Facebook CWM
Code Worlds LLM
Run CWM Efficiently
Reasoning Built-In
Enable Thinking Tags
Inject <think> tags for step-by-step code reasoning in responses.
32B Parameters
Dense Decoder LLM
Deploy facebook cwm model for advanced code and chat tasks via API.
vLLM Ready
Serve Parallel
Use tensor-parallel-size for fast inference on facebook cwm api.
Examples
See what Facebook CWM can create
Copy any prompt below and try it yourself in the playground.
Recursion Haiku
“Write a haiku about recursion in programming. Use precise code concepts and poetic structure.”
SWE-bench Solve
“Solve this SWE-bench task: Fix bug in Python repo handling async file I/O. Provide diff and explanation.”
LiveCodeBench
“Generate Python solution for LiveCodeBench problem on dynamic programming with memoization. Include tests.”
MATH Proof
“Prove this AIME-level math theorem step-by-step using logical reasoning and LaTeX notation.”
For Developers
A few lines of code.
CWM inference. Two commands.
ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.
- Serverless: scales to zero, scales to millions
- Pay per token, no minimums
- Python and JavaScript SDKs, plus REST API
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())