Delete sessions/2026-05-13/d16efa0c-b826-4a63-85d1-3dbed4e6203d.jsonl

sessions/2026-05-13/d16efa0c-b826-4a63-85d1-3dbed4e6203d.jsonl

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-{"type": "user", "message": {"role": "user", "content": "fine-tune Liquid LFM2.5-VL-450M on VisCor55k dataset for visual question answering"}, "uuid": "a553f64c-c663-2acf-e37b-c21224032b08", "parentUuid": null, "sessionId": "d16efa0c-b826-4a63-85d1-3dbed4e6203d", "timestamp": "2026-05-13T15:49:04.256078"}
-{"type": "assistant", "message": {"role": "assistant", "model": "llamacpp/gemma-4-26b-a4b-it", "content": [{"type": "tool_use", "id": "WUFFOmzhuez4Ynte3sO5F6VKMYf1aWzp", "name": "research", "input": {"context": "User wants to fine-tune Liquid LFM2.5-VL-450M on VisCor55k for VQA. I need to find the correct HF IDs for the model and dataset, understand the model architecture (how it processes images), and verify the dataset format. I also need to find the current recommended way to fine-tune VLMs using TRL/Transformers (e.g., SFTTrainer with vision support).", "task": "Research 'Liquid/LFM2.5-VL-450M' and 'VisCor55k' on Hugging Face. \n1. Find exact repo IDs for the model and dataset.\n2. For the model: Check if it's a vision-language model and how it handles multimodal inputs (e.g., using `AutoModelForVision2Seq`).\n3. For the dataset: Check the schema and if it's in a format suitable for SFT (e.g., 'messages' with images).\n4. Find working code examples for fine-tuning VLMs using TRL's SFTTrainer or similar, especially for small multimodal models."}}]}, "uuid": "db76348a-f4e3-c59d-e6e4-6eb9fddfd760", "parentUuid": "a553f64c-c663-2acf-e37b-c21224032b08", "sessionId": "d16efa0c-b826-4a63-85d1-3dbed4e6203d", "timestamp": "2026-05-13T15:49:04.256078"}