The model was trained on pre-processed HDFS logs; unknown behavior may occur with logs from different systems.
The model doesn’t explain why an anomaly happens — only predicts classification.

Recommendations

Developed by: Abdoulaye MBAYE (FatLab, Fathala IT, ZigZeug)
Finetuned from model: meta-llama/LLaMA-3.2B
Language(s): English (Log data text)
License: llama3 original license (non-commercial research only)
Model type: Causal Language Model (Instruction-tuned for classification)

Always keep human supervision when deploying anomaly detection models in production.

How to Get Started with the Model

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "ZigZeug/LogLaye-LLaMA3.2B-QLoRA-finetuned-HDFS-logs"

model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = [
  {"role": "system", "content": "You are an expert in HDFS log analysis. Classify if the following log is normal or anomalous."},
  {"role": "user", "content": "Log: PacketResponder 2 for block blk_-3552845605773916309 terminating"}
]

chat = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(chat, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=20)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))