Instructions to use thelamapi/next-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next-12b with Transformers:

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

pipe = pipeline("image-text-to-text", model="thelamapi/next-12b")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("thelamapi/next-12b")
model = AutoModelForImageTextToText.from_pretrained("thelamapi/next-12b")
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]:]))

llama-cpp-python

How to use thelamapi/next-12b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thelamapi/next-12b",
	filename="next-12b-mmproj.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"
					}
				}
			]
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use thelamapi/next-12b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next-12b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-12b:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next-12b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-12b:Q8_0

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/next-12b:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf thelamapi/next-12b:Q8_0

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/next-12b:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thelamapi/next-12b:Q8_0

Use Docker

docker model run hf.co/thelamapi/next-12b:Q8_0

LM Studio
Jan

vLLM

How to use thelamapi/next-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next-12b"
# 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/next-12b",
		"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/next-12b:Q8_0

SGLang

How to use thelamapi/next-12b 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/next-12b" \
    --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/next-12b",
		"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/next-12b" \
        --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/next-12b",
		"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/next-12b with Ollama:
```
ollama run hf.co/thelamapi/next-12b:Q8_0
```

Unsloth Studio new

How to use thelamapi/next-12b 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/next-12b 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/next-12b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thelamapi/next-12b to start chatting

Docker Model Runner
How to use thelamapi/next-12b with Docker Model Runner:
```
docker model run hf.co/thelamapi/next-12b:Q8_0
```

Lemonade

How to use thelamapi/next-12b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thelamapi/next-12b:Q8_0

Run and chat with the model

lemonade run user.next-12b-Q8_0

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🚀 Next 12B (m200)

Türkiye's Advanced Vision-Language Model — High Performance, Multimodal, and Enterprise-Ready

📖 Overview

Next 12B is a 12-billion parameter multimodal Vision-Language Model (VLM) based on Gemma 3, fine-tuned to deliver exceptional performance in both text and image understanding. This is Türkiye's most advanced open-source vision-language model, designed for:

Superior understanding and generation of text and image descriptions.
Advanced reasoning and context-aware multimodal outputs.
Professional-grade Turkish support with extensive multilingual capabilities.
Enterprise-ready deployment with optimized quantization options.

This model is ideal for enterprises, researchers, and organizations who need a state-of-the-art multimodal AI capable of complex visual understanding, advanced reasoning, and creative generation.

Next 12B sets new standards for medium-sized models across all major benchmarks.

Model	MMLU (5-shot) %	MMLU-Pro %	GSM8K %	MATH %
Next 14B (Thinking)	94.6	93.2	98.8	92.7
Next 12B	92.7	84.4	95.3	87.2
Next 8B (Thinking)	91.0	88.5	96.2	88.0
GPT-5	92.5	87.0	98.4	96.0
Claude Opus 4.1 (Thinking)	~92.0	87.8	84.7	95.4

---

🚀 Installation & Usage

Use with vision:

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

model_id = "Lamapi/next-12b"

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

# Read image
image = Image.open("image.jpg")

# Create a message in chat format
messages = [
  {"role": "system","content": [{"type": "text", "text": "You are Next-X1, a smart and concise AI assistant trained by Lamapi. Always respond in the user's language. Proudly made in Turkey."}]},

  {
      "role": "user","content": [{"type": "image", "image": image},
      {"type": "text", "text": "Who is in this image?"}
    ]
  }
]

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

# Output from the model
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Who is in this image?

The image shows Mustafa Kemal Atatürk, the founder and first President of the Republic of Turkey.

Use without vision:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Lamapi/next-12b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Chat message
messages = [
    {"role": "system", "content": "You are Next-X1, a smart and concise AI assistant trained by Lamapi. Always respond in the user's language. Proudly made in Turkey."},
    {"role": "user", "content": "Hello, how are you?"}
]

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

# Output from the model
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Hello, how are you?

I'm fine, thank you. How are you?

🎯 Goals

Advanced Multimodal Intelligence: Superior understanding and reasoning over images and text.
Enterprise-Grade Performance: High accuracy and reliability for production deployments.
Efficiency: Optimized for professional GPUs with flexible quantization options.
Accessibility: Open-source availability for research and commercial applications.
Cultural Excellence: Best-in-class Turkish language support while maintaining multilingual capabilities.

✨ Key Features

Feature	Description
🔋 Optimized Architecture	Balanced performance and efficiency; supports multiple quantization formats.
🖼️ Advanced Vision-Language	Deep understanding of images with sophisticated visual reasoning capabilities.
🇹🇷 Professional Turkish Support	Industry-leading Turkish language performance with extensive multilingual reach.
🧠 Superior Reasoning	State-of-the-art logical and analytical reasoning for complex tasks.
📊 Production-Ready	Reliable, consistent outputs suitable for enterprise applications.
🌍 Open Source	Transparent, community-driven, and commercially friendly.

📐 Model Specifications

Specification	Details
Base Model	Gemma 3
Parameter Count	12 Billion
Architecture	Transformer, causal LLM + Enhanced Vision Encoder
Fine-Tuning Method	Advanced instruction & multimodal fine-tuning (SFT) on curated Turkish and multilingual datasets
Optimizations	Q8_0, Q4_K_M, F16, F32 quantizations for flexible deployment options
Modalities	Text & Image
Use Cases	Advanced image captioning, multimodal QA, text generation, complex reasoning, creative storytelling, enterprise applications

💡 Performance Highlights

MMLU Excellence: 91.8% on MMLU benchmark, demonstrating comprehensive knowledge across diverse domains
Mathematical Prowess: 81.2% on MATH benchmark, excelling in complex mathematical reasoning
Problem Solving: 94.3% on GSM8K, showcasing superior word problem solving capabilities
Professional Reasoning: 78.4% on MMLU-Pro, handling advanced professional-level questions

🎨 Use Cases

Enterprise Content Generation: High-quality multilingual content creation
Advanced Visual Analysis: Detailed image understanding and description
Educational Applications: Complex tutoring and explanation systems
Research Assistance: Literature review and data analysis
Creative Writing: Story generation and creative content
Technical Documentation: Code documentation and technical writing
Customer Support: Multilingual customer service automation
Data Extraction: Visual document processing and information extraction

📄 License

This project is licensed under the MIT License — free to use, modify, and distribute for commercial and non-commercial purposes. Attribution is appreciated.

📞 Contact & Support

📧 Email: lamapicontact@gmail.com
🤗 HuggingFace: Lamapi

Next 12B — Türkiye's most advanced vision-language AI, combining state-of-the-art multimodal understanding, superior reasoning, and enterprise-grade reliability.

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for thelamapi/next-12b

Quantizations

5 models

thelamapi
/

next-12b