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@@ -22,26 +22,6 @@ text = "<s>[INST] What is your favourite condiment? [/INST]"
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  "[INST] Do you have mayonnaise recipes? [/INST]"
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  ```
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- This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
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-
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- ```python
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- from transformers import AutoModelForCausalLM, AutoTokenizer
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- device = "cuda" # the device to load the model onto
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- model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
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- tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
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- messages = [
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- {"role": "user", "content": "What is your favourite condiment?"},
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- {"role": "Luna", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
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- {"role": "user", "content": "Do you have mayonnaise recipes?"}
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- ]
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- encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
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- model_inputs = encodeds.to(device)
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- model.to(device)
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- generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
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- decoded = tokenizer.batch_decode(generated_ids)
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- print(decoded[0])
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- ```
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-
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  ## Model Architecture
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  This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
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  - Grouped-Query Attention
 
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  "[INST] Do you have mayonnaise recipes? [/INST]"
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  ```
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  ## Model Architecture
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  This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
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  - Grouped-Query Attention