Pinguy1982/Rhizome
Fine-Tuning Pipeline for Memory-Infused Dialogic AI A modular pipeline for preparing, embedding, indexing, and fine-tuning dialogue data using LoRA and FAISS-backed memory on resource-constrained systems. ๐ฆ Prerequisites Platform: Linux (tested in Distrobox) Minimum Specs: CPU with 8 threads and 24 GB RAM for training. You can reduce memory usage by adjusting the following values in train.py: per_device_train_batch_size gradient_accumulation_steps Recommended:โฆ See the full description on the dataset page: https://huggingface.co/datasets/Pinguy1982/Rhizome.
| Author / organization | Pinguy1982 |
|---|---|
| Downloads | 588 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 21 August 2025 |
| Last updated at source | 3 December 2025 |
Recent changes
- 16 Oct 2026 Description: Fine-Tuning Pipeline for Memory-Infused Dialogic AI A modular pipeline for preparing, embedding, indexing, and fine-tuning dialogue data using LoRA and FAISS-backed memory on resource-constrained systems. ๐ฆ Prerequisites Platform: Linux (t โ Fine-Tuning Pipeline for Memory-Infused Dialogic AI A modular pipeline for preparing, embedding, indexing, and fine-tuning dialogue data using LoRA and FAISS-backed memory on resource-constrained systems. ๐ฆ Prerequisites Platform: Linux (t
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