Instructions to use thelamapi/next-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next-270m with Transformers:

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

pipe = pipeline("text-generation", model="thelamapi/next-270m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thelamapi/next-270m")
model = AutoModelForCausalLM.from_pretrained("thelamapi/next-270m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use thelamapi/next-270m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thelamapi/next-270m",
	filename="next-270m-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use thelamapi/next-270m 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-270m:F16
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-270m:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next-270m:F16
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-270m:F16

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-270m:F16
# Run inference directly in the terminal:
./llama-cli -hf thelamapi/next-270m:F16

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-270m:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thelamapi/next-270m:F16

Use Docker

docker model run hf.co/thelamapi/next-270m:F16

LM Studio
Jan

vLLM

How to use thelamapi/next-270m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next-270m"
# 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-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thelamapi/next-270m:F16

SGLang

How to use thelamapi/next-270m 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-270m" \
    --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-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-270m" \
        --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-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use thelamapi/next-270m with Ollama:
```
ollama run hf.co/thelamapi/next-270m:F16
```

Unsloth Studio new

How to use thelamapi/next-270m 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-270m 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-270m 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-270m to start chatting

Docker Model Runner
How to use thelamapi/next-270m with Docker Model Runner:
```
docker model run hf.co/thelamapi/next-270m:F16
```

Lemonade

How to use thelamapi/next-270m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thelamapi/next-270m:F16

Run and chat with the model

lemonade run user.next-270m-F16

List all available models

lemonade list

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

🚀 Next-270M (xt330)

Lightweight, Efficient, and Türkiye-Focused AI

📖 Overview

Next-270M is a 270-million parameter causal language model based on Gemma 3, designed for efficiency, low-resource deployment, and reasoning-focused natural language understanding.

Key highlights:

Extremely lightweight — can run on consumer GPUs with low VRAM.
Optimized for text reasoning, summarization, and creative generation.
Supports Turkish natively while remaining multilingual.
Open-source and transparent for research and applications.

Ideal for developers, students, and organizations needing fast, reliable, and low-resource text-generation.

Our Next 1B and Next 4B models are leading to all of the tiny models in benchmarks.

Model	MMLU (5-shot) %	MMLU-Pro %	GSM8K %	MATH %
Next 4B preview Version s325	84.6	66.9	82.7	70.5
Next 1B Version t327	87.3	69.2	90.5	70.1
Qwen 3 0.6B	52.81	37.6	60.7	20.5
Llama 3.2 1B	49.3	44.4	11.9	30.6
Kumru 7B not verified	30.7	28.6	15.38	6.4

Also, our Next Z1 model is leading to state-of-the-art models in some of the Benchmarks.

Model	MMLU (5-shot) %	MMLU-Pro %	GSM8K %	MATH %
Next Z1 Version l294	97.3	94.2	97.7	93.2
Next Z1 Version l294 (no tool)	94.7	90.1	94.5	88.7
GPT 5	92.5	87.0	98.4	96.0
Claude Opus 4.1 (Thinking)	~92.0	87.8	84.7	95.4

🎯 Goals

Lightweight Efficiency: Run smoothly on low-resource devices.
Reasoning-Focused: Provide logical and coherent text outputs.
Accessibility: Fully open-source with clear documentation.
Multilingual Adaptability: Turkish-focused but supports other languages.

✨ Key Features

Feature	Description
🔋 Lightweight Architecture	Optimized for low VRAM usage; ideal for small GPUs or CPU deployment.
🇹🇷 Turkish & Multilingual	Handles complex Turkish prompts accurately.
🧠 Reasoning Capabilities	Logical chain-of-thought for question-answering and problem-solving.
📊 Consistent Outputs	Reliable and reproducible results across multiple runs.
🌍 Open Source	Transparent, research-friendly, and community-driven.

📐 Model Specifications

Specification	Details
Base Model	Gemma 3
Parameter Count	270 Million
Architecture	Transformer, causal LLM
Fine-Tuning Method	Instruction fine-tuning (SFT) with Turkish and multilingual datasets
Optimizations	Quantization-ready (q8, f16, f32)
Use Cases	Text generation, summarization, Q&A, creative writing, reasoning tasks

🚀 Installation & Usage

Use the model:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Lamapi/next-270m"
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?

📄 License

MIT License — free to use, modify, and distribute. Attribution appreciated.

📞 Contact & Support

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

Next-270M — Lightweight, efficient, and reasoning-focused, bringing Turkey’s AI forward on low-resource hardware.

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Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for thelamapi/next-270m

Finetunes

1 model

Quantizations

13 models

thelamapi
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next-270m