Qwen 2.5 Coder 32B: Is This Best Open Weight Model Better than GPT-4o and Claude 3.5 Sonnet
Author(s): Barhoumi Mosbeh Originally published on Towards AI. On November 11, Alibaba announced its most advanced coding model to date: Qwen 2.5-Coder-32B-Instruct. But thats not all, its actually part of a whole family of coding models! In addition to the 32B model, there are versions with 0.5B, 1.5B, 3B, 7B, and 14B parameters. Before writing this article, I reviewed feedback from many of YouTubers, writers, and technical experts, and the consensus has been overwhelmingly positive. Today, well explore whether it really lives up to the hype.Ive been using ChatGPT, Gemini, and Claude for a while, and I can confidently say that Claude is the best at coding and understanding complex tasks. To be honest, Gemini is in a different league altogether. Fun fact: I once won a machine learning competition two years ago using Bard (now rebranded as Gemini) because everyone else was using ChatGPT. Bards performance gave me a competitive edge back then. Today, I believe Gemini might be the best vision model available, but overall, Im not a big fan.To evaluate the models performance in coding, we should cover the following three key aspects: Code Generation, Code Repair, and Code Reasoning. Additionally, we need to consider how many programming languages the model supports and its alignment with human preferences.OverviewBefore testing out Alibabas model, lets take a look at the performance results they have announced.Based on their internal benchmarks, it even outperforms GPT-4 and comes very close to Claude 3.5. However, I personally dont place too much weight on internal benchmarks alone, but the results seem very interesting according to those who have tried it.You can find more details about the performance here:Qwen2.5-Coder-32B-Instruct Qwen2Now it is the time, Nov. 11 10:24! The perfect time for our best coder model ever! Qwen2.5-Coder-32B-Instruct!qwen2.orgIm testing the model locally using Ollama:qwen2.5-coder:32bThe latest series of Code-Specific Qwen models, with significant improvements in code generation, code reasoning, andollama.comYou can also try out the model on the demo page of Hugging Face:Qwen2.5 Coder Demo a Hugging Face Space by QwenDiscover amazing ML apps made by the communityhuggingface.coCode GenerationMany people have already covered how well it performs in web and design:So, we will focus on testing it with some Python challenges. Lets start with a medium-level LeetCode problem:PromptThe problem description: Longest Substring Without Repeating CharactersAnswerclass Solution(object): def lengthOfLongestSubstring(self, s): """ :type s: str :rtype: int """ # Dictionary to store the last seen index of each character char_index_map = {} # Initialize the start of the current window and the maximum length start = 0 max_length = 0 for end in range(len(s)): # If the character is already in the map and its index is within the current window if s[end] in char_index_map and char_index_map[s[end]] >= start: # Move the start to the right of the last occurrence of the current character start = char_index_map[s[end]] + 1 # Update the last seen index of the current character char_index_map[s[end]] = end # Update the maximum length of the substring found so far max_length = max(max_length, end - start + 1) return max_lengthResultsImage from the authorNot the best, but it gets things done and passes all the tests. Can it solve a hard problem? (Note that the model could have been trained on these problems already, but its still impressive.)PromptProblem description: Regular Expression MatchingAnswerclass Solution(object): def isMatch(self, s, p): """ :type s: str :type p: str :rtype: bool """ m, n = len(s), len(p) # Create a 2D array to store the results of subproblems dp = [[False] * (n + 1) for _ in range(m + 1)] # Base case: empty string matches empty pattern dp[0][0] = True # Handle patterns like a*, a*b*, etc., which can match an empty string for j in range(1, n + 1): if p[j - 1] == '*': dp[0][j] = dp[0][j - 2] # Fill the dp table for i in range(1, m + 1): for j in range(1, n + 1): if p[j - 1] == '.' or p[j - 1] == s[i - 1]: dp[i][j] = dp[i - 1][j - 1] elif p[j - 1] == '*': dp[i][j] = dp[i][j - 2] # '*' matches zero occurrences if p[j - 2] == '.' or p[j - 2] == s[i - 1]: dp[i][j] = dp[i][j] or dp[i - 1][j] # '*' matches one or more occurrences # The result is in dp[m][n] return dp[m][n]ResultsImage from the authorAfter testing it on 1020 problems on LeetCode, I have the impression that it was trained on all problems, so its not the best way to evaluate it. However, its still worth sharing. Additionally, it always chooses to provide a Python solution, even when not specifically asked for one. But lets try something different. I asked the model to optimize the code to minimize resource consumption as much as possible.Image from the authorI didnt expect that, but when I asked the model to improve the performance, it even fails in the simplest cases.Lets now move to the next test, Code Repair. I will be providing the model with some code containing errors generated by Claude. I asked the model to generate some hard cases to fix, and lets see how it performs.Code RepairPromptThe following code contains several bugs. Please identify and fix them, and provide an explanation for each bug you find.class AsyncTaskQueue: def __init__(self): self.tasks = [] self.running = False self.lock = threading.Lock() self.workers = [] async def add_task(self, task): async with self.lock: self.tasks.append(task) if not self.running: self.start_processing() async def start_processing(self): self.running = True while self.tasks: task = self.tasks.pop(0) worker = asyncio.create_task(self.process_task(task)) self.workers.append(worker) if len(self.workers) >= 3: done, pending = await asyncio.wait(self.workers) self.workers = list(pending) self.running = False async def process_task(self, task): result = await task() return result AnswerCertainly! Let's go through the provided code and identify the issues, then correct them. Here are the main problems and their fixes:Issues IdentifiedThreading Lock Usage with Asyncio:The threading.Lock is not suitable for use with asyncio. Instead, you should use asyncio.Lock.Async Context Manager for Lock:The async with statement is used incorrectly with threading.Lock. It should be used with asyncio.Lock.Starting Processing:The start_processing method should be called in a non-blocking manner. Since start_processing is an async method, it should be awaited properly.Task Management:The start_processing method should handle task processing more efficiently. The current implementation might not handle task completion correctly.Worker Management:The workers list should be managed more carefully to ensure that tasks are completed before marking the queue as not running.Corrected CodeHere's the corrected version of the code:import asyncioimport threadingclass AsyncTaskQueue: def __init__(self): self.tasks = [] self.running = False self.lock = asyncio.Lock() self.workers = [] async def add_task(self, task): async with self.lock: self.tasks.append(task) if not self.running: self.running = True asyncio.create_task(self.start_processing()) async def start_processing(self): while True: async with self.lock: if not self.tasks: self.running = False break task = self.tasks.pop(0) worker = asyncio.create_task(self.process_task(task)) self.workers.append(worker) if len(self.workers) >= 3: done, pending = await asyncio.wait(self.workers, return_when=asyncio.FIRST_COMPLETED) self.workers = list(pending) async def process_task(self, task): result = await task() return result# Example usageasync def example_task(): await asyncio.sleep(1) print("Task completed") return "Result"async def main(): queue = AsyncTaskQueue() await queue.add_task(example_task) await queue.add_task(example_task) await queue.add_task(example_task) await queue.add_task(example_task) await queue.add_task(example_task) await queue.add_task(example_task)asyncio.run(main())Explanation of FixesAsyncio Lock:Replaced threading.Lock with asyncio.Lock to ensure compatibility with asynchronous operations.Async Context Manager for Lock:Used async with self.lock: to properly manage the lock in an asynchronous context.Starting Processing:Changed the logic to start processing in a non-blocking manner by using asyncio.create_task(self.start_processing()) inside add_task.Task Management:Modified start_processing to continuously check for tasks and process them until no tasks are left. It breaks out of the loop when there are no more tasks and sets self.running to False.Worker Management:Ensured that workers are managed correctly by using asyncio.wait with return_when=asyncio.FIRST_COMPLETED to handle task completion efficiently.Example UsageThe example usage demonstrates how to add tasks to the AsyncTaskQueue and run them asynchronously. The main function adds several tasks to the queue, and they are processed concurrently with a maximum of 3 workers at a time.This corrected and optimized code should perform better and be more reliable.Results and feedbackClaude evaluates the models response as 7.5/10. with this feedback:While the model provided a functionally correct solution, it delivered a tutorial-grade fix rather than a production-grade implementation. This indicates that for complex concurrent programming tasks, human expertise is still crucial for robust system design.After experimenting with the model, it gets the job done, but often requires follow-up prompts to improve the response. While it doesnt struggle with understanding the task at hand and can correct most critical bugs, it still lacks some of the strengths seen in models like ChatGPT and Claude, particularly in areas such as error handling, resource management, and optimization. I believe these may be the models key weaknesses.Specialized LLMs: A New Era in AIIts fascinating to witness the emergence of relatively small, domain-specific language models that can run locally. I believe were entering an exciting new era of specialized LLMs, and this is just the beginning. While the concept isnt new, were finally seeing models that truly excel in specific domains.The combination of RAG (Retrieval-Augmented Generation) and specialist LLMs could define the AI landscape in the coming months/years. Were likely to see more powerful coding-focused models from industry leaders like OpenAI and Anthropic. Programming is perhaps one of the most natural domains for specialized AI, and we might soon see even more focused models, imagine LLMs specifically optimized for DevOps or front-end development!Dont feel overwhelmed by these rapid advances. Yes, it can be somewhat daunting to see LLMs mastering skills that traditionally took years to develop. Coding, which has challenged humanity for decades, is being transformed before our eyes. But rather than seeing this as an endpoint, we should view it as an opportunity for growth and innovation.Whether the current wave of LLM advances slows down in the coming years or this is merely the beginning of a longer journey, our response should remain the same: stay curious, keep learning, and never stop innovating. The future of technology is being written right now, and we all have a part to play in shaping it.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AI
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