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--- |
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license: mit |
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datasets: |
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- yahma/alpaca-cleaned |
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- HuggingFaceH4/ultrafeedback_binarized |
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language: |
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- en |
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pipeline_tag: question-answering |
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library_name: transformers |
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--- |
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Model Description: armaGPT is a finetuned version of Gemma 7b, a pre-trained language model developed by Google. It is designed to generate human-like text based on the input it receives. And armaGPT is finetuned using DPO Training for fair and safe generation. |
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Model Architecture: The architecture of armaGPT is based on the transformer model, which is a type of recurrent neural network (RNN) that uses self-attention mechanisms to process input sequences. |
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Model Size: The model has approximately 7 billion parameters. |
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### Context Length |
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Models are trained on a context length of 8192 tokens. |
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#### Running the model on a CPU |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("sidharthsajith7/armaGPT") |
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model = AutoModelForCausalLM.from_pretrained("sidharthsajith7/armaGPT") |
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input_text = "Write me a poem about Machine Learning." |
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input_ids = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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#### Running the model on a single / multi GPU |
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```python |
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# pip install accelerate |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("sidharthsajith7/armaGPT") |
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model = AutoModelForCausalLM.from_pretrained("sidharthsajith7/armaGPT", device_map="auto") |
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input_text = "Write me a poem about Machine Learning." |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
