Files changed (3) hide show
  1. README.md +207 -122
  2. adapter_config.json +9 -4
  3. adapter_model.bin +1 -1
README.md CHANGED
@@ -1,134 +1,219 @@
1
  ---
2
- language:
3
- - en
4
- - sp
5
- - ja
6
- - pe
7
- - hi
8
- - fr
9
- - ch
10
- - be
11
- - gu
12
- - ge
13
- - te
14
- - it
15
- - ar
16
- - po
17
- - ta
18
- - ma
19
- - ma
20
- - or
21
- - pa
22
- - po
23
- - ur
24
- - ga
25
- - he
26
- - ko
27
- - ca
28
- - th
29
- - du
30
- - in
31
- - vi
32
- - bu
33
- - fi
34
- - ce
35
- - la
36
- - tu
37
- - ru
38
- - cr
39
- - sw
40
- - yo
41
- - ku
42
- - bu
43
- - ma
44
- - cz
45
- - fi
46
- - so
47
- - ta
48
- - sw
49
- - si
50
- - ka
51
- - zh
52
- - ig
53
- - xh
54
- - ro
55
- - ha
56
- - es
57
- - sl
58
- - li
59
- - gr
60
- - ne
61
- - as
62
- - no
63
-
64
- widget:
65
- - text: "Translate to German: My name is Arthur"
66
- example_title: "Translation"
67
- - text: "Please answer to the following question. Who is going to be the next Ballon d'or?"
68
- example_title: "Question Answering"
69
- - text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
70
- example_title: "Logical reasoning"
71
- - text: "Please answer the following question. What is the boiling point of Nitrogen?"
72
- example_title: "Scientific knowledge"
73
- - text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
74
- example_title: "Yes/no question"
75
- - text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
76
- example_title: "Reasoning task"
77
- - text: "Q: ( False or not False or False ) is? A: Let's think step by step"
78
- example_title: "Boolean Expressions"
79
- - text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
80
- example_title: "Math reasoning"
81
- - text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"
82
- example_title: "Premise and hypothesis"
83
-
84
- tags:
85
- - text2text-generation
86
-
87
- datasets:
88
- - svakulenk0/qrecc
89
- - taskmaster2
90
- - djaym7/wiki_dialog
91
- - deepmind/code_contests
92
- - lambada
93
- - gsm8k
94
- - aqua_rat
95
- - esnli
96
- - quasc
97
- - qed
98
- - financial_phrasebank
99
-
100
-
101
- license: apache-2.0
102
  ---
103
 
104
- # Model Card for LoRA-FLAN-T5 large
105
 
106
- ![model image](https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png)
107
 
108
- This repository contains the LoRA (Low Rank Adapters) of `flan-t5-large` that has been fine-tuned on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset.
109
 
110
- ## Usage
111
 
112
- Use this adapter with `peft` library
113
 
114
- ```python
115
- # pip install peft transformers
116
- import torch
117
- from peft import PeftModel, PeftConfig
118
- from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
119
 
120
- peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora"
121
- config = PeftConfig.from_pretrained(peft_model_id)
122
 
123
- model = AutoModelForSeq2SeqLM.from_pretrained(
124
- config.base_model_name_or_path,
125
- torch_dtype='auto',
126
- device_map='auto'
127
- )
128
- tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
129
 
130
- # Load the Lora model
131
- model = PeftModel.from_pretrained(model, peft_model_id)
132
- ```
133
 
134
- Enjoy!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ library_name: peft
3
+ base_model: google/flan-t5-large
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
 
 
 
19
 
20
+ - **Developed by:** [More Information Needed]
21
+ - **Shared by [optional]:** [More Information Needed]
22
+ - **Model type:** [More Information Needed]
23
+ - **Language(s) (NLP):** [More Information Needed]
24
+ - **License:** [More Information Needed]
25
+ - **Finetuned from model [optional]:** [More Information Needed]
26
+
27
+ ### Model Sources [optional]
28
+
29
+ <!-- Provide the basic links for the model. -->
30
+
31
+ - **Repository:** [More Information Needed]
32
+ - **Paper [optional]:** [More Information Needed]
33
+ - **Demo [optional]:** [More Information Needed]
34
+
35
+ ## Uses
36
+
37
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
38
+
39
+ ### Direct Use
40
+
41
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
42
+
43
+ [More Information Needed]
44
+
45
+ ### Downstream Use [optional]
46
+
47
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
48
+
49
+ [More Information Needed]
50
+
51
+ ### Out-of-Scope Use
52
+
53
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
54
+
55
+ [More Information Needed]
56
+
57
+ ## Bias, Risks, and Limitations
58
+
59
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
60
+
61
+ [More Information Needed]
62
+
63
+ ### Recommendations
64
+
65
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
66
+
67
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
68
+
69
+ ## How to Get Started with the Model
70
+
71
+ Use the code below to get started with the model.
72
+
73
+ [More Information Needed]
74
+
75
+ ## Training Details
76
+
77
+ ### Training Data
78
+
79
+ <!-- This should link to a Data 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. -->
80
+
81
+ [More Information Needed]
82
+
83
+ ### Training Procedure
84
+
85
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
86
+
87
+ #### Preprocessing [optional]
88
+
89
+ [More Information Needed]
90
+
91
+
92
+ #### Training Hyperparameters
93
+
94
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
95
+
96
+ #### Speeds, Sizes, Times [optional]
97
+
98
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
99
+
100
+ [More Information Needed]
101
+
102
+ ## Evaluation
103
+
104
+ <!-- This section describes the evaluation protocols and provides the results. -->
105
+
106
+ ### Testing Data, Factors & Metrics
107
+
108
+ #### Testing Data
109
+
110
+ <!-- This should link to a Data Card if possible. -->
111
+
112
+ [More Information Needed]
113
+
114
+ #### Factors
115
+
116
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
117
+
118
+ [More Information Needed]
119
+
120
+ #### Metrics
121
+
122
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
123
+
124
+ [More Information Needed]
125
+
126
+ ### Results
127
+
128
+ [More Information Needed]
129
+
130
+ #### Summary
131
+
132
+
133
+
134
+ ## Model Examination [optional]
135
+
136
+ <!-- Relevant interpretability work for the model goes here -->
137
+
138
+ [More Information Needed]
139
+
140
+ ## Environmental Impact
141
+
142
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
143
+
144
+ 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).
145
+
146
+ - **Hardware Type:** [More Information Needed]
147
+ - **Hours used:** [More Information Needed]
148
+ - **Cloud Provider:** [More Information Needed]
149
+ - **Compute Region:** [More Information Needed]
150
+ - **Carbon Emitted:** [More Information Needed]
151
+
152
+ ## Technical Specifications [optional]
153
+
154
+ ### Model Architecture and Objective
155
+
156
+ [More Information Needed]
157
+
158
+ ### Compute Infrastructure
159
+
160
+ [More Information Needed]
161
+
162
+ #### Hardware
163
+
164
+ [More Information Needed]
165
+
166
+ #### Software
167
+
168
+ [More Information Needed]
169
+
170
+ ## Citation [optional]
171
+
172
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
173
+
174
+ **BibTeX:**
175
+
176
+ [More Information Needed]
177
+
178
+ **APA:**
179
+
180
+ [More Information Needed]
181
+
182
+ ## Glossary [optional]
183
+
184
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
185
+
186
+ [More Information Needed]
187
+
188
+ ## More Information [optional]
189
+
190
+ [More Information Needed]
191
+
192
+ ## Model Card Authors [optional]
193
+
194
+ [More Information Needed]
195
+
196
+ ## Model Card Contact
197
+
198
+ [More Information Needed]
199
+
200
+
201
+ ## Training procedure
202
+
203
+
204
+ The following `bitsandbytes` quantization config was used during training:
205
+ - quant_method: bitsandbytes
206
+ - load_in_8bit: True
207
+ - load_in_4bit: False
208
+ - llm_int8_threshold: 6.0
209
+ - llm_int8_skip_modules: None
210
+ - llm_int8_enable_fp32_cpu_offload: False
211
+ - llm_int8_has_fp16_weight: False
212
+ - bnb_4bit_quant_type: fp4
213
+ - bnb_4bit_use_double_quant: False
214
+ - bnb_4bit_compute_dtype: float32
215
+
216
+ ### Framework versions
217
+
218
+
219
+ - PEFT 0.6.0.dev0
adapter_config.json CHANGED
@@ -1,18 +1,23 @@
1
  {
 
 
2
  "base_model_name_or_path": "google/flan-t5-large",
3
  "bias": "none",
4
- "enable_lora": null,
5
  "fan_in_fan_out": false,
6
  "inference_mode": true,
 
 
 
7
  "lora_alpha": 32,
8
  "lora_dropout": 0.05,
9
- "merge_weights": false,
10
  "modules_to_save": null,
11
  "peft_type": "LORA",
12
  "r": 16,
 
 
13
  "target_modules": [
14
- "q",
15
- "v"
16
  ],
17
  "task_type": "SEQ_2_SEQ_LM"
18
  }
 
1
  {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
  "base_model_name_or_path": "google/flan-t5-large",
5
  "bias": "none",
 
6
  "fan_in_fan_out": false,
7
  "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layers_pattern": null,
10
+ "layers_to_transform": null,
11
  "lora_alpha": 32,
12
  "lora_dropout": 0.05,
 
13
  "modules_to_save": null,
14
  "peft_type": "LORA",
15
  "r": 16,
16
+ "rank_pattern": {},
17
+ "revision": null,
18
  "target_modules": [
19
+ "v",
20
+ "q"
21
  ],
22
  "task_type": "SEQ_2_SEQ_LM"
23
  }
adapter_model.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:b120d54fc8ce458aa90768fa0e56d03b4061b606b51ec8b8be8c17906094a570
3
  size 18980429
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e2514d6d06c2e3c4ded9b367c0958a51d4c6a63efe95147a476548fe2f1c8bfc
3
  size 18980429