jhsu12
/

solidity-vuln-cls-access-control-v1

@@ -1,206 +1,86 @@
 ---
-base_model: Qwen/Qwen2.5-Coder-3B-Instruct
 library_name: peft
 tags:
-- base_model:adapter:Qwen/Qwen2.5-Coder-3B-Instruct
-- lora
-- transformers
 ---
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-### Framework versions
-- PEFT 0.18.1

 ---
 library_name: peft
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct
 tags:
+  - peft
+  - lora
+  - sequence-classification
+  - solidity
+  - smart-contract
+  - vulnerability-detection
+  - access-control
+pipeline_tag: text-classification
+license: apache-2.0
 ---
+# Solidity Vulnerability Classifier — Access Control
+Binary classifier that detects **Access Control** vulnerabilities in Solidity smart contracts.
 ## Model Details
+- **Base model**: [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct)
+- **Method**: QLoRA (4-bit NF4) + classification head
+- **Task**: Sequence Classification (2 labels: safe / vulnerable)
+- **LoRA rank**: 16, targeting q_proj, k_proj, v_proj, o_proj
+- **Classification head**: `modules_to_save=["score"]`
+## Available Checkpoints
+Load a specific checkpoint with `revision=`:
+```python
+model = PeftModel.from_pretrained(base, "jhsu12/solidity-vuln-cls-access-control-v1", revision="checkpoint-200")
+```
+| Tag | Step |
+|-----|------|
+| `checkpoint-28` | 28 |
+| `checkpoint-56` | 56 |
+| `checkpoint-84` | 84 |
+| `checkpoint-112` | 112 |
+| `checkpoint-140` | 140 ← `main` |
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+base_model = "Qwen/Qwen2.5-Coder-3B-Instruct"
+bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)
+model = AutoModelForSequenceClassification.from_pretrained(
+    base_model, num_labels=2, quantization_config=bnb_config,
+    device_map="auto", trust_remote_code=True, ignore_mismatched_sizes=True)
+model = PeftModel.from_pretrained(model, "jhsu12/solidity-vuln-cls-access-control-v1")
+model.eval()
+tokenizer = AutoTokenizer.from_pretrained("jhsu12/solidity-vuln-cls-access-control-v1", trust_remote_code=True)
+code = "pragma solidity ^0.8.0; contract Example { ... }"
+inputs = tokenizer(code, return_tensors="pt", truncation=True, max_length=1536).to(model.device)
+with torch.no_grad():
+    logits = model(**inputs).logits
+probs = torch.softmax(logits, dim=-1)
+print(f"Safe: {probs[0][0]:.2%}, Vulnerable: {probs[0][1]:.2%}")
+```
+Or use the inference script:
+```bash
+python inference_classifier.py --checkpoint jhsu12/solidity-vuln-cls-access-control-v1 --file contract.sol
+```
+## Part of
+This is one of 5 expert classifiers in the
+[Solidity Vulnerability Detector](https://huggingface.co/jhsu12/solidity-vulnerability-detector) system.
+| Expert | Hub Repo |
+|--------|----------|
+| Reentrancy | `jhsu12/solidity-vuln-cls-reentrancy-v1` |
+| Access Control | `jhsu12/solidity-vuln-cls-access-control-v1` |
+| Integer Overflow/Underflow | `jhsu12/solidity-vuln-cls-integer-overflow-underflow-v1` |
+| Timestamp Dependence | `jhsu12/solidity-vuln-cls-timestamp-dependence-v1` |
+| Unchecked Low-Level Calls | `jhsu12/solidity-vuln-cls-unchecked-low-level-calls-v1` |