Instructions to use monsoon-nlp/llama3-biotokenpretrain-kaniwa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use monsoon-nlp/llama3-biotokenpretrain-kaniwa with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("gradientai/Llama-3-8B-Instruct-262k") model = PeftModel.from_pretrained(base_model, "monsoon-nlp/llama3-biotokenpretrain-kaniwa") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use monsoon-nlp/llama3-biotokenpretrain-kaniwa with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for monsoon-nlp/llama3-biotokenpretrain-kaniwa to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for monsoon-nlp/llama3-biotokenpretrain-kaniwa to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for monsoon-nlp/llama3-biotokenpretrain-kaniwa to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="monsoon-nlp/llama3-biotokenpretrain-kaniwa", max_seq_length=2048, )
uppercase nucleotides for biotokens
Browse files
README.md
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@@ -54,7 +54,7 @@ tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-k
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tokenizer.pad_token = tokenizer.eos_token # pad fix
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qed = "∎" # from math symbols, used in pretraining
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sequence = "".join([(qed + nt) for nt in "GCCTATAGTGTGTAGCTAATGAGCCTAGGTTATCGACCCTAATCT"])
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inputs = tokenizer(f"{prefix}{sequence}{annotation}", return_tensors="pt")
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outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50)
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tokenizer.pad_token = tokenizer.eos_token # pad fix
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qed = "∎" # from math symbols, used in pretraining
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sequence = "".join([(qed + nt.upper()) for nt in "GCCTATAGTGTGTAGCTAATGAGCCTAGGTTATCGACCCTAATCT"])
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inputs = tokenizer(f"{prefix}{sequence}{annotation}", return_tensors="pt")
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outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50)
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