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rider-provider-777
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Model Details
Model Provider
rider-provider-777
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
The synthetic generator was designed from Razorpay's public webhook documentation. The documented
behaviors used as source requirements are HMAC-SHA256 verification over the exact raw request body,
duplicate handling through the unique x-razorpay-event-id, at-least-once delivery, and tolerance
for out-of-order events. The generator created original Python modules and inserted controlled bugs;
it did not scrape Razorpay code or use private payloads.
Minor-unit typing, fulfil-once, delayed event commit, and unknown-event checks are additional benchmark invariants. They should not be read as a description of Razorpay's internal code.
Source repository: https://github.com/nabeel-wq/payments-qwen-4b-finetued
Candidates were compiled and executed against nine hidden behavioral checks. Base and tuned models used the same prompts, renderer, temperature zero, one sample per task, and a 2,200-token cap.
| Split | Qwen3.5-4B | PayFix LoRA |
|---|---|---|
| One-defect test, 120 programs | 48.3% | 100% |
| Two-defect challenge, 24 programs | 33.3% | 95.8% |
The test and challenge sets use implementation-family IDs excluded from training. They are still generated by the same program generator, so these numbers measure controlled domain adaptation and should not be read as production correctness. The one tuned challenge failure accepted an empty event ID while correctly repairing the other defect.
Full protocol, per-defect results, confidence intervals, and executable evaluator source are in the PayFix GitHub repository.
Qwen/Qwen3.5-4B1e-4, linear decayqwen3_5_disable_thinkingNo private code, credentials, customer payloads, or transaction records were used.
Use recent versions of Transformers and PEFT that support Qwen 3.5:
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase_id = "Qwen/Qwen3.5-4B"adapter_id = "rider-provider-777/payments-4b"tokenizer = AutoTokenizer.from_pretrained(base_id)base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")model = PeftModel.from_pretrained(base, adapter_id)
For vLLM, start the base model with the adapter attached:
bash
vllm serve Qwen/Qwen3.5-4B \--enable-lora \--lora-modules payfix=rider-provider-777/payments-4b
Use the code-only system prompt and full behavioral contract from the GitHub repository. The adapter was not trained as a general chat assistant.
Released under Apache-2.0. The base model is also published under Apache-2.0. Razorpay is referenced only as the source of public webhook requirements; this project is not affiliated with or endorsed by Razorpay.