Instructions to use CoderDoge/synthfix-deepseek-coder-1.3b-sven with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CoderDoge/synthfix-deepseek-coder-1.3b-sven with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base") model = PeftModel.from_pretrained(base_model, "CoderDoge/synthfix-deepseek-coder-1.3b-sven") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| base_model: deepseek-ai/deepseek-coder-1.3b-base | |
| pipeline_tag: text-generation | |
| tags: | |
| - code | |
| - program-repair | |
| - vulnerability-repair | |
| - peft | |
| - lora | |
| - synthfix | |
| - router | |
| - sven | |
| # SynthFix deepseek-coder-1.3b-base (SVEN) | |
| This repository contains the SynthFix LoRA repair-agent adapter and matching router checkpoint for **security vulnerability repair** on **SVEN**. | |
| SynthFix is an adaptive neuro-symbolic code repair framework. During training, a lightweight router chooses between supervised fine-tuning (SFT) and reward fine-tuning (RFT), while the reward combines compiler-, analyzer-, and test-derived symbolic evidence. At inference time, the same evidence supports best-of-K candidate selection with a greedy floor. | |
| ## Contents | |
| - `adapter_model.safetensors`, `adapter_config.json`: PEFT/LoRA adapter for `deepseek-ai/deepseek-coder-1.3b-base`. | |
| - tokenizer files copied from the training checkpoint. | |
| - `router.pt`: PyTorch state dict for the SynthFix router associated with this adapter. | |
| ## Base Model | |
| This adapter is intended to be loaded with `deepseek-ai/deepseek-coder-1.3b-base` using `peft.PeftModel.from_pretrained`. | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| base_model = "deepseek-ai/deepseek-coder-1.3b-base" | |
| adapter_id = "CoderDoge/synthfix-deepseek-coder-1.3b-sven" | |
| tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True) | |
| base = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code=True) | |
| model = PeftModel.from_pretrained(base, adapter_id) | |
| ``` | |
| The router architecture is implemented in the SynthFix artifact repository: https://github.com/CoderDoge1108/SynthFix and https://github.com/largehappygroup/SynthFix. | |
| ## Paper | |
| SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair | |
| - arXiv: https://arxiv.org/abs/2604.17184 | |
| - GitHub artifact: https://github.com/CoderDoge1108/SynthFix | |
| ## Notes | |
| These are adapter weights rather than full copies of the base models. Users should follow the license and usage terms of the corresponding base model. | |