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When dealing with large-scale unstructured data (1TB+) for AI inference or fine-tuning, traditional methods like RAG (Retrieval-Augmented Generation) may not suffice. Hereβs a breakdown of efficient approaches, including practical commands and workflows.
You Should Know:
1. Local vs. Cloud Inference
- Local Models: Ideal for privacy-sensitive data. Use Hugging Faceβs `SFT Trainer` or `qLoRa` for fine-tuning.
Install Hugging Face transformers pip install transformers datasets accelerate peft Fine-tune with qLoRa (4-bit quantization) python -m bitsandbytes transformers finetune.py --model_name=meta-llama/Llama-2-7b --use_qlora=True
Cloud Inference: For scalable processing (AWS SageMaker, GCP Vertex AI).
AWS CLI to launch SageMaker instance aws sagemaker create-training-job --training-job-name "llama-finetune" --algorithm-specification TrainingImage=763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.10.2-transformers4.17.0-gpu-py38-cu113-ubuntu18.04
2. Handling 1TB Unstructured Data
Chunking & Embedding: Use `LlamaIndex` or `FAISS` for efficient retrieval.
from llama_index import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader("1TB_data_dir").load_data() index = VectorStoreIndex.from_documents(documents)
Parallel Processing: Leverage `GNU Parallel` for batch processing.
Split and process files in parallel find /data/unstructured -type f | parallel -j 8 'python embed.py {}'
3. Optimizing Context Windows
- Modern models (e.g., Llama 3) support 10M+ tokens, but filtering is key:
Use jq to pre-filter JSON logs cat large_logs.json | jq '. | select(.priority == "HIGH")' > filtered_logs.json
What Undercode Say:
- Local Fine-Tuning: Best for control (
torchrun
,deepspeed
).deepspeed --num_gpus=4 finetune_hf_model.py --model_name=bigscience/bloom-7b1
- Cloud Hybrid: Pre-process locally, infer in cloud (
gsutil
for GCP).gsutil -m cp -r ./processed_data gs://my-bucket/
- Embedding at Scale: Use `Sentence-Transformers` for semantic search.
from sentence_transformers import SentenceTransformer model = SentenceTransformer('all-MiniLM-L6-v2') embeddings = model.encode(["your text here"])
Prediction:
As context windows expand, RAG will decline for smaller datasets, but hybrid approaches (local fine-tuning + cloud inference) will dominate for TB-scale data.
Expected Output:
- Filtered datasets ready for model ingestion.
- Optimized embeddings for retrieval.
- Hybrid cloud/local pipelines.
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