thelamapi/next-codex

💻 Next-Codex (L846MoE)

Code your future with our models.

 ![Architecture: MoE]()  

📖 Overview

Next-Codex is a high-performance, specialized Mixture-of-Experts (MoE) Large Language Model designed specifically for code generation, debugging, and software engineering tasks.

Unlike traditional dense models, Next-Codex utilizes a sparse architecture with 30 Billion total parameters, but only activates 3 Billion parameters per token. This unique design allows it to deliver the deep reasoning capabilities of a massive model while maintaining the ultra-low latency and inference cost of a lightweight 3B model. It is fine-tuned on a massive corpus of code across 20+ programming languages, making it the most efficient coding assistant in its class.

⚡ Highlights

• —🇹🇷 Türkiye’s First Specialized MoE Coding Model: Designed for speed and precision.
• —🚀 Hyper-Efficient Inference: Runs with 3B active parameters, enabling deployment on consumer GPUs (e.g., RTX 3090/4090).
• —💻 SOTA Coding Performance: Surpasses Claude Sonnet 4 and rivals o3-High in Python & JavaScript benchmarks.
• —🌍 Polyglot Programming: Master-level proficiency in Python, JS/TS, Rust, Go, C++, SQL, and Swift.
• —🧠 Context-Aware Debugging: Excellent at understanding large codebases and suggesting architectural improvements.
• —🏢 Production Ready: Optimized for autocomplete, unit test generation, and docstring creation.

📊 Benchmark Performance (Coding & Logic)

Next-Codex achieves state-of-the-art results among open-weights coding models, balancing extreme efficiency with high accuracy.

Benchmarks are being conducted... ---

🚀 Installation & Usage

Note: Due to the MoE architecture, this model is memory efficient. You can run it comfortably on 24GB VRAM GPUs (4-bit quantization highly recommended for lower VRAM).

!pip install unsloth transformers

from unsloth import FastLanguageModel

# Load the MoE Model
model, tokenizer = FastLanguageModel.from_pretrained(
    "Lamapi/next-codex",
    load_in_4bit = True, # Optimized for 24GB VRAM
)

messages = [
    {"role": "system", "content": "You are Next-Codex, an expert software engineer and AI coding assistant."},
    {"role" : "user", "content" : "Write a highly optimized Rust function to calculate the Fibonacci sequence using memoization."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 2048,
    temperature = 0.2, # Lower temperature for code precision
    top_p = 0.95,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

🧩 Key Features

📐 Model Specifications

🎯 Ideal Use Cases

• —IDE Autocomplete Plugins — Low latency makes it perfect for "Copilot" style completions.
• —Legacy Code Refactoring — Converting outdated code to modern standards (e.g., Java 8 to Java 21).
• —SQL Generation — Text-to-SQL for complex data analytics.
• —Turkish/English Development — Teams working in bilingual environments.
• —Algorithm Optimization — Reducing time complexity of existing functions.

📄 License

Licensed under the MIT License — free for commercial and non-commercial use.

📞 Contact & Support

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

Next-Codex — Smart as a giant, fast as a lightweight. The future of coding is MoE.