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Update README.md

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@@ -33,6 +33,9 @@ datasets:
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  ## Model description
 
 
 
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  The gemma-portuguese-2b model is a portuguese model trained with the superset dataset with 250,000 instructions.
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  The model is mainly focused on text generation and instruction.
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  The model was not trained on math and code tasks.
@@ -46,7 +49,7 @@ With this fine tuning for portuguese, you can adjust the model for a specific fi
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  from transformers import AutoTokenizer, pipeline
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  import torch
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- model = "rhaymison/gemma-portuguese-2b-luana"
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  tokenizer = AutoTokenizer.from_pretrained(model)
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  pipeline = pipeline(
@@ -83,8 +86,8 @@ print(outputs[0]["generated_text"][len(prompt):].replace("model",""))
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  ```python
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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- tokenizer2 = AutoTokenizer.from_pretrained("rhaymison/gemma-portuguese-2b-luana)
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- model2 = AutoModelForCausalLM.from_pretrained("rhaymison/gemma-portuguese-2b-luana", device_map={"":0})
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  tokenizer2.pad_token = tokenizer2.eos_token
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  tokenizer2.add_eos_token = True
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  tokenizer2.add_bos_token, tokenizer2.add_eos_token
 
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  ## Model description
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+
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+ updated: 2024-04-10 20:06
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+
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  The gemma-portuguese-2b model is a portuguese model trained with the superset dataset with 250,000 instructions.
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  The model is mainly focused on text generation and instruction.
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  The model was not trained on math and code tasks.
 
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  from transformers import AutoTokenizer, pipeline
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  import torch
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+ model = "rhaymison/gemma-portuguese-luana-2b"
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  tokenizer = AutoTokenizer.from_pretrained(model)
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  pipeline = pipeline(
 
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  ```python
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer2 = AutoTokenizer.from_pretrained("rhaymison/gemma-portuguese-luana-2b")
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+ model2 = AutoModelForCausalLM.from_pretrained("rhaymison/gemma-portuguese-luana-2b", device_map={"":0})
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  tokenizer2.pad_token = tokenizer2.eos_token
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  tokenizer2.add_eos_token = True
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  tokenizer2.add_bos_token, tokenizer2.add_eos_token