Instructions to use klhashim/glm-4.7-flash-JP-EN-prune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use klhashim/glm-4.7-flash-JP-EN-prune with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="klhashim/glm-4.7-flash-JP-EN-prune")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("klhashim/glm-4.7-flash-JP-EN-prune") model = AutoModelForMultimodalLM.from_pretrained("klhashim/glm-4.7-flash-JP-EN-prune") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Expert-pruned GLM-4.7-Flash for Japanese→English subtitle translation. Routed-expert usage was measured on subtitle data and the least-used experts dropped (64 → 32 per layer). ~16B parameters.
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [More Information Needed]
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- Language(s) (NLP): Japanese, English
- License: MIT
- Finetuned from model [optional]: GLM-4.7-Flash
Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Results
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Summary
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Model Architecture and Objective
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Hardware
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Software
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Citation / Data Attribution
Partial calibration data: JESC (Japanese-English Subtitle Corpus), licensed under CC BY 4.0. Source: https://nlp.stanford.edu/projects/jesc/
@ARTICLE{pryzant_jesc_2018, author = {{Pryzant}, R. and {Chung}, Y. and {Jurafsky}, D. and {Britz}, D.}, title = "{JESC: Japanese-English Subtitle Corpus}", journal = {Language Resources and Evaluation Conference (LREC)}, keywords = {Computer Science - Computation and Language}, year = 2018 }
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Model Card Authors [optional]
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Base model
zai-org/GLM-4.7-Flash