Fill-Mask
Transformers
Safetensors
English
esmc
biology
esm
protein
protein-language-model
protein-embeddings
masked-language-modeling
transfer-learning
variant-effect-prediction
protein-engineering
Instructions to use biohub/ESMC-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-300M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="biohub/ESMC-300M")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("biohub/ESMC-300M", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -28,7 +28,7 @@ The ESMC 6B model has 6 billion parameters, with 80 layers and 2.37e23 training
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The [ESMFold2](https://huggingface.co/biohub/ESMFold2) structure prediction models are trained on top of a frozen ESMC 6B language model. ESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy.
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The [ESMC sparse autoencoder](https://huggingface.co/
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To run this model with the Biohub Platform API, visit the [Biohub Platform](https://biohub.ai/).
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Install `esm` from GitHub (a PyPI release is coming soon):
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```
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pip install esm@git+https://github.com/Biohub/esm.git@
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```
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```py
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| Model Variant | Description | URL |
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| ESMC 300M | Smallest variant, publicly released. | [https://huggingface.co/
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| ESMC 600M | Medium variant, publicly released. | [https://huggingface.co/
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| ESMC 6B | Large variant, publicly released. | [https://huggingface.co/
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### System Requirements
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The [ESMFold2](https://huggingface.co/biohub/ESMFold2) structure prediction models are trained on top of a frozen ESMC 6B language model. ESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy.
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The [ESMC sparse autoencoder](https://huggingface.co/biohub/ESMC-6B-sae-layer60-k64-codebook16384), `ESMC-6B-sae-layer60-k64-codebook16384`, is built on the ESMC 6B model and provides human-interpretable, agent-generated feature descriptions. See the [ESMC SAE overview card](https://huggingface.co/biohub/ESMC-SAE-Overview) for the full set of ESMC SAE variants.
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To run this model with the Biohub Platform API, visit the [Biohub Platform](https://biohub.ai/).
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Install `esm` from GitHub (a PyPI release is coming soon):
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```
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pip install esm@git+https://github.com/Biohub/esm.git@main
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```
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```py
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| Model Variant | Description | URL |
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| :---- | :---- | :---- |
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| ESMC 300M | Smallest variant, publicly released. | [https://huggingface.co/biohub/ESMC-300M](https://huggingface.co/biohub/ESMC-300M) |
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| ESMC 600M | Medium variant, publicly released. | [https://huggingface.co/biohub/ESMC-600M](https://huggingface.co/biohub/ESMC-600M) |
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| ESMC 6B | Large variant, publicly released. | [https://huggingface.co/biohub/ESMC-6B](https://huggingface.co/biohub/ESMC-6B) |
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### System Requirements
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