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  ## Llama 2-13b-alpaca-spanish LoRA
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  This is a LoRA for Llama 2 13B trained on a translated [alpaca dataset](https://huggingface.co/datasets/bertin-project/alpaca-spanish) on an attempt to improve spanish performance of the Llama-2 foundation model with a conversational focus.
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- Base model used was [The Bloke's Llama-2-13B-fp16](https://huggingface.co/TheBloke/Llama-2-13B-fp16) trained in 4bit precision.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  | Training parameteres | |
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  | ----------- | ----------- |
 
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  ## Llama 2-13b-alpaca-spanish LoRA
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  This is a LoRA for Llama 2 13B trained on a translated [alpaca dataset](https://huggingface.co/datasets/bertin-project/alpaca-spanish) on an attempt to improve spanish performance of the Llama-2 foundation model with a conversational focus.
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+ Base model used was [The Bloke's Llama-2-13B-fp16](https://huggingface.co/TheBloke/Llama-2-13B-fp16) trained in 4bit precision with an added padding token.
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+
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+ ## Important INFO
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+ The original Llama 2 model does not have a padding token, this came to be restrictive for training for me. To adress this, I added a padding token to the tokenizer associated with the model.
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+ ```python
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+ from transformers import LlamaTokenizer, LlamaForCausalLM
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+
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+ model_name = 'TheBloke/Llama-2-13B-fp16'
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+
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+ model = LlamaForCausalLM.from_pretrained(model_name).half()
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+ tokenizer = LlamaTokenizer.from_pretrained(model_name)
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+
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+ # Add padding token
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+ tokenizer.add_tokens(['<PAD>'])
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+ tokenizer.pad_token = '<PAD>'
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+
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+ # Resizing the model
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+ model.resize_token_embeddings(len(tokenizer))
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+
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+ padded_model_name = 'Llama-2-13B-fp16-padded'
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+
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+ # Save
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+ tokenizer.save_pretrained(padded_model_name)
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+ model.save_pretrained(padded_model_name)
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+
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+ ```
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  | Training parameteres | |
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  | ----------- | ----------- |