Happy Horse 1.0 is now on ModelsLab

Try Now
Skip to main content
Available now on ModelsLab · Language Model

Deepseek Coder 33B InstructCode Writes Itself

Master Code Generation

33B Parameters

Project-Level Completion

Handles 16K context for code infilling across 80+ languages with Deepseek Coder 33B Instruct model.

Instruction Tuned

Precise Code Reasoning

Follows developer prompts for generation, refactoring, and debugging via Deepseek Coder 33B Instruct API.

Fine-Tunable

Custom Data Training

Adapt Deepseek Coder 33B Instruct with LoRA on your datasets for specialized coding tasks.

Examples

See what Deepseek Coder 33B Instruct can create

Copy any prompt below and try it yourself in the playground.

REST API

Write a FastAPI endpoint that accepts JSON input for user registration, validates email with pydantic, hashes password with bcrypt, and stores in PostgreSQL with SQLAlchemy. Include error handling and response models.

Algorithm Optimize

Refactor this Python bubble sort to quicksort with O(n log n) complexity. Add type hints, docstrings, and unit tests using pytest. Original code: def bubble_sort(arr): ...

React Hook

Create a custom React hook useAsyncQuery that fetches data from a GraphQL endpoint, handles loading, error, and refetch states. Use TypeScript, Apollo Client, and include debounce for search inputs.

Docker Script

Generate a multi-stage Dockerfile for a Node.js app with Yarn, production build, Nginx serve, healthcheck, and non-root user. Optimize layers and include .dockerignore best practices.

For Developers

A few lines of code.
Code gen. One call.

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 requests
response = requests.post(
"https://modelslab.com/api/v7/llm/chat/completions",
json={
"key": "YOUR_API_KEY",
"prompt": "",
"model_id": ""
}
)
print(response.json())

FAQ

Common questions about Deepseek Coder 33B Instruct

Read the docs

33B parameter model trained on 2T tokens of 87% code, 13% natural language. Fine-tuned on 2B instruction tokens for coding tasks. Supports project-level completion in 80+ languages.

Access via LLM endpoint for code generation and reasoning. Provides 16K context length. No function calling or embeddings supported.

Handles Python, JavaScript, C/C++, Java, and 80+ others. Excels in generation, completion, refactoring.

Open-source SOTA performer on code benchmarks. Matches closed models in multi-language tasks. Fine-tunable for custom needs.

Yes, using LoRA on your data. Improves responses for domain-specific code. Deploy on-demand with dedicated GPUs.

Supports 16.4K tokens. Enables project-level infilling and completion. Trained with 16K window and fill-in-the-blank tasks.

Ready to create?

Start generating with Deepseek Coder 33B Instruct on ModelsLab.