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Model Details
Model Provider
shohuu
Model Tree
Qwen/Qwen2.5-Coder-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Pyroton-PrimeFix-v3 is a patched adapter built on top of shohuu/pyroton. It applies targeted repair finetuning to fix specific correctness bugs that appeared in the base Pyroton adapter.
For the full project details, training strategy, and documentation, see the main repo: 👉 shohuu/pyroton 👉 github.com/TunasTuna/pyroton
The base Pyroton adapter had recurring correctness bugs in generated Python code:
| Bug | Description |
|---|---|
Missing import math | Model used math.sqrt() without importing math |
| Wrong edge case handling | Incorrect results for negative numbers, 0, and 1 |
Unsafe n**0.5 | Caused crashes for negative inputs |
math. without the required importTested on is_prime() with inputs: -1, 0, 1, 2, 3, 4, 6, 9, 17, 49
| Mode | Result |
|---|---|
Greedy decoding (do_sample=False) | ✅ 5/5 passing |
Sampled decoding (temperature=0.3) | ⚠️ Improved but less stable |
Use greedy decoding for best results.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerimport torch base = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2.5-Coder-0.5B-Instruct", dtype=torch.bfloat16, device_map="auto",) model = PeftModel.from_pretrained(base, "shohuu/pyroton-primefix-v3")tokenizer = AutoTokenizer.from_pretrained("shohuu/pyroton-primefix-v3")tokenizer.pad_token = tokenizer.eos_token prompt = "### Instruction:\nWrite a Python function to check if a number is prime\n\n### Response:\n"inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=220, do_sample=False, repetition_penalty=1.1,) print(tokenizer.decode(outputs[0], skip_special_tokens=True))Prompt:
text
Write a Python function to check if a number is primeOutput:
python
import math def is_prime(n): """Check if the given integer n is prime.""" if n <= 1: return False for i in range(2, int(math.sqrt(n)) + 1): if n % i == 0: return False return Truepython
outputs = model.generate( **inputs, max_new_tokens=220, do_sample=False, repetition_penalty=1.1,)python
outputs = model.generate( **inputs, max_new_tokens=220, do_sample=True, temperature=0.1, top_p=0.9, repetition_penalty=1.2,)Apache 2.0 — see LICENSE for details.
Base model (Qwen2.5-Coder) is also Apache 2.0. Attribution to Alibaba Cloud / Qwen Team.
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerimport torch base = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2.5-Coder-0.5B-Instruct", dtype=torch.bfloat16, device_map="auto",) model = PeftModel.from_pretrained(base, "shohuu/pyroton-primefix-v3")tokenizer = AutoTokenizer.from_pretrained("shohuu/pyroton-primefix-v3")tokenizer.pad_token = tokenizer.eos_token prompt = "### Instruction:\nWrite a Python function to check if a number is prime\n\n### Response:\n"inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=220, do_sample=False, repetition_penalty=1.1,) print(tokenizer.decode(outputs[0], skip_special_tokens=True))import math def is_prime(n): """Check if the given integer n is prime.""" if n <= 1: return False for i in range(2, int(math.sqrt(n)) + 1): if n % i == 0: return False return Trueoutputs = model.generate( **inputs, max_new_tokens=220, do_sample=False, repetition_penalty=1.1,)outputs = model.generate( **inputs, max_new_tokens=220, do_sample=True, temperature=0.1, top_p=0.9, repetition_penalty=1.2,)