Instructions to use arinbalyan/smolvlm-chartqa-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use arinbalyan/smolvlm-chartqa-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolVLM-256M-Instruct") model = PeftModel.from_pretrained(base_model, "arinbalyan/smolvlm-chartqa-lora") - Notebooks
- Google Colab
- Kaggle
SmolVLM ChartQA LoRA Adapter
Note: Training did not complete successfully.
This directory was set up as the model repo for a SmolVLM LoRA adapter fine-tuned on ChartQA. However, the Kaggle training notebook encountered an environment error (torchaudio version conflict with torch 2.5.1 on P100 GPU) and did not produce LoRA weights.
Status
- Training notebook:
arinbalyan/smolvlm-fine-tune-on-chartqa - Issue: OSError โ
libtorchaudio.so: undefined symbol: aoti_torch_memory_format_preserve_formatcaused bytorchaudio 2.10.0+cu128requiringtorch==2.10.0while the P100 environment pinstorch==2.5.1+cu118. - No LoRA weights were produced due to this environment conflict.
Next Steps
Rerun the notebook in an environment without the torchaudio conflict (e.g., disable torchaudio install or use compatible versions) to generate actual adapter_model.safetensors.
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Model tree for arinbalyan/smolvlm-chartqa-lora
Base model
HuggingFaceTB/SmolLM2-135M Quantized
HuggingFaceTB/SmolLM2-135M-Instruct Quantized
HuggingFaceTB/SmolVLM-256M-Instruct