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taylor-m1
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@@ -10,7 +10,7 @@ tags:
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  - taylor-m1
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  ---
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- # constructai/taylor-m1-38m-128
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  ---
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@@ -23,7 +23,7 @@ The model produces **384-dimensional** embeddings (CLS token) optimized for cosi
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  # Training details
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- * ~38.3M parameters
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  * **Max sequence length: 128 tokens**
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@@ -66,7 +66,7 @@ from taylor_model import load_taylor_model, embed_texts
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  import torch
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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- model, tokenizer, _ = load_taylor_model("constructai/taylor-m1-38m-128", device=device)
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  texts = ["What is a neural network?", "How to make pizza at home?"]
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  embeddings = embed_texts(model, tokenizer, texts, device=device)
@@ -80,7 +80,7 @@ from taylor_model import load_taylor_model, embed_texts
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  import torch
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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- model, tokenizer, _ = load_taylor_model("constructai/taylor-m1-38m-128", device=device)
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  queries = [
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  "What is a neural network?",
 
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  - taylor-m1
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  ---
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+ # constructai/taylor-m1-22m-128
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  ---
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  # Training details
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+ * ~22 417 920M parameters
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  * **Max sequence length: 128 tokens**
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  import torch
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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+ model, tokenizer, _ = load_taylor_model("constructai/taylor-m1-22m-128", device=device)
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  texts = ["What is a neural network?", "How to make pizza at home?"]
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  embeddings = embed_texts(model, tokenizer, texts, device=device)
 
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  import torch
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  device = "cuda" if torch.cuda.is_available() else "cpu"
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+ model, tokenizer, _ = load_taylor_model("constructai/taylor-m1-22m-128", device=device)
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  queries = [
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  "What is a neural network?",