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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# Load the MoE Model
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model, tokenizer = FastLanguageModel.from_pretrained(
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"Lamapi/next-codex
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load_in_4bit = True, # Optimized for 24GB VRAM
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messages = [
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{"role": "system", "content": "You are Next-
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{"role" : "user", "content" : "Write a highly optimized Rust function to calculate the Fibonacci sequence using memoization."}
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# Load the MoE Model
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model, tokenizer = FastLanguageModel.from_pretrained(
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"Lamapi/next-codex",
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load_in_4bit = True, # Optimized for 24GB VRAM
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messages = [
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{"role": "system", "content": "You are Next-CodeX, an expert software engineer and AI coding assistant."},
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{"role" : "user", "content" : "Write a highly optimized Rust function to calculate the Fibonacci sequence using memoization."}
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]
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