Feature Extraction
Transformers
Safetensors
English
llama
text-generation
DNA
RNA
genomic
metagenomic
metagene
metagene-1
text-embeddings-inference
Instructions to use metagene-ai/METAGENE-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use metagene-ai/METAGENE-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="metagene-ai/METAGENE-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("metagene-ai/METAGENE-1") model = AutoModelForCausalLM.from_pretrained("metagene-ai/METAGENE-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -33,13 +33,13 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
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# Load the tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("metagene-ai/METAGENE-1")
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model = AutoModelForCausalLM.from_pretrained("metagene-ai/METAGENE-1", torch_dtype=torch.bfloat16)
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# Example input sequence
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input_sequence = "TCACCGTTCTACAATCCCAAGCTGGAGTCAAGCTCAACAGGGTCTTC"
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# Tokenize the input sequence and remove the [EOS] token for generation
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input_tokens = tokenizer.encode(input_sequence, return_tensors="pt", add_special_tokens=False)
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# Generate output from the model
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generated_tokens = model.generate(input_tokens, max_length=32)
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# Load the tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("metagene-ai/METAGENE-1")
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model = AutoModelForCausalLM.from_pretrained("metagene-ai/METAGENE-1", torch_dtype=torch.bfloat16, device_map="auto")
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# Example input sequence
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input_sequence = "TCACCGTTCTACAATCCCAAGCTGGAGTCAAGCTCAACAGGGTCTTC"
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# Tokenize the input sequence and remove the [EOS] token for generation
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input_tokens = tokenizer.encode(input_sequence, return_tensors="pt", add_special_tokens=False).to(model.device)
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# Generate output from the model
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generated_tokens = model.generate(input_tokens, max_length=32)
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