Instructions to use BroAlanTaps/Stage1-PCC-Lite-64x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BroAlanTaps/Stage1-PCC-Lite-64x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BroAlanTaps/Stage1-PCC-Lite-64x")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("BroAlanTaps/Stage1-PCC-Lite-64x") model = AutoModelForMultimodalLM.from_pretrained("BroAlanTaps/Stage1-PCC-Lite-64x") - Notebooks
- Google Colab
- Kaggle
File size: 359 Bytes
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library_name: transformers
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
metrics:
- f1
- bleu
- rouge
base_model:
- openai-community/gpt2-large
pipeline_tag: feature-extraction
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
**PCC-Lite-64x Model:**
Compressor: Continue pre-training based on GPT2-Large |