metadata
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:
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