Qwen2.5 Coder 1.5B Instruct Reasoning

🚀 Qwen2.5-Coder-1.5B-Instruct-Reasoning

The Qwen2.5-Coder-1.5B-Instruct-Reasoning model is supervised fine-tuned (SFT) on the nvidia/OpenCodeReasoning dataset to enhance its reasoning capabilities.

🚀 Quick Start

Hugging Face

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_name = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
adapter_repo = "bunyaminergen/Qwen2.5-Coder-1.5B-Instruct-Reasoning"

tokenizer = AutoTokenizer.from_pretrained(adapter_repo, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",
    torch_dtype="auto",
)

model.resize_token_embeddings(len(tokenizer))

model = PeftModel.from_pretrained(model, adapter_repo)
model.eval()

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Please provide a Python script that checks whether a number is prime."}
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=2048
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

✨ Features

The model has been fine - tuned to enhance its reasoning capabilities, as demonstrated by the comparison section.

💻 Usage Examples

Basic Usage

# The usage example on Hugging Face
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_name = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
adapter_repo = "bunyaminergen/Qwen2.5-Coder-1.5B-Instruct-Reasoning"

tokenizer = AutoTokenizer.from_pretrained(adapter_repo, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",
    torch_dtype="auto",
)

model.resize_token_embeddings(len(tokenizer))

model = PeftModel.from_pretrained(model, adapter_repo)
model.eval()

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Please provide a Python script that checks whether a number is prime."}
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=2048
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Advanced Usage

The advanced usage is shown in the comparison section, where the model demonstrates its reasoning process before generating the code.

📚 Documentation

Comparison

• Please pay attention to the <think> </think> special tokens in the Reasoning Model. As you can see, the Reasoning Model can now “think”.

Base Model

System: You are a helpful coding assistant. User: Please provide a Python script that checks whether a number is prime. Assistant:

def check_prime(num):
    # Check if the number is less than 2
    if num < 2:
        return False
    # Check for factors from 2 to the square root of the number
    for i in range(2, int(num**0.5) + 1):
        if num % i == 0:
            return False
    # If no factors were found, the number is prime
    return True

# Example usage
number = 7
if check_prime(number):
    print(f"{number} is a prime number.")
else:
    print(f"{number} is not a prime number.")

In this Python script, the function check_prime takes an integer as input and returns True if the number is prime, otherwise it returns False. The function first checks if the number is less than 2, which means it cannot be prime. Then, it iterates from 2 up to the square root of the number (inclusive). If any factor of the number is found within this range, the function immediately returns False, indicating that the number is not prime. If no factors are found after checking all possible divisors, the function concludes that the number is prime and returns True. The example usage demonstrates how to call the check_prime function with the number 7. The output will indicate whether 7 is a prime number or not.

Reasoning Model

System: You are a helpful coding assistant. User: Please provide a Python script that checks whether a number is prime. Assistant: <think> Okay, I need to write a Python program that checks if a given number is prime. Hmm, what's a prime number? Oh right, a prime number has exactly two distinct positive divisors: 1 and itself. So for a number to be prime, it must not have any divisors other than those two. First, let's think about the steps involved. The first thing is to determine if there are any divisors other than 1 and the number itself. How do I check that? Well, for a number n, starting from 2 up to sqrt(n), check if any of them divide n. If any do, then it's not prime. But wait, maybe even before checking up to the square root, some numbers might have factors beyond that. For example, take 9. The square root is 3. So checking 2,3,4 would catch 9's factors. Wait, but 3 divides 9, so the loop from 2 to sqrt( n) (which is 3 in this case) will check 2 and 3. So yes, checking up to the square root is sufficient because any composite number has at least one factor less than or equal to its square root. So the plan is:

1. Read an integer from input.
2. Check if the number is less than 2. If so, it's not prime.
3. Otherwise, check from 2 up to the square root of the number. For each i, if the number mod i is zero, return False.
4. If none of the numbers in this range divide the number, return True. Wait, but what about 0 and 1? Well, according to the problem statement, the input could be 0 or 1, which are not primes. So we should handle those cases separately. Let me outline the code structure. Read the input number as an integer. Let's call it num. Handle edge cases first:

• If num < 2: print "No" (since primes are >=2) Else: Compute sqrt_num = int(math.sqrt(num)) Then loop from 2 to sqrt_num inclusive. For each i, check if num % i ==0. If any such i exists, return False. Else, after checking all, return True. But how to compute the square root accurately in Python? Using math.isqrt for integers, which returns the floor of the square root. That's better than using ** operator since it avoids floating point inaccuracies. Wait, but what if the number is a perfect square? Like 9. Then math.isqrt(9) is 3. So in that case, the loop would check up to 3. But when i reaches 3, 9%3 is 0, so it would return False, which is correct. So the code would look something like this: import math n = int(input()) if n < 2: print("No") else: sqrt_n = math.isqrt(n) is_prime = True for i in range(2, sqrt_n +1): if n % i == 0: is_prime = False break print("Yes" if is_prime else "No") Wait, but wait, what about numbers like 25. The square root is 5. So the loop runs from 2 to 5. When i=5, 25%5 is 0, so returns false. Correct. This logic seems solid. Let me test some examples. Test case 1: input 7. The loop runs up to 2. 2 to 2. Check 2. 7%2 is 1, so returns true. Output Yes. Test case 2: input 6. sqrt(6) is ~2.449. So sqrt_n is 2. Loop from 2 to 2. Check 2. 6%2 is 0. So returns false. Output No. Another test case: 8. sqrt(8) is ~2.828. So sqrt_n is 2. Check 2. 8%2 is 0. So output No. What about a prime number like 13. sqrt(13) is ~3.605. So loop up to 3. Check 2 and 3. 13%2 is 1, 13%3 is 1. So returns true. Correct. Edge case: 1. Output No. Another edge case: 2. Output Yes. What about a very large number, say 999983 (a prime). The code should check up to sqrt(999983) which is around 999.9. So the loop runs 2 to 999. It will check all possible factors. Since it's a prime, none will divide it. So returns Yes. So the code seems to handle all cases correctly. I think that's the solution. Now, writing the code accordingly. </think>

import math

n = int(input())
if n < 2:
    print("No")
else:
    sqrt_n = math.isqrt(n)
    is_prime = True
    for i in range(2, sqrt_n + 1):
        if n % i == 0:
            is_prime = False
            break
    print("Yes" if is_prime else "No")

Dataset

• nvidia/OpenCodeReasoning

Training

Base

PEFT/LoRA

Model

Dataset

Tokenizer

Speeds, Sizes, Times

Compute Infrastructure

Links

• Github
• Website
• Linkedin

Team

• Bunyamin Ergen

Contact

• Mail

Reference

• This model has been fine - tuned using Supervised Fine Tuning (SFT) method from the original model Qwen/Qwen2.5-Coder-1.5B-Instruct.

Citation

@software{       Qwen2.5-Coder-1.5B-Instruct-Reasoning,
  author       = {Bunyamin Ergen},
  title        = {{Qwen2.5-Coder-1.5B-Instruct-Reasoning}},
  year         = {2025},
  month        = {04},
}

📄 License

• LICENSE