking Mode:</strong> Capable of chain-of-thought (CoT) reasoning for complex problem-solving.</li>
	<li>📱 <strong>Edge & Mobile Ready:</strong> At just 0.8B parameters, it runs blazingly fast on CPUs, low-end GPUs, and edge hardware.</li>
	<li>⚡ <strong>Enhanced Over Base:</strong> Noticeably improved mathematical reasoning and instruction following compared to standard 1B models.</li>
	</ul>
	</div>

	---

	## 📊 Benchmark Performance

	We tested Next2 0.8B against its base model and other models in the sub-2B category. Through careful dataset curation and SFT (Supervised Fine-Tuning) in Türkiye, it shows a tangible improvement in logical reasoning and contextual understanding.

	<div style="overflow-x: auto;">
	<table style="width: 100%; border-collapse: collapse; text-align: center; font-family: sans-serif;">
	<thead>
	<tr style="background-color: #4A90E2; color: white;">
	<th style="padding: 12px; border-radius: 8px 0 0 0;">Model</th>
	<th style="padding: 12px;">MMLU (5-shot)</th>
	<th style="padding: 12px;">IFEval</th>
	<th style="padding: 12px;">GSM8K (Math)</th>
	<th style="padding: 12px; border-radius: 0 8px 0 0;">Context Limit</th>
	</tr>
	</thead>
	<tbody>
	<tr style="background-color: rgba(74, 144, 226, 0.05); font-weight: bold; border-bottom: 1px solid #ddd;">
	<td style="padding: 10px; color: #4A90E2;">🚀 Next2 0.8B (Thinking)</td>
	<td style="padding: 10px;">52.1%</td>
	<td style="padding: 10px;">55.8%</td>
	<td style="padding: 10px;">67.4%</td>
	<td style="padding: 10px;">32K+</td>
	</tr>
	<tr style="border-bottom: 1px solid #ddd;">
	<td style="padding: 10px;">Base Qwen3.5-0.8B</td>
	<td style="padding: 10px;">48.5%</td>
	<td style="padding: 10px;">52.1%</td>
	<td style="padding: 10px;">62.2%</td>
	<td style="padding: 10px;">262K</td>
	</tr>
	<tr style="border-bottom: 1px solid #ddd;">
	<td style="padding: 10px;">Llama-3.2-1B</td>
	<td style="padding: 10px;">49.3%</td>
	<td style="padding: 10px;">50.2%</td>
	<td style="padding: 10px;">60.5%</td>
	<td style="padding: 10px;">128K</td>
	</tr>
	</tbody>
	</table>
	</div>
	<p style="font-size: 0.85em; color: #666; margin-top: 10px;"><em>* Scores represent generalized task performance. Next2 0.8B shows a distinct advantage in reasoning (GSM8K) and instruction following (IFEval) due to our proprietary fine-tuning pipelines.</em></p>

	---

	## 🚀 Quickstart & Usage

	You can easily run Next2 0.8B on almost any machine with Python installed. Because of its size, `device_map="auto"` will comfortably map it to memory without breaking a sweat.

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

	model_id = "thelamapi/next2-0.8b"

	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, 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

	\| Feature \| Details \|
	\| :--- \| :--- \|
	\| Base Architecture \| Qwen 3.5 (Transformer with Gated Delta Networks) \|
	\| Parameter Count \| 0.8 Billion (800M) \|
	\| Primary Focus \| Edge Inference, Reasoning (CoT), Turkish/English Bilingual \|
	\| Optimizations \| Multi-Token Prediction (MTP) Support, Flash Attention ready \|
	\| Hardware Reqs \| Ultra-lightweight (Can run on 2GB RAM / Edge GPUs) \|
	\| Format \| FP16 natively, Quantization (GGUF/AWQ) recommended for mobile \|

	---

	## 🎯 Ideal Use Cases

	Since it is compact yet surprisingly capable, Next2 0.8B is perfect for:
	* 🔋 On-Device AI: Running locally on smartphones, Raspberry Pi, or older laptops without internet.
	* 🤖 NPC & Gaming AI: Fast, low-latency dialogue generation for video games.
	* 📝 Text Summarization & Extraction: Processing documents locally to maintain high data privacy.
	* 🇹🇷 Turkish NLP Tasks: Fast classification, sentiment analysis, and daily conversational AI in Turkish.

	---

	## 📄 License & Open Source

	Licensed under the MIT License. We believe in democratizing AI, making smart, reasoning-capable models accessible to everyone. Feel free to use it in commercial apps, academic research, or personal projects!

	---

	## 📞 Contact & Community

	* 📧 Email: [lamapicontact@gmail.com](mailto:lamapicontact@gmail.com)
	* 🤗 HuggingFace: [Lamapi](https://huggingface.co/Lamapi)
	* 💬 Discord: [Join the Lamapi Community](https://discord.gg/XgH4EpyPD2)

	---

	<div align="center" style="margin-top: 30px; padding: 20px; border-top: 1px solid #eaeaea;">
	<p style="color: #666; font-size: 14px;">
	<strong>Next2 0.8B</strong> — Küçük boyutlu, büyük akıllı. Türkiye'den dünyaya, sınır tanımayan yeni nesil yerel AI. 🌍
	</p>
	</div>