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