Text Generation
Transformers
Safetensors
qwen3_moe
turkish
türkiye
ai
lamapi
next-codex
coder
codex
open-source
30b
Mixture of Experts
mixture-of-experts
code-generation
coding
llm
transformer
artificial-intelligence
4-bit precision
bitsandbytes
Instructions to use thelamapi/next-codex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thelamapi/next-codex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thelamapi/next-codex")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("thelamapi/next-codex") model = AutoModelForCausalLM.from_pretrained("thelamapi/next-codex") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use thelamapi/next-codex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thelamapi/next-codex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thelamapi/next-codex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/thelamapi/next-codex
- SGLang
How to use thelamapi/next-codex with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "thelamapi/next-codex" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thelamapi/next-codex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "thelamapi/next-codex" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thelamapi/next-codex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use thelamapi/next-codex with Docker Model Runner:
docker model run hf.co/thelamapi/next-codex
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README.md
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- 🇹🇷 **Türkiye’s First Specialized MoE Coding Model:** Designed for speed and precision.
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- 🚀 **Hyper-Efficient Inference:** Runs with **3B active parameters**, enabling deployment on consumer GPUs (e.g., RTX 3090/4090).
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- 💻 **SOTA Coding Performance:** Surpasses
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- 🌍 **Polyglot Programming:** Master-level proficiency in Python, JS/TS, Rust, Go, C++, SQL, and Swift.
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- 🧠 **Context-Aware Debugging:** Excellent at understanding large codebases and suggesting architectural improvements.
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- 🏢 **Production Ready:** Optimized for autocomplete, unit test generation, and docstring creation.
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- 🇹🇷 **Türkiye’s First Specialized MoE Coding Model:** Designed for speed and precision.
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- 🚀 **Hyper-Efficient Inference:** Runs with **3B active parameters**, enabling deployment on consumer GPUs (e.g., RTX 3090/4090).
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- 💻 **SOTA Coding Performance:** Surpasses Claude Sonnet 4 and rivals o3-High in Python & JavaScript benchmarks.
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- 🌍 **Polyglot Programming:** Master-level proficiency in Python, JS/TS, Rust, Go, C++, SQL, and Swift.
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- 🧠 **Context-Aware Debugging:** Excellent at understanding large codebases and suggesting architectural improvements.
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- 🏢 **Production Ready:** Optimized for autocomplete, unit test generation, and docstring creation.
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