Codebert Java
Open Weights • Released 2022-09-26 • Last Verified 2026-08-06
Codebert Java is a specialized code intelligence model engineered by Neulab. Pre-trained on extensive repository-scale source code and fine-tuned for Fill-in-the-Middle (FIM) completion, automated refactoring, and multi-language software engineering across Python, TypeScript, Rust, C++, and SQL.
Plain English Summary (What is this model & who is it for?)
Think of Codebert Java as a smart coding partner inside your editor. It autocompletes lines of code, writes entire software functions, detects hidden bugs, and explains complex code logic in clear, plain language.
💡 Real-World Use Cases & Practical Examples
🚀 How to Run & Use This Model (Step-by-Step Guide)
Simple setup instructions for everyday users and developers.
Download a One-Click App (No Coding Required)
Download a free local AI launcher like LM Studio (lmstudio.ai) or Ollama (ollama.com) on your Mac, Windows, or Linux PC.
Load the Model
In LM Studio, search for "Codebert Java". In Ollama, open your terminal and run "ollama run neulab-codebert-java".
Start Chatting or Generating
Type your text instructions or upload files into the app. The AI runs 100% privately on your hardware without internet requirement!
Developer API Integration
Developers can integrate Codebert Java directly via Python (using Hugging Face transformers/diffusers) or connect via local OpenAI-compatible REST server (http://localhost:11434).
Benchmark Performance
Hardware Requirements for Local Running
Consumer GPU / CPU compatible
Strengths
- •Repository-Scale Context Completion (FIM)
- •Multi-Language Software Syntax Accuracy
- •Automated Test & Docstring Generation
- •Compatible with VS Code & JetBrains Extensions
Limitations & Weaknesses
- •Requires local GPU hardware for self-hosting
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "neulab/codebert-java"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
prompt = "def quicksort(arr):"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Pricing Overview
Prices subject to provider tiers and volume discounts. Check documentation for current token rates.
Model Tags
Did this model work for you?
Your feedback helps others find the right model.