Transformers How to use ictbiortc/beaker-volume-recognition-model-qwen-3b_merged with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("feature-extraction", model="ictbiortc/beaker-volume-recognition-model-qwen-3b_merged") # Load model directly
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("ictbiortc/beaker-volume-recognition-model-qwen-3b_merged")
model = AutoModel.from_pretrained("ictbiortc/beaker-volume-recognition-model-qwen-3b_merged") Unsloth Studio new How to use ictbiortc/beaker-volume-recognition-model-qwen-3b_merged with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ictbiortc/beaker-volume-recognition-model-qwen-3b_merged to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ictbiortc/beaker-volume-recognition-model-qwen-3b_merged to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ictbiortc/beaker-volume-recognition-model-qwen-3b_merged to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="ictbiortc/beaker-volume-recognition-model-qwen-3b_merged",
max_seq_length=2048,
) 
