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Lamapi / next-codex
Model's Last Updated: December 06 2025
text-generation
Introduction of next-codex
Model Details of next-codex
๐ป Next-Codex (L846MoE)
Code your future with our models.
๐ 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.
โก Highlights
- ๐น๐ท Tรผrkiyeโs First Specialized MoE Coding Model: Designed for speed and precision.
- ๐ Hyper-Efficient Inference: Runs with 3B active parameters , enabling deployment on consumer GPUs (e.g., RTX 3090/4090).
- ๐ป SOTA Coding Performance: Surpasses Claude Sonnet 4 and rivals o3-High in Python & JavaScript benchmarks.
- ๐ Polyglot Programming: Master-level proficiency in Python, JS/TS, Rust, Go, C++, SQL, and Swift.
- ๐ง Context-Aware Debugging: Excellent at understanding large codebases and suggesting architectural improvements.
- ๐ข Production Ready: Optimized for autocomplete, unit test generation, and docstring creation.
๐ 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...
๐ 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),
)
๐งฉ Key Features
| Feature | Description |
|---|---|
| ๐ Smart Routing (MoE) | Dynamically routes tokens to the best "expert" layers, activating only 3B params for speed. |
| ๐ ๏ธ Full-Stack Mastery | Trained on frontend (React, Vue), backend (Django, Spring), and systems (C, Rust) code. |
| ๐น๐ท Code Support | Exceptional ability to understand Turkish variable names and comments in legacy codebases. |
| ๐ Deep Debugging | Analyzes stack traces and logic errors to provide instant fixes. |
| ๐ Docstring & Testing | Automatically generates Javadoc, PyDoc, and Unit Tests (Pytest/Jest). |
| ๐ Secure Coding | Aligned to avoid common vulnerabilities (SQLi, XSS) in generated code. |
๐ Model Specifications
| Specification | Details |
|---|---|
| Architecture | Mixture of Experts (MoE) Transformer |
| Total Parameters | 30 Billion |
| Active Parameters | 3 Billion (per token) |
| Context Window | 32k Tokens |
| Experts | 8 Experts (Top-2 Routing) |
| Training Data | 1T+ Tokens of Code (The Stack v2, GitHub, Synthetic) |
| Quantization | GGUF, AWQ, GPTQ supported |
๐ฏ Ideal Use Cases
- IDE Autocomplete Plugins โ Low latency makes it perfect for "Copilot" style completions.
- Legacy Code Refactoring โ Converting outdated code to modern standards (e.g., Java 8 to Java 21).
- SQL Generation โ Text-to-SQL for complex data analytics.
- Turkish/English Development โ Teams working in bilingual environments.
- Algorithm Optimization โ Reducing time complexity of existing functions.
๐ License
Licensed under the MIT License โ free for commercial and non-commercial use.
๐ Contact & Support
- ๐ง Email: [email protected]
- ๐ค HuggingFace: Lamapi
Next-Codex โ Smart as a giant, fast as a lightweight. The future of coding is MoE.
Runs of Lamapi next-codex on huggingface.co
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