Summarization
Transformers
PyTorch
Safetensors
English
Portuguese
mt5
text2text-generation
Eval Results (legacy)
Instructions to use cloudqi/cqi_brain_memory_summarizer_large_pt_v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudqi/cqi_brain_memory_summarizer_large_pt_v0 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="cloudqi/cqi_brain_memory_summarizer_large_pt_v0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cloudqi/cqi_brain_memory_summarizer_large_pt_v0") model = AutoModelForSeq2SeqLM.from_pretrained("cloudqi/cqi_brain_memory_summarizer_large_pt_v0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (387a3f320cc99e2bd5e63da8cfdcde0b0c5ee1b1) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>e62262d
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Test Examp´le
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readme
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Test V1
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Update README.md
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BaseTestv00
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initial commit
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