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Lamapi
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Model Details
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Lamapi
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Supported Functionality
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:
Ideal for developers, students, and organizations needing fast, reliable, and low-resource text-generation.
| 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. |
| 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 |
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "Lamapi/next-270m"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id) # Chat messagemessages = [ {"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 Tokenizerprompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt") # Output from the modeloutput = model.generate(**inputs, max_new_tokens=50)print(tokenizer.decode(output[0], skip_special_tokens=True))MIT License — free to use, modify, and distribute. Attribution appreciated.
Next-270M — Lightweight, efficient, and reasoning-focused, bringing Turkey’s AI forward on low-resource hardware.
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "Lamapi/next-270m"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id) # Chat messagemessages = [ {"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 Tokenizerprompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt") # Output from the modeloutput = model.generate(**inputs, max_new_tokens=50)print(tokenizer.decode(output[0], skip_special_tokens=True))