HachiML commited on
Commit
4a5f9eb
1 Parent(s): 3c3cbb5

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +42 -183
README.md CHANGED
@@ -1,199 +1,58 @@
1
  ---
2
  library_name: transformers
3
- tags: []
 
 
 
 
4
  ---
5
 
6
- # Model Card for Model ID
7
 
8
- <!-- Provide a quick summary of what the model is/does. -->
 
 
 
9
 
 
 
10
 
 
11
 
12
- ## Model Details
 
 
13
 
14
- ### Model Description
 
 
15
 
16
- <!-- Provide a longer summary of what this model is. -->
17
 
18
- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
 
 
 
 
 
 
 
19
 
20
- - **Developed by:** [More Information Needed]
21
- - **Funded by [optional]:** [More Information Needed]
22
- - **Shared by [optional]:** [More Information Needed]
23
- - **Model type:** [More Information Needed]
24
- - **Language(s) (NLP):** [More Information Needed]
25
- - **License:** [More Information Needed]
26
- - **Finetuned from model [optional]:** [More Information Needed]
27
 
28
- ### Model Sources [optional]
 
29
 
30
- <!-- Provide the basic links for the model. -->
 
31
 
32
- - **Repository:** [More Information Needed]
33
- - **Paper [optional]:** [More Information Needed]
34
- - **Demo [optional]:** [More Information Needed]
35
 
36
- ## Uses
37
-
38
- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
-
40
- ### Direct Use
41
-
42
- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
-
44
- [More Information Needed]
45
-
46
- ### Downstream Use [optional]
47
-
48
- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
-
50
- [More Information Needed]
51
-
52
- ### Out-of-Scope Use
53
-
54
- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
-
56
- [More Information Needed]
57
-
58
- ## Bias, Risks, and Limitations
59
-
60
- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
-
62
- [More Information Needed]
63
-
64
- ### Recommendations
65
-
66
- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
-
68
- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
-
70
- ## How to Get Started with the Model
71
-
72
- Use the code below to get started with the model.
73
-
74
- [More Information Needed]
75
-
76
- ## Training Details
77
-
78
- ### Training Data
79
-
80
- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
-
82
- [More Information Needed]
83
-
84
- ### Training Procedure
85
-
86
- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
-
88
- #### Preprocessing [optional]
89
-
90
- [More Information Needed]
91
-
92
-
93
- #### Training Hyperparameters
94
-
95
- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
-
97
- #### Speeds, Sizes, Times [optional]
98
-
99
- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
-
101
- [More Information Needed]
102
-
103
- ## Evaluation
104
-
105
- <!-- This section describes the evaluation protocols and provides the results. -->
106
-
107
- ### Testing Data, Factors & Metrics
108
-
109
- #### Testing Data
110
-
111
- <!-- This should link to a Dataset Card if possible. -->
112
-
113
- [More Information Needed]
114
-
115
- #### Factors
116
-
117
- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
-
119
- [More Information Needed]
120
-
121
- #### Metrics
122
-
123
- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
-
125
- [More Information Needed]
126
-
127
- ### Results
128
-
129
- [More Information Needed]
130
-
131
- #### Summary
132
-
133
-
134
-
135
- ## Model Examination [optional]
136
-
137
- <!-- Relevant interpretability work for the model goes here -->
138
-
139
- [More Information Needed]
140
-
141
- ## Environmental Impact
142
-
143
- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
-
145
- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
-
147
- - **Hardware Type:** [More Information Needed]
148
- - **Hours used:** [More Information Needed]
149
- - **Cloud Provider:** [More Information Needed]
150
- - **Compute Region:** [More Information Needed]
151
- - **Carbon Emitted:** [More Information Needed]
152
-
153
- ## Technical Specifications [optional]
154
-
155
- ### Model Architecture and Objective
156
-
157
- [More Information Needed]
158
-
159
- ### Compute Infrastructure
160
-
161
- [More Information Needed]
162
-
163
- #### Hardware
164
-
165
- [More Information Needed]
166
-
167
- #### Software
168
-
169
- [More Information Needed]
170
-
171
- ## Citation [optional]
172
-
173
- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
-
175
- **BibTeX:**
176
-
177
- [More Information Needed]
178
-
179
- **APA:**
180
-
181
- [More Information Needed]
182
-
183
- ## Glossary [optional]
184
-
185
- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
-
187
- [More Information Needed]
188
-
189
- ## More Information [optional]
190
-
191
- [More Information Needed]
192
-
193
- ## Model Card Authors [optional]
194
-
195
- [More Information Needed]
196
-
197
- ## Model Card Contact
198
-
199
- [More Information Needed]
 
1
  ---
2
  library_name: transformers
3
+ tags:
4
+ - time series
5
+ - multimodal
6
+ - TimeSeries-Text-to-Text
7
+ license: apache-2.0
8
  ---
9
 
10
+ # Mists-7B-v01-not-trained
11
 
12
+ Mists(**Mis**tral **T**ime **S**eries) is a multimodal model that combines language and time series model.
13
+ This model is based on the following models:
14
+ - [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
15
+ - [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))
16
 
17
+ This is an experimental model.
18
+ Since the adapter has not been trained, the model is not yet suitable for use.
19
 
20
+ ## How to use
21
 
22
+ ```Python
23
+ !pip install accelerate
24
+ ```
25
 
26
+ ```Python
27
+ from transformers import AutoProcessor, AutoModel
28
+ import torch
29
 
30
+ model_id = "HachiML/Mists-7B-v01-not-trained"
31
 
32
+ processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
33
+ model = AutoModel.from_pretrained(
34
+ model_id,
35
+ torch_dtype=torch.float32,
36
+ low_cpu_mem_usage=True,
37
+ device_map="auto",
38
+ trust_remote_code=True,
39
+ )
40
+ ```
41
 
42
+ ```Python
43
+ import pandas as pd
44
+ import torch
 
 
 
 
45
 
46
+ hist_ndaq = pd.DataFrame("nasdaq_price_history.csv")
47
+ time_series_data = hist_ndaq[["Open", "High", "Low", "Close", "Volume"]].iloc[:512]
48
 
49
+ prompt = "USER: <time_series>\nWhat are the features of this data?\nASSISTANT:"
50
+ inputs = processor(prompt, time_series_data, return_tensors='pt')
51
 
52
+ device = "cuda" if torch.cuda.is_available() else "cpu"
53
+ for key, item in inputs.items():
54
+ inputs[key] = inputs[key].to(device)
55
 
56
+ output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
57
+ print(processor.decode(output[0], skip_special_tokens=False))
58
+ ```