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

Libraries

How to use thelamapi/next2-0.8b-GGUF 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-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/next2-0.8b-GGUF", dtype="auto")

llama-cpp-python

How to use thelamapi/next2-0.8b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thelamapi/next2-0.8b-GGUF",
	filename="next2-0.8b.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/next2-0.8b-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/next2-0.8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next2-0.8b-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/next2-0.8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next2-0.8b-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/next2-0.8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf thelamapi/next2-0.8b-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/next2-0.8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thelamapi/next2-0.8b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/thelamapi/next2-0.8b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use thelamapi/next2-0.8b-GGUF 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-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/next2-0.8b-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/next2-0.8b-GGUF:Q4_K_M

SGLang

How to use thelamapi/next2-0.8b-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/next2-0.8b-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/next2-0.8b-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/next2-0.8b-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/next2-0.8b-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/next2-0.8b-GGUF with Ollama:
```
ollama run hf.co/thelamapi/next2-0.8b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use thelamapi/next2-0.8b-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/next2-0.8b-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/next2-0.8b-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/next2-0.8b-GGUF to start chatting

Pi new

How to use thelamapi/next2-0.8b-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/next2-0.8b-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/next2-0.8b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use thelamapi/next2-0.8b-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/next2-0.8b-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/next2-0.8b-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use thelamapi/next2-0.8b-GGUF with Docker Model Runner:
```
docker model run hf.co/thelamapi/next2-0.8b-GGUF:Q4_K_M
```

Lemonade

How to use thelamapi/next2-0.8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thelamapi/next2-0.8b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.next2-0.8b-GGUF-Q4_K_M

List all available models

lemonade list

🧠 Next2 0.8B

Most Efficient & Compact Reasoning AI Model

📖 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

🇹🇷 Developed & Fine-Tuned in Türkiye: Specially optimized for rich Turkish syntax and logical flows.
🧠 Native Thinking Mode: Capable of chain-of-thought (CoT) reasoning for complex problem-solving.
📱 Edge & Mobile Ready: At just 0.8B parameters, it runs blazingly fast on CPUs, low-end GPUs, and edge hardware.
⚡ Enhanced Over Base: Noticeably improved mathematical reasoning and instruction following compared to standard 1B models.

📊 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.

Model	MMLU (5-shot)	IFEval	GSM8K (Math)	Context Limit
🚀 Next2 0.8B (Thinking)	52.1%	55.8%	67.4%	32K+
Base Qwen3.5-0.8B	48.5%	52.1%	62.2%	262K
Llama-3.2-1B	49.3%	50.2%	60.5%	128K

* 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.

🚀 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.

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
🤗 HuggingFace: Lamapi
💬 Discord: Join the Lamapi Community

Next2 0.8B — Küçük boyutlu, büyük akıllı. Türkiye'den dünyaya, sınır tanımayan yeni nesil yerel AI. 🌍

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GGUF

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0.8B params

Architecture

qwen35

Hardware compatibility

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