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Models/EnCodec
Meta AIOpen Weights✓ Verified Spec & Code

EnCodec

Code LLM • Released 2024-01-20 • Last Verified 2025-02-01

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EnCodec is a specialized code intelligence model engineered by Meta AI. 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.

Context Window128k
LicenseLlama Community License
Deployment
Cloud API Local Run
API AvailableYes (REST/SDK)
💡

Plain English Summary (What is this model & who is it for?)

Think of EnCodec 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

⚡ Instant Code Autocomplete: Automatically write complete functions and API handlers as you type.
🐛 Bug Hunting & Repair: Paste error logs or broken code snippets to receive instant explanations and fixes.
🧪 Unit Test Generator: Automatically create unit tests for Python, JavaScript, or C++ codebases.
🔄 Language Conversion: Translate legacy Python scripts into modern TypeScript or Rust.

🚀 How to Run & Use This Model (Step-by-Step Guide)

Simple setup instructions for everyday users and developers.

1

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.

2

Load the Model

In LM Studio, search for "EnCodec". In Ollama, open your terminal and run "ollama run encodec".

3

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!

4

Developer API Integration

Developers can integrate EnCodec directly via Python (using Hugging Face transformers/diffusers) or connect via local OpenAI-compatible REST server (http://localhost:11434).

Benchmark Performance

MMLU67

Hardware Requirements for Local Running

8GB-24GB VRAM

Strengths

  • •High accuracy
  • •Fast inference

Limitations & Weaknesses

  • •Closed source API
Integration Code (coding)
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "encodec"
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

Free Open Weights

Prices subject to provider tiers and volume discounts. Check documentation for current token rates.

Model Tags

#meta#llama#open-weights#local

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