Transformers
Safetensors
t5
text2text-generation
protein-language-model
fastplms
custom_code
text-generation-inference
Instructions to use Synthyra/ANKH3_xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Synthyra/ANKH3_xl with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Synthyra/ANKH3_xl", trust_remote_code=True) model = AutoModelForSeq2SeqLM.from_pretrained("Synthyra/ANKH3_xl", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
22 days ago
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Update FastPLMs files from Synthyra/FastPLMs 32d9951 (PR 52: ESMFold2 noise cap, DPLM2 parity)
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Give this artifact's bridge its own config and model subclasses (FastPLMs#50)
Verified signatureArtifacts share one FastPLMs runtime per process. Re-exported shared classes let one artifact's model class answer for another with the same config class, for example ESMFold2-300 built as ESMFold2Model after ESMFold2-Fast loaded. Only modeling_fastplms.py changes; the runtime bundle and weights are untouched. https://github.com/Synthyra/FastPLMs/issues/5053faab5
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Sync current FastPLMs source, model cards and dependencies
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Update FastPLMs runtime and model cards
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Update FastPLMs runtime files
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Update FastPLMs runtime files
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Update FastPLMs runtime files
Verified signatureAtomic FastPLMs complete publication. Checkpoint weights, tokenizer assets, runtime sources, model card, legal texts, and scoped attestations are updated together. Deletes are restricted to obsolete current-registry-pinned paths.d285689
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