|
|
--- |
|
|
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 |
|
|
|
