Instructions to use sandspeare/llasm-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sandspeare/llasm-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sandspeare/llasm-encoder", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sandspeare/llasm-encoder", trust_remote_code=True) model = AutoModel.from_pretrained("sandspeare/llasm-encoder", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 2ac000a
encoder 1.0
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README.md
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<h1 align="center">llasm: Naming Functions in Binaries by Fusing Encoder-only and Decoder-only LLMs</h1>
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## About
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llasm, is a novel framework that fuses encoder-only and decoder-only LLMs, which utilizes their capabilities to better comprehend assembly language and have better generalizability at function naming.
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<h1 align="center">llasm: Naming Functions in Binaries by Fusing Encoder-only and Decoder-only LLMs</h1>
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## About
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llasm, is a novel framework that fuses encoder-only and decoder-only LLMs, which utilizes their capabilities to better comprehend assembly language and have better generalizability at function naming.
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This is the encoder of llasm.
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