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--- |
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library_name: transformers |
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tags: |
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- time series |
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- multimodal |
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- TimeSeries-Text-to-Text |
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license: apache-2.0 |
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--- |
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# Mists-7B-v0.1-not-trained |
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Mists(**Mis**tral **T**ime **S**eries) model is a multimodal model that combines language and time series model. |
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This model is based on the following models: |
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- [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) |
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- [AutonLab/MOMENT-1-large](https://huggingface.co/AutonLab/MOMENT-1-large) |
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This is an experimental model. |
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It has some limitations and is not suitable for use at this time. |
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## How to load model |
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```Python |
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!pip install git+https://github.com/Hajime-Y/moment.git |
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!pip install -U transformers |
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!git clone https://github.com/Hajime-Y/Mists.git |
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``` |
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```Python |
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import torch |
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from Mists.configuration_mists import MistsConfig |
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from Mists.modeling_mists import MistsForConditionalGeneration |
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from Mists.processing_mists import MistsProcessor |
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model_id = "HachiML/Mists-7B-v0.1-not-trained" |
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model = MistsForConditionalGeneration.from_pretrained( |
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model_id, |
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torch_dtype=torch.bfloat16, |
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low_cpu_mem_usage=True, |
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).to("cuda") |
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processor = MistsProcessor.from_pretrained(model_id) |
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``` |
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```Python |
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import pandas as pd |
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hist_ndaq_512 = pd.DataFrame("nasdaq_price_history.csv") |
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time_series_data = torch.tensor(hist_ndaq_512[["Open", "High", "Low", "Close", "Volume"]].values, dtype=torch.float) |
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time_series_data = time_series_data.t().unsqueeze(0) |
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prompt = "USER: <time_series>\nWhat are the features of this data?\nASSISTANT:" |
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inputs = processor(prompt, time_series_data, return_tensors='pt').to(torch.float32) |
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output = model.generate(**inputs, max_new_tokens=200, do_sample=False) |
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print(processor.decode(output[0], skip_special_tokens=True)) |
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``` |
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