Upload SynthFix adapter and router

README.md

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+---
+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.

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "deepseek-ai/deepseek-coder-1.3b-base",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "o_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+oid sha256:8705d3ceefafe5e2bef9d4b85cd5ee26384f94a7beff1197fc5105ba2441b0ab
+size 12608648

router.pt

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+oid sha256:dd920287f29c6bfa2b773fcf45fdc29a6b8d484ac2b901932ad94f1396fe9c85
+size 20478

special_tokens_map.json

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+    "lstrip": false,
+    "normalized": true,
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+    "single_word": false
+  }
+}

tokenizer_config.json

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+}