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QueryloopAI
/
MonarchCoder-7B

Feature Extraction
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0
mlabonne/AlphaMonarch-7B
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use QueryloopAI/MonarchCoder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use QueryloopAI/MonarchCoder-7B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="QueryloopAI/MonarchCoder-7B")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("QueryloopAI/MonarchCoder-7B")
    model = AutoModel.from_pretrained("QueryloopAI/MonarchCoder-7B")
  • Notebooks
  • Google Colab
  • Kaggle
MonarchCoder-7B
28.4 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
abideen's picture
abideen
Upload tokenizer
15a0b20 verified about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    5.17 kB
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  • config.json
    633 Bytes
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  • model-00001-of-00006.safetensors
    4.99 GB
    xet
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  • model-00002-of-00006.safetensors
    4.9 GB
    xet
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  • model-00003-of-00006.safetensors
    5 GB
    xet
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  • model-00004-of-00006.safetensors
    5 GB
    xet
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  • model-00005-of-00006.safetensors
    4.83 GB
    xet
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  • model-00006-of-00006.safetensors
    3.72 GB
    xet
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  • model.safetensors.index.json
    22.2 kB
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  • special_tokens_map.json
    624 Bytes
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  • tokenizer.json
    1.8 MB
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  • tokenizer_config.json
    1.25 kB
    Upload tokenizer about 2 years ago