How to use Ma-Vector/qwen_finetune_16bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ma-Vector/qwen_finetune_16bit") sentences = [ "ace-v", "The floor plan was drafted at 1/4 inch scale where each quarter inch equals one foot.", "Fingerprint examiners follow the ACE-V methodology for identification.", "Most modern streaming services offer content in 1080p full HD quality." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
How to use Ma-Vector/qwen_finetune_16bit with Unsloth Studio:
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 Ma-Vector/qwen_finetune_16bit to start chatting
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 Ma-Vector/qwen_finetune_16bit to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Ma-Vector/qwen_finetune_16bit to start chatting
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Ma-Vector/qwen_finetune_16bit", max_seq_length=2048, )
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