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Model: chYassine/Base-AMAN
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+---
+
+# Base-AMAN (AMAN stand for Automated Monitoring and Anomaly Notifier it also mean safety in Arabic 🔒)
+
+This is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct for log undanrstanding and analysis and cybersecurity tasks.
+
+## Model Details
+
+- **Base Model**: Qwen/Qwen2.5-3B-Instruct
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **Task**: Causal Language Modeling for Log Analysis
+
+## Usage
+
+You can load and use this model directly like any other Hugging Face model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "chYassine/AMAN-merged",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "chYassine/AMAN-merged",
+    trust_remote_code=True
+)
+
+# Use the model
+prompt = "Analyze this log session:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=512, temperature=0.7)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Training Details
+
+This model was fine-tuned using LoRA adapters that have been merged into the base model.
+The adapter was trained on log analysis and cybersecurity datasets.
+
+## Limitations
+
+- This model is specialized for log analysis tasks
+- Performance may vary on general language tasks
+- Always review outputs for accuracy in security-critical applications