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!pip install unsloth1from unsloth import FastLanguageModel
2
3model, tokenizer = FastLanguageModel.from_pretrained("Lamapi/next-70b")
4
5messages = [
6 {"role": "system", "content": "You are Next-X1, a helpful, smart, and precise AI assistant created by Lamapi."},
7 {"role" : "user", "content" : "Write a Python script to optimize a neural network using PyTorch."}
8]
9text = tokenizer.apply_chat_template(
10 messages,
11 tokenize = False,
12 add_generation_prompt = True
13)
14
15from transformers import TextStreamer
16_ = model.generate(
17 **tokenizer(text, return_tensors = "pt").to("cuda"),
18 max_new_tokens = 2048,
19 temperature = 0.7, top_p = 0.95, top_k = 400,
20 streamer = TextStreamer(tokenizer, skip_prompt = True),
21)| Feature | Description |
|---|---|
| 📚 Massive Knowledge Base | Trained on a diverse, high-quality dataset covering science, history, and law. |
| 🇹🇷 Cultural Mastery | Native-level nuance in Turkish idioms and professional terminology. |
| ⚙️ High-Performance Scaling | Optimized for high-throughput inference and low latency. |
| 🧮 Scientific & Coding Excellence | 99.0% MATH score. Solves complex engineering and algorithmic problems. |
| 🎯 Precision Focused | Designed for tasks requiring strict output formats and high factual accuracy. |
| 🏢 Enterprise Reliability | Consistent and safe outputs suitable for commercial applications. |
| Specification | Details |
|---|---|
| Base Model | Llama |
| Parameters | 70 Billion |
| Architecture | Transformer (Causal LLM) |
| Modalities | Text-only |
| Fine-Tuning | SFT & DPO on high-quality instruct datasets |
| Optimizations | GQA, Flash Attention 3, Quantization-ready |
| Primary Focus | General Purpose Assistant, Math, Multilingual Chat |
Next 70B — Türkiye’s flagship AI model. Built for those who demand accuracy, speed, and scale.
