JoaoMigSilva commited on
Commit
f218ca1
·
verified ·
1 Parent(s): 73a2550

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +3 -166
README.md CHANGED
@@ -4,178 +4,15 @@
4
  {}
5
  ---
6
 
7
- # Model Card for Model ID
8
 
9
  <!-- Provide a quick summary of what the model is/does. -->
10
 
11
  ArchitectLLM is a large language model fine-tuned on a custom dataset of texts in which architects discuss their designs. The model is designed to generate text that reflects architectural reasoning, design intentions, and spatial considerations in a manner similar to professional architects.
12
 
13
-
14
- ### Model Sources [optional]
15
-
16
- <!-- Provide the basic links for the model. -->
17
-
18
- ## Uses
19
-
20
- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
21
-
22
- ### Direct Use
23
-
24
- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
25
-
26
- [More Information Needed]
27
-
28
- ### Downstream Use [optional]
29
-
30
- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
31
-
32
- [More Information Needed]
33
-
34
- ### Out-of-Scope Use
35
-
36
- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
37
-
38
- [More Information Needed]
39
-
40
- ## Bias, Risks, and Limitations
41
-
42
- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
43
-
44
- [More Information Needed]
45
-
46
- ### Recommendations
47
-
48
- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
49
-
50
- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
51
-
52
- ## How to Get Started with the Model
53
-
54
- Use the code below to get started with the model.
55
-
56
- [More Information Needed]
57
-
58
  ## Training Details
 
59
 
60
  ### Training Data
 
61
 
62
- <!-- 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. -->
63
-
64
- [More Information Needed]
65
-
66
- ### Training Procedure
67
-
68
- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
69
-
70
- #### Preprocessing [optional]
71
-
72
- [More Information Needed]
73
-
74
-
75
- #### Training Hyperparameters
76
-
77
- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
78
-
79
- #### Speeds, Sizes, Times [optional]
80
-
81
- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
82
-
83
- [More Information Needed]
84
-
85
- ## Evaluation
86
-
87
- <!-- This section describes the evaluation protocols and provides the results. -->
88
-
89
- ### Testing Data, Factors & Metrics
90
-
91
- #### Testing Data
92
-
93
- <!-- This should link to a Dataset Card if possible. -->
94
-
95
- [More Information Needed]
96
-
97
- #### Factors
98
-
99
- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
100
-
101
- [More Information Needed]
102
-
103
- #### Metrics
104
-
105
- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
106
-
107
- [More Information Needed]
108
-
109
- ### Results
110
-
111
- [More Information Needed]
112
-
113
- #### Summary
114
-
115
-
116
-
117
- ## Model Examination [optional]
118
-
119
- <!-- Relevant interpretability work for the model goes here -->
120
-
121
- [More Information Needed]
122
-
123
- ## Environmental Impact
124
-
125
- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
126
-
127
- 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).
128
-
129
- - **Hardware Type:** [More Information Needed]
130
- - **Hours used:** [More Information Needed]
131
- - **Cloud Provider:** [More Information Needed]
132
- - **Compute Region:** [More Information Needed]
133
- - **Carbon Emitted:** [More Information Needed]
134
-
135
- ## Technical Specifications [optional]
136
-
137
- ### Model Architecture and Objective
138
-
139
- [More Information Needed]
140
-
141
- ### Compute Infrastructure
142
-
143
- [More Information Needed]
144
-
145
- #### Hardware
146
-
147
- [More Information Needed]
148
-
149
- #### Software
150
-
151
- [More Information Needed]
152
-
153
- ## Citation [optional]
154
-
155
- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
156
-
157
- **BibTeX:**
158
-
159
- [More Information Needed]
160
-
161
- **APA:**
162
-
163
- [More Information Needed]
164
-
165
- ## Glossary [optional]
166
-
167
- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
168
-
169
- [More Information Needed]
170
-
171
- ## More Information [optional]
172
-
173
- [More Information Needed]
174
-
175
- ## Model Card Authors [optional]
176
-
177
- [More Information Needed]
178
-
179
- ## Model Card Contact
180
-
181
- [More Information Needed]
 
4
  {}
5
  ---
6
 
7
+ # Model Card for ArchitectLL
8
 
9
  <!-- Provide a quick summary of what the model is/does. -->
10
 
11
  ArchitectLLM is a large language model fine-tuned on a custom dataset of texts in which architects discuss their designs. The model is designed to generate text that reflects architectural reasoning, design intentions, and spatial considerations in a manner similar to professional architects.
12
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
13
  ## Training Details
14
+ Finetuned Llama 2 7B.
15
 
16
  ### Training Data
17
+ Custom training dataset of architectural tests. Reach out for more details.
18