Instructions to use manihani4/portal-vlm-qwen3vl-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use manihani4/portal-vlm-qwen3vl-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-4B-Instruct") model = PeftModel.from_pretrained(base_model, "manihani4/portal-vlm-qwen3vl-lora") - Notebooks
- Google Colab
- Kaggle
adapter manifest entry
Browse files- manifest.json +23 -0
manifest.json
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{
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"name": "qwen3vl-lora",
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"kind": "peft",
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"base_model": "Qwen/Qwen3-VL-4B-Instruct",
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"base_revision": "ebb281ec70b05090aa6165b016eac8ec08e71b17",
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"config": "configs/qwen3vl_lora.yaml",
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"eval_result": "results/qwen3vl_lora.json",
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"description": "Fresh-training reference for the same-family porting row: from-scratch grounding LoRA (r16/a32, LM sites) on Qwen3-VL-4B, native 0-1000 dialect.",
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"trained_at_commit": "65cee69",
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"source_artifact": "PorTAL-vlm-probe results/adapters/m3_lora_ref_qwen3vl4b",
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"files": {
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"adapter_config.json": {
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"bytes": 1103,
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"sha256": "e83f629837cbd4d3fb6d96017bcc57675fd29be2c1e2ba4e6200fb68b30a8f7b"
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},
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"adapter_model.safetensors": {
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"bytes": 132195448,
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"sha256": "f630557387051a5cc8d7abdaa22c7e97376d3109270d7f926d4507814d86507e"
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}
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},
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"screenspot_overall": 0.9245,
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"screenspot_web": 0.9016
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}
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