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--- |
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tags: |
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- text-generation-inference |
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- text-generation |
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- peft |
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library_name: transformers |
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widget: |
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- messages: |
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- role: user |
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content: What is better for gut health protein or carbs ? |
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license: mit |
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pipeline_tag: text-generation |
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--- |
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# Model Trained Using WEBSPACEAI_TRANIER AGENT |
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WEBSPACEAI Trainer Agent is a powerful tool designed to streamline the training of large language models (LLMs) using PyTorch and TensorFlow. It offers efficient data management, enabling users to curate and organize datasets for optimal model performance. The platform supports comprehensive training strategies, allowing for iterative adjustments to enhance accuracy. With collaboration features and a user-friendly interface, the Trainer Agent makes AI training accessible to both technical and non-technical users, empowering organizations to develop sophisticated AI solutions tailored to their needs. |
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# Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_path = "PATH_TO_THIS_REPO" |
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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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# Prompt content: "hi" |
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messages = [ |
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{"role": "user", "content": "hi"} |
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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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# Model response: "Hello! How can I assist you today?" |
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print(response) |
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``` |
