Qwen2.5-Coder-7B-ResearchAudio-v2

Production-Ready Code Generation Model

Fine-tuned from Qwen2.5-Coder-7B-Instruct to produce complete, clean, production-ready code.

Why This Model?

Base Model	This Model
Verbose explanations	Concise, to-the-point
Often truncates mid-function	Complete implementations
Theory-heavy for system design	Working code
Needs post-processing	Copy-paste ready

Real-World Comparison

We tested both models on 10 coding problems. This model won 9/10.

Example: Rate Limiter

Prompt: "Design a rate limiter"

Base Model Output:

Designing a rate limiter is crucial for maintaining performance...

### 1. Define Requirements
- **Rate**: Maximum requests allowed per unit time
- **Time Window**: Duration over which rate limit applies
- **Concurrency**: How many clients can be processed...

❌ Theory only, no code

This Model Output:

import time

class RateLimiter:
    def __init__(self, max_requests, period):
        self.max_requests = max_requests
        self.period = period
        self.requests = []

    def is_allowed(self):
        current_time = int(time.time())
        self.requests = [r for r in self.requests if current_time - r < self.period]
        if len(self.requests) >= self.max_requests:
            return False
        self.requests.append(current_time)
        return True

✅ Working implementation

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Example: Binary Search

Base Model: Long explanation, truncated code

Sure! Binary search is an efficient algorithm for finding an item 
from a sorted list of items. It works by repeatedly dividing...

def binary_search(arr, target):
    """
    Perform binary search on a sorted array to find the index...
    
    Parameters:
    arr (list): The sorted list of elements.
    target (int/float): The value to search for...

❌ Verbose, truncated

This Model:

def binary_search(arr, x):
    low = 0
    high = len(arr) - 1

    while low <= high:
        mid = (low + high) // 2
        if arr[mid] == x:
            return mid
        elif arr[mid] < x:
            low = mid + 1
        else:
            high = mid - 1
    return -1

✅ Clean, complete

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Example: Binary Tree Serialization

Base Model: Only showed TreeNode class definition, no actual serialization code

This Model:

class Node:
    def __init__(self, val):
        self.val = val
        self.left = None
        self.right = None

def serialize(root):
    if root is None:
        return '#'
    left = serialize(root.left)
    right = serialize(root.right)
    return str(root.val) + ',' + left + ',' + right

def deserialize(data):
    data_list = data.split(',')
    index = [0]

    def helper():
        if data_list[index[0]] == '#':
            index[0] += 1
            return None
        node = Node(int(data_list[index[0]]))
        index[0] += 1
        node.left = helper()
        node.right = helper()
        return node
    
    return helper()

✅ Complete serialize AND deserialize

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Test Results Summary

Problem	Base	v2	Winner
LRU Cache	Truncated	Complete	✅ v2
Binary Search	Verbose, truncated	Clean, complete	✅ v2
Rate Limiter	Theory only	Working code	✅ v2
Merge Sort	Truncated	More complete	✅ v2
Trie	Truncated at insert	Insert + search	✅ v2
Thread-safe Singleton	Complete	Complete	Tie
Dijkstra	Truncated	More complete	✅ v2
Retry Decorator	Verbose docstrings	Concise, working	✅ v2
Connection Pool	Truncated	Get + release	✅ v2
Binary Tree Serialize	TreeNode only	Full implementation	✅ v2

Score: 9/10 wins

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Training Details

Parameter	Value
Base Model	Qwen2.5-Coder-7B-Instruct
Dataset	glaive-code-assistant-v2
Samples	50,000
Epochs	2
Method	LoRA (r=16, alpha=32)
Batch Size	16
Learning Rate	2e-4
Hardware	NVIDIA H200
Training Time	~4 hours

---

Benchmark Comparison

General benchmarks show slight decrease (expected when specializing for code):

Benchmark	Base	v2	Delta
MMLU	64.6%	62.8%	-1.8%
HellaSwag	74.6%	72.8%	-1.8%
Winogrande	70.2%	67.5%	-2.8%
ARC-Challenge	48.5%	48.4%	-0.1%

Trade-off: Small general knowledge drop → Much better code output quality

For a code-focused model, this is the right trade-off.

---

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "researchaudio/qwen2.5-coder-7b-researchaudio-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "researchaudio/qwen2.5-coder-7b-researchaudio-v2",
    trust_remote_code=True
)

prompt = "Implement a thread-safe queue in Python"
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=500, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Best For

✅ Code generation APIs ✅ IDE extensions / autocomplete ✅ CI/CD automation ✅ System design implementations ✅ Prototyping ✅ Learning algorithms (clear, complete examples)

Not Recommended For

❌ General knowledge Q&A ❌ Long explanations / tutorials ❌ Non-code tasks

---

Version History

Version	Base	Dataset	Focus
v1	Qwen2.5-Coder-7B	500K mixed (Magicoder, Nemotron, etc.)	General code
v2	v1	50K Glaive	Production-ready output

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Citation

@misc{qwen2.5-coder-researchaudio-v2,
  author = {ResearchAudio},
  title = {Qwen2.5-Coder-7B-ResearchAudio-v2: Production-Ready Code Generation},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/researchaudio/qwen2.5-coder-7b-researchaudio-v2}
}

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License

Apache 2.0

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Built by ResearchAudio