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README.md
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@@ -16,12 +16,12 @@ Download the model
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# This is to set the path to save the model
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from pathlib import Path
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# Download the model
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from huggingface_hub import snapshot_download
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my_model = snapshot_download(repo_id="KLimaLima/finetuned-Question-Generation-mistral-7b-instruct", local_dir=
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```
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To load the model that have been downloaded
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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```
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To generate output
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```python
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outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
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tokenizer.batch_decode(outputs)
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```
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This model uses alpaca prompt format such as below
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```python
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Response:
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{}"""
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```
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# Uploaded model
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# This is to set the path to save the model
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from pathlib import Path
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models_path = Path.home().joinpath('Question_Generation_model', 'UTeMGPT')
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models_path.mkdir(parents=True, exist_ok=True)
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# Download the model
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from huggingface_hub import snapshot_download
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my_model = snapshot_download(repo_id="KLimaLima/finetuned-Question-Generation-mistral-7b-instruct", local_dir=models_path)
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```
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To load the model that have been downloaded
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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```
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This model uses alpaca prompt format such as below
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```python
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Response:
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{}"""
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instruction = 'Write an inquisitive question about a specific text span in a given sentence such that the answer is not in the text.'
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sentence = "I want to bake a cake during my free time. I need to know the ingredients that need to be use."
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inputs = tokenizer(
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[
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alpaca_prompt.format(
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instruction,
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sentence,
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"", # output - leave this blank for generation!
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)
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], return_tensors = "pt").to("cuda")
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```
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To generate output
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```python
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outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
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tokenizer.batch_decode(outputs)
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```
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# Uploaded model
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