shenwen-coder-0.5b

Model Overview

shenwen-coder-0.5b is shenwenAI's first open-source code completion model. With 500 million parameters, it has been pre-trained on large-scale code corpora and fine-tuned specifically for code completion tasks. The model accurately predicts and completes code based on surrounding context, significantly boosting developer productivity. Thanks to its lightweight architecture, it runs smoothly on standard CPUs or consumer-grade GPUs, delivering local, low-latency intelligent code completion.

Parameters 0.5B (500 Million)

Model Type Code Completion

Architecture Transformer (Decoder-only)

License Apache 2.0

Organization shenwenAI

Languages

Python JavaScript C/C++ Java Go Rust

Completion Example

Python · Function Completion

# Input context (existing code)
def bubble_sort(arr):
    n = len(arr)
    for i in range(n):

# ↓ shenwen-coder-0.5b auto-completes
        for j in range(0, n - i - 1):
            if arr[j] > arr[j + 1]:
                arr[j], arr[j + 1] = arr[j + 1], arr[j]
    return arr

Core Capabilities

shenwen-coder-0.5b is deeply optimized for code completion scenarios

Intelligent Code Completion

Understands current code context to accurately predict the next line or multiple lines of code, supporting auto-completion of function bodies, class methods, logic blocks, and more.

Low-Latency Inference

The lightweight 0.5B parameter scale ensures ultra-low inference latency. Real-time responses are possible on a standard CPU — no high-end GPU required for a smooth completion experience.

Local Private Deployment

The model runs entirely on-device. Your code never leaves your machine, protecting both enterprise and personal code privacy without risk of data leakage.

Multi-Language Support

Supports Python, JavaScript, C/C++, Java, Go, Rust, and other mainstream programming languages to meet the completion needs of developers across different tech stacks.

Fill-in-the-Middle (FIM)

Supports FIM mode, which can accurately insert code in the middle position based on the code context before and after the cursor — ideal for mainstream IDE integrations.

Open Source & Free

Fully open-sourced under the Apache 2.0 license. Free for personal and commercial projects — developers are welcome to build on and customize it further.

Quick Start

Run shenwen-coder-0.5b locally in three simple steps

Install Dependencies

Install the transformers library via pip and make sure your Python environment is ready.

pip install transformers torch

Load the Model

Download and load the shenwen-coder-0.5b model and tokenizer from HuggingFace.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "shenwenAI/shenwen-coder-0.5b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Run Completion

Provide a code prefix and call the model generation API to get intelligent completion results.

prefix = "def fibonacci(n):\n    "
inputs = tokenizer(prefix, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0]))

Available on HuggingFace

shenwen-coder-0.5b is published on HuggingFace Model Hub. It can be loaded directly via the transformers library, or downloaded in GGUF format to run efficiently on-device with swllm.cpp.

Go to HuggingFace Learn about swllm.cpp

0.5B

Parameters