Swapnanil09
/

native-log-translator-qlora

@@ -1,199 +1,120 @@
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 ---
+language: en
+license: mit
+base_model: microsoft/codebert-base
+tags:
+- log-translation
+- security-logs
+- cloud-logs
+- siem
+- qlora
+- peft
+- codebert
+datasets:
+- custom
+pipeline_tag: text-generation
 ---
+# 🔐 Native Log Translator
+> Maps heterogeneous cloud and OS logs to a unified normalized schema.
+Fine-tuned from `microsoft/codebert-base` using **LoRA (PEFT)** on a curated dataset
+of multi-provider security logs. Trained on Kaggle T4 x2 in FP16.
+---
+## 🚀 Quick Start
+```python
+import torch
+from transformers import RobertaTokenizer, RobertaForCausalLM, RobertaConfig
+from peft import PeftModel
+MODEL_REPO = "Swapnanil09/native-log-translator-qlora"
+BASE_MODEL = "microsoft/codebert-base"
+tokenizer = RobertaTokenizer.from_pretrained(MODEL_REPO)
+config = RobertaConfig.from_pretrained(BASE_MODEL)
+config.is_decoder = True
+base = RobertaForCausalLM.from_pretrained(
+    BASE_MODEL,
+    config=config,
+    ignore_mismatched_sizes=True,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(base, MODEL_REPO)
+model.eval()
+def translate_log(log_input):
+    prompt = f"<log>{log_input}</log>\n<schema>"
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        out = model.generate(**inputs, max_new_tokens=60, temperature=0.1,
+                             do_sample=True, pad_token_id=tokenizer.eos_token_id)
+    decoded = tokenizer.decode(out[0], skip_special_tokens=True)
+    return decoded.split("<schema>")[-1].strip()
+print(translate_log("AzureSignInLogs | ResultType=0"))
+# event_type: authentication_success
+# provider: azure
+# risk_level: low
+```
+---
+## 📋 Output Schema
+| Field | Description | Values |
+|---|---|---|
+| `event_type` | Normalized event category | e.g. `authentication_success`, `privilege_escalation` |
+| `provider` | Source cloud / OS | `azure`, `aws`, `gcp`, `windows`, `linux`, `paloalto`, `cisco`, `fortinet` |
+| `risk_level` | Severity classification | `low`, `medium`, `high`, `critical` |
+---
+## 📦 Supported Log Sources
+| Provider | Log Type |
+|---|---|
+| **Azure** | SignInLogs, Activity, NSGFlowLogs |
+| **AWS** | CloudTrail |
+| **GCP** | Audit Logs |
+| **Windows** | Security Events (4624, 4625, 4688, 4698, 4720, 4732, 1102 ...) |
+| **Linux** | Syslog (auth, kern) |
+| **Network** | Palo Alto, Cisco, Fortinet (CommonSecurityLog) |
+---
+## ⚙️ Training Details
+| Setting | Value |
+|---|---|
+| Base model | `microsoft/codebert-base` |
+| Method | LoRA (PEFT) |
+| LoRA rank | 16 |
+| LoRA alpha | 32 |
+| Target modules | query, key, value |
+| Epochs | 15 |
+| Batch size | 8 per device |
+| Gradient accumulation | 4 steps |
+| Learning rate | 2e-4 |
+| Precision | FP16 |
+| Hardware | Kaggle T4 x2 |
+---
+## 📌 Intended Use
+- SIEM normalization pipelines
+- Multi-cloud SOC log ingestion
+- Security event correlation
+- Threat detection preprocessing
+## ⚠️ Limitations
+- Trained on a small curated dataset — production use should involve fine-tuning on your own log corpus
+- May not generalize to vendor-specific log formats not seen during training
+- Not a replacement for rule-based parsers in high-stakes pipelines without validation