PEFT
Safetensors
English
cybersecurity
malware-analysis
att&ck
threat-intelligence
mixtral
lora
expert-adapters
cape-sandbox
digital-forensics
Instructions to use umer07/fathom-mixtral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use umer07/fathom-mixtral with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "umer07/fathom-mixtral") - Notebooks
- Google Colab
- Kaggle
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## Model Overview
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- **Base:** Mixtral-8x7B-Instruct-v0.1 (full bf16, no quantization)
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- **Training:** Direct PEFT+TRL
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- **Adapters:** 1 unified + 9 expert LoRA adapters (all rank=32, α=16)
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- **Hardware:** AMD MI300X (205.8 GB VRAM) — full bf16 training
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- **Key Innovation:** Evidence extraction layer + structured behavioral prompts → **9× improvement** in real ATT&CK mapping
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## Model Overview
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- **Base:** Mixtral-8x7B-Instruct-v0.1 (full bf16, no quantization)
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+
- **Training:** Direct PEFT+TRL
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| 37 |
- **Adapters:** 1 unified + 9 expert LoRA adapters (all rank=32, α=16)
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| 38 |
- **Hardware:** AMD MI300X (205.8 GB VRAM) — full bf16 training
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| 39 |
- **Key Innovation:** Evidence extraction layer + structured behavioral prompts → **9× improvement** in real ATT&CK mapping
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