Latest Model

shenwen-coderV2-Instruct

The next-generation instruction-following code generation model by shenwenAI,
enabling conversational programming to help developers efficiently generate, explain, and debug code.

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Model Overview

shenwen-coderV2-Instruct is shenwenAI's next-generation open-source code generation model. Fine-tuned with instruction data, it understands natural language descriptions and generates high-quality code. It supports multi-turn conversational programming interactions. Whether generating complete functions from scratch, explaining existing code logic, or helping track down bugs, shenwen-coderV2-Instruct delivers accurate and efficient coding assistance — your intelligent programming companion.

Model Type Code Generation (Instruction-tuned)

Architecture Transformer (Decoder-only)

Training Instruction Fine-tuning (SFT)

License Apache 2.0

Organization shenwenAI

Languages

Python JavaScript C/C++ Java Go Rust

Code Generation Example

Python · Instruction-based Generation

# User instruction:
# Write a Python function for binary search

# ↓ shenwen-coderV2-Instruct generates
def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1

Core Capabilities

shenwen-coderV2-Instruct is deeply optimized for conversational code generation

Natural Language Programming

Describe your requirements in plain language and the model automatically generates the corresponding code — no need to memorize complex syntax.

Code Generation

Generates complete functions, classes, or modules from feature descriptions. Supports multiple programming languages with clean, ready-to-run output.

Code Explanation

Explains complex code line-by-line or at a high level, helping developers quickly understand unfamiliar codebases and improve maintainability.

Bug Fix Assistance

Analyzes error messages and stack traces to pinpoint root causes and suggest fixes — accelerating your debugging workflow.

Multi-Turn Dialogue

Supports context-aware multi-turn programming conversations. Iteratively refine requirements and update code within the same session for a seamless collaboration experience.

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-coderV2-Instruct 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-coderV2-Instruct model and tokenizer from HuggingFace.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "shenwenAI/shenwen-coderV2-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Send Instructions

Build a chat message and call the model generation API to get code generation results.

messages = [
    {"role": "user", "content": "Write a Python function for bubble sort"}
]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

Available on HuggingFace

shenwen-coderV2-Instruct 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

Version

Languages

Chat

Interaction Mode

Local

Deployment

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