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Models/Qwen-2.5-Code-OpenChat-3.5
Alibaba / CommunityOpen Weights

Qwen-2.5-Code-OpenChat-3.5

Code LLM โ€ข Released 2024-11-8 โ€ข Last Verified 2025-02-01

Try PlaygroundDocs

Qwen-2.5-Code-OpenChat-3.5 - Fine-tuned and quantized Code domain model variant optimized for fast local host inference.

Context Window32k-128k
LicenseApache-2.0 / Community Open Source
Deployment
Local Model
API AvailableNo
๐Ÿ’ก

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

Think of Qwen-2.5-Code-OpenChat-3.5 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.

๐Ÿ’ก Real-World Use Cases & Practical Examples

โšก Instant Code Autocomplete: Automatically write complete functions and API handlers as you type.
๐Ÿ› Bug Hunting & Repair: Paste error logs or broken code snippets to receive instant explanations and fixes.
๐Ÿงช Unit Test Generator: Automatically create unit tests for Python, JavaScript, or C++ codebases.
๐Ÿ”„ Language Conversion: Translate legacy Python scripts into modern TypeScript or Rust.

๐Ÿš€ 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 "Qwen-2.5-Code-OpenChat-3.5". In Ollama, open your terminal and run "ollama run qwen-2-5-code-openchat-3-5".

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 Qwen-2.5-Code-OpenChat-3.5 directly via Python (using Hugging Face transformers/diffusers) or connect via local OpenAI-compatible REST server (http://localhost:11434).

Benchmark Performance

Code Domain Score91

Hardware Requirements for Local Running

Requires 24GB VRAM

Strengths

  • โ€ขHigh accuracy
  • โ€ขFast inference

Limitations & Weaknesses

  • โ€ขClosed source API
Integration Code (coding)
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "qwen-2-5-code-openchat-3-5"
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 Source (Hugging Face / Ollama)

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

Model Tags

#code#ollama#huggingface#quantized#local

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