Update README.md

README.md

@@ -13,4 +13,44 @@ Mists(**Mis**tral **T**ime **S**eries) model is a multimodal model that combines
 This model is based on the following models:  
  - [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
  - [AutonLab/MOMENT-1-large](https://huggingface.co/AutonLab/MOMENT-1-large)
-<!-- Provide a quick summary of what the model is/does. -->
+
+This model is experimental.  
+This model still has some flaws and cannot be used.  
+
+## How to load model
+
+```Python
+!pip install git+https://github.com/Hajime-Y/moment.git
+!pip install -U transformers
+!git clone https://github.com/Hajime-Y/Mists.git
+```
+
+```Python
+import torch
+
+from Mists.configuration_mists import MistsConfig
+from Mists.modeling_mists import MistsForConditionalGeneration
+from Mists.processing_mists import MistsProcessor
+
+model_id = "HachiML/Mists-7B-v0.1-not-trained"
+model = MistsForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+).to("cuda")
+processor = MistsProcessor.from_pretrained(model_id)
+```
+
+```Python
+import pandas as pd
+
+hist_ndaq_512 = pd.DataFrame("nasdaq_price_history.csv")
+time_series_data = torch.tensor(hist_ndaq_512[["Open", "High", "Low", "Close", "Volume"]].values, dtype=torch.float)
+time_series_data = time_series_data.t().unsqueeze(0)
+
+prompt = "USER: <time_series>\nWhat are the features of this data?\nASSISTANT:"
+inputs = processor(prompt, time_series_data, return_tensors='pt').to(torch.float32)
+
+output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+print(processor.decode(output[0], skip_special_tokens=True))
+```