| | ---
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| | license: other
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| | library_name: transformers
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| | tags:
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| | - autotrain
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| | - text-generation-inference
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| | - text-generation
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| | - peft
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| | widget:
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| | - messages:
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| | - role: user
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| | content: What is your favorite condiment?
|
| | ---
|
| |
|
| | # Model Trained Using AutoTrain
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| |
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| | This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
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| |
|
| | # Usage
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| |
|
| | ```python
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| |
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| | from transformers import AutoModelForCausalLM, AutoTokenizer
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| |
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| | model_path = "PATH_TO_THIS_REPO"
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| |
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| | tokenizer = AutoTokenizer.from_pretrained(model_path)
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| | model = AutoModelForCausalLM.from_pretrained(
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| | model_path,
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| | device_map="auto",
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| | torch_dtype='auto'
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| | ).eval()
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| |
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| | # Prompt content: "hi"
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| | messages = [
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| | {"role": "user", "content": "hi"}
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| | ]
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| |
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| | input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
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| | output_ids = model.generate(input_ids.to('cuda'))
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| | response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
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| |
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| | # Model response: "Hello! How can I assist you today?"
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| | print(response)
|
| | ``` |