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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**Primary adapter:** `unified-v2` (general cybersecurity + malware analysis)
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**9 expert adapters** for domain-specific routing (static/dynamic analysis, network, forensics, threat intel, etc.)
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**Hugging Face Hub:** [`umer07/fathom-mixtral`](https://huggingface.co/umer07/fathom-mixtral)
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**Datasets:** [`umer07/fathom-expert-data`](https://huggingface.co/datasets/umer07/fathom-expert-data)
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**Fathom** turns raw sandbox reports (CAPE, Joe Sandbox, etc.) into high-quality ATT&CK-mapped malware analysis. It outperforms general-purpose models on cybersecurity tasks while remaining fully open-source and runnable on a single AMD MI300X / A100 80GB.
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**Primary adapter:** `unified-v2` (general cybersecurity + malware analysis)
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**9 expert adapters** for domain-specific routing (static/dynamic analysis, network, forensics, threat intel, etc.)
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**Fathom** turns raw sandbox reports (CAPE, Joe Sandbox, etc.) into high-quality ATT&CK-mapped malware analysis. It outperforms general-purpose models on cybersecurity tasks while remaining fully open-source and runnable on a single AMD MI300X / A100 80GB.
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