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README.md
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@@ -35,16 +35,16 @@ In this version, we employed our new, improved decomposable ELM techniques on a
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There are three ELM Turbo slices derived from the `Meta-Llama-3.1-8B-Instruct` model:
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1. `slicexai/Llama3.1-elm-turbo-3B-instruct` (3B params)
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2. `slicexai/Llama3.1-elm-turbo-4B-instruct`(4B params)
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3. `slicexai/Llama3.1-elm-turbo-6B-instruct` (6B params)
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Make sure to update your transformers installation via pip install --upgrade transformers.
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Example - To run the `slicexai/Llama3.1-elm-turbo-
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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import torch
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elm_turbo_model = "slicexai/Llama3.1-elm-turbo-
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model = AutoModelForCausalLM.from_pretrained(
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elm_turbo_model,
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device_map="cuda",
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There are three ELM Turbo slices derived from the `Meta-Llama-3.1-8B-Instruct` model:
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1. `slicexai/Llama3.1-elm-turbo-3B-instruct` (3B params)
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2. `slicexai/Llama3.1-elm-turbo-4B-instruct`(4B params)
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**3. `slicexai/Llama3.1-elm-turbo-6B-instruct` (6B params)**
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Make sure to update your transformers installation via pip install --upgrade transformers.
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+
Example - To run the `slicexai/Llama3.1-elm-turbo-6B-instruct`
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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import torch
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elm_turbo_model = "slicexai/Llama3.1-elm-turbo-6B-instruct"
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model = AutoModelForCausalLM.from_pretrained(
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elm_turbo_model,
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device_map="cuda",
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