Marin 8B Instruct
Open-source instruction-following LLM
Transparent. Efficient. Production-ready.
Instruction-tuned
Question Answering and Code Generation
Handles factual queries, summarization, and multi-language code synthesis with proper syntax.
Efficient Architecture
8B Parameters, 128K Context
Llama-based transformer balances computational efficiency with strong performance across tasks.
Full Transparency
Open Training Data and Code
All experiments, datasets, and documentation publicly available for reproducibility and customization.
Examples
See what Marin 8B Instruct can create
Copy any prompt below and try it yourself in the playground.
API Documentation
“Write comprehensive API documentation for a REST endpoint that accepts JSON payloads and returns structured responses. Include request/response examples, error handling, and authentication details.”
Data Analysis
“Summarize quarterly sales trends from a dataset showing revenue by region, product category, and customer segment. Highlight key insights and growth opportunities.”
Content Creation
“Generate a technical blog post explaining how transformer architectures work, including attention mechanisms, embeddings, and practical applications in modern AI.”
Code Refactoring
“Refactor this Python function to improve readability and performance. Add type hints, docstrings, and optimize for O(n) time complexity.”
For Developers
A few lines of code.
Instruction-following. Three lines.
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())
Ready to create?
Start generating with Marin 8B Instruct on ModelsLab.