HachiML
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Mists-7B-v01-not-trained

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 ---
 library_name: transformers
+tags:
+- time series
+- multimodal
+- TimeSeries-Text-to-Text
+license: apache-2.0
 ---
+# Mists-7B-v01-not-trained
+Mists(**Mis**tral **T**ime **S**eries) is a multimodal model that combines language and time series model.
+This model is based on the following models:
+ - [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
+ - [HachiML/MOMENT-1-large-embedding-v0.1](https://huggingface.co/HachiML/MOMENT-1-large-embedding-v0.1) (an embedding model derived from [AutonLab/MOMENT-1-large](https://huggingface.co/AutonLab/MOMENT-1-large))
+This is an experimental model.
+Since the adapter has not been trained, the model is not yet suitable for use.
+## How to use
+```Python
+!pip install accelerate
+```
+```Python
+from transformers import AutoProcessor, AutoModel
+import torch
+model_id = "HachiML/Mists-7B-v01-not-trained"
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModel.from_pretrained(
+    model_id,
+    torch_dtype=torch.float32,
+    low_cpu_mem_usage=True,
+    device_map="auto",
+    trust_remote_code=True,
+)
+```
+```Python
+import pandas as pd
+import torch
+hist_ndaq = pd.DataFrame("nasdaq_price_history.csv")
+time_series_data = hist_ndaq[["Open", "High", "Low", "Close", "Volume"]].iloc[:512]
+prompt = "USER: <time_series>\nWhat are the features of this data?\nASSISTANT:"
+inputs = processor(prompt, time_series_data, return_tensors='pt')
+device = "cuda" if torch.cuda.is_available() else "cpu"
+for key, item in inputs.items():
+    inputs[key] = inputs[key].to(device)
+output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+print(processor.decode(output[0], skip_special_tokens=False))
+```