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Ma-Vector
/
qwen_finetune_16bit

Sentence Similarity
sentence-transformers
Safetensors
English
qwen3
unsloth
feature-extraction
Generated from Trainer
dataset_size:106628
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Ma-Vector/qwen_finetune_16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    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]
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • Unsloth Studio new

    How to use Ma-Vector/qwen_finetune_16bit 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 Ma-Vector/qwen_finetune_16bit 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 Ma-Vector/qwen_finetune_16bit to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for Ma-Vector/qwen_finetune_16bit to start chatting
    Load model with FastModel
    pip install unsloth
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name="Ma-Vector/qwen_finetune_16bit",
        max_seq_length=2048,
    )
qwen_finetune_16bit
1.21 GB
Ctrl+K
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  • 1 contributor
History: 2 commits
Ma-Vector's picture
Ma-Vector
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1e6d750 verified 10 days ago
  • 1_Pooling
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  • .gitattributes
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  • README.md
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  • added_tokens.json
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  • chat_template.jinja
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  • config.json
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  • config_sentence_transformers.json
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  • merges.txt
    1.67 MB
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  • model.safetensors
    1.19 GB
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  • modules.json
    349 Bytes
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    613 Bytes
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  • tokenizer.json
    11.4 MB
    xet
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  • tokenizer_config.json
    9.73 kB
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  • vocab.json
    2.78 MB
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