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exploitintel
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Model Details
Model Provider
exploitintel
Model Tree
unsloth/gemma-4-12b-it
Input Modalities
Output Modalities
Supported Functionality
Held-out test split (exploitintel/cve-cwe-consensus, 10,514 examples), greedy decoding, description-only (no CVE-ID or label metadata in the prompt). Rows are split into easy (the weakness is named in the text) vs hard (it must be inferred).
| metric | this model (bf16) | v1 baseline* |
|---|---|---|
| exact-match | 0.714 | 0.29 |
| micro-F1 | 0.756 | 0.32 |
| macro-F1 | 0.538 | 0.067 |
| easy exact-match | 0.805 | — |
| hard exact-match | 0.644 | — |
* v1 baseline = a 1-epoch Gemma-4-E4B fine-tune. The headline gain is macro-F1 (the rare-CWE long tail), which improves ~8×; hard (must-infer) exact-match of 0.644 is close to easy (0.805), indicating the model genuinely infers weaknesses rather than only keyword-matching.
Requires transformers >= 5.10 (Gemma 4 is the gemma4_unified architecture).
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "exploitintel/cve-cwe-gemma4-12b"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto").eval()cve = ("A vulnerability in the login form allows remote attackers to execute ""arbitrary SQL commands via the username parameter.")messages = [{"role": "system", "content": "You are a vulnerability analyst. Given a CVE ""description, reply with only the CWE ID(s) it maps to, comma-separated."},{"role": "user", "content": cve},]prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=32, do_sample=False)print(tok.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))# -> CWE-89
unsloth/gemma-4-12b-it (4-bit QLoRA, bitsandbytes nf4).exploitintel/cve-cwe-consensus (train split, 50,074 examples).Apache-2.0, inherited from the Gemma 4 base model.