Visual Question Answering
PEFT
Safetensors
English
decision-model
calibration
typesafe
kev
jev
vision
qwen3.5
Instructions to use Jacqkues/sev-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jacqkues/sev-0.8b with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen3.5-0.8B-Base") model = PeftModel.from_pretrained(base_model, "Jacqkues/sev-0.8b") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Jacqkues/sev-0.8b: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/Jacqkues/sev-0.8b/resolve/4a0a64eb04957e0609b2ec9a15e42e55e8d7485e/tokenizer.json
- Command line
-
hf download hf://Jacqkues/sev-0.8b@4a0a64eb04957e0609b2ec9a15e42e55e8d7485e/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Jacqkues/sev-0.8b/resolve/4a0a64eb04957e0609b2ec9a15e42e55e8d7485e/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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