Views
No views yet
| 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 |
| 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 |
| 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 |
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4model_id = "Lamapi/next-270m"
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(model_id)
7
8# Chat message
9messages = [
10 {"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."},
11 {"role": "user", "content": "Hello, how are you?"}
12]
13
14# Prepare input with Tokenizer
15prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
16inputs = tokenizer(prompt, return_tensors="pt")
17
18# Output from the model
19output = model.generate(**inputs, max_new_tokens=50)
20print(tokenizer.decode(output[0], skip_special_tokens=True))Next-270M — Lightweight, efficient, and reasoning-focused, bringing Turkey’s AI forward on low-resource hardware.
