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.ipynb_checkpoints/README-checkpoint.md ADDED
@@ -0,0 +1,151 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ base_model: microsoft/phi-2
4
+ tags:
5
+ - generated_from_trainer
6
+ model-index:
7
+ - name: phi-sft-out
8
+ results: []
9
+ ---
10
+
11
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
+ should probably proofread and complete it, then remove this comment. -->
13
+
14
+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
15
+ <details><summary>See axolotl config</summary>
16
+
17
+ axolotl version: `0.4.0`
18
+ ```yaml
19
+ base_model: microsoft/phi-2
20
+ model_type: AutoModelForCausalLM
21
+ tokenizer_type: AutoTokenizer
22
+
23
+ load_in_8bit: false
24
+ load_in_4bit: false
25
+ strict: false
26
+
27
+ datasets:
28
+ - path: Intel/orca_dpo_pairs
29
+ type:
30
+ system_prompt: ""
31
+ field_system: system
32
+ field_instruction: question
33
+ field_output: rejected
34
+ field_output: chosen
35
+
36
+ dataset_prepared_path:
37
+ val_set_size: 0.05
38
+ output_dir: ./phi-sft-out
39
+
40
+ sequence_len: 2048
41
+ sample_packing: true
42
+ pad_to_sequence_len: true
43
+
44
+ adapter:
45
+ lora_model_dir:
46
+ lora_r:
47
+ lora_alpha:
48
+ lora_dropout:
49
+ lora_target_linear:
50
+ lora_fan_in_fan_out:
51
+
52
+ wandb_project:
53
+ wandb_entity:
54
+ wandb_watch:
55
+ wandb_name:
56
+ wandb_log_model:
57
+
58
+ gradient_accumulation_steps: 1
59
+ micro_batch_size: 2
60
+ num_epochs: 2
61
+ optimizer: adamw_torch
62
+ adam_beta2: 0.95
63
+ adam_epsilon: 0.00001
64
+ max_grad_norm: 1.0
65
+ lr_scheduler: cosine
66
+ learning_rate: 0.000003
67
+
68
+ train_on_inputs: false
69
+ group_by_length: false
70
+ bf16: auto
71
+ fp16:
72
+ tf32: true
73
+
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+ gradient_checkpointing: true
75
+ gradient_checkpointing_kwargs:
76
+ use_reentrant: True
77
+ early_stopping_patience:
78
+ resume_from_checkpoint:
79
+ local_rank:
80
+ logging_steps: 1
81
+ xformers_attention:
82
+ flash_attention: true
83
+
84
+ warmup_steps: 100
85
+ evals_per_epoch: 4
86
+ saves_per_epoch: 1
87
+ debug:
88
+ deepspeed:
89
+ weight_decay: 0.1
90
+ fsdp:
91
+ fsdp_config:
92
+ resize_token_embeddings_to_32x: true
93
+ special_tokens:
94
+ pad_token: "<|endoftext|>"
95
+
96
+ ```
97
+
98
+ </details><br>
99
+
100
+ # phi-sft-out
101
+
102
+ This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
103
+ It achieves the following results on the evaluation set:
104
+ - Loss: 1.2999
105
+
106
+ ## Model description
107
+
108
+ More information needed
109
+
110
+ ## Intended uses & limitations
111
+
112
+ More information needed
113
+
114
+ ## Training and evaluation data
115
+
116
+ More information needed
117
+
118
+ ## Training procedure
119
+
120
+ ### Training hyperparameters
121
+
122
+ The following hyperparameters were used during training:
123
+ - learning_rate: 3e-06
124
+ - train_batch_size: 2
125
+ - eval_batch_size: 2
126
+ - seed: 42
127
+ - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
128
+ - lr_scheduler_type: cosine
129
+ - lr_scheduler_warmup_steps: 100
130
+ - num_epochs: 2
131
+
132
+ ### Training results
133
+
134
+ | Training Loss | Epoch | Step | Validation Loss |
135
+ |:-------------:|:-----:|:----:|:---------------:|
136
+ | 1.3053 | 0.0 | 1 | 1.3288 |
137
+ | 1.2314 | 0.25 | 287 | 1.3183 |
138
+ | 1.1664 | 0.5 | 574 | 1.3090 |
139
+ | 1.4349 | 0.75 | 861 | 1.3034 |
140
+ | 1.4875 | 1.0 | 1148 | 1.3012 |
141
+ | 1.3461 | 1.23 | 1435 | 1.3006 |
142
+ | 1.3247 | 1.48 | 1722 | 1.2998 |
143
+ | 1.2906 | 1.73 | 2009 | 1.2999 |
144
+
145
+
146
+ ### Framework versions
147
+
148
+ - Transformers 4.37.0
149
+ - Pytorch 2.1.2+cu121
150
+ - Datasets 2.16.1
151
+ - Tokenizers 0.15.0
README.md CHANGED
@@ -1,3 +1,151 @@
1
  ---
2
- license: apache-2.0
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ license: mit
3
+ base_model: microsoft/phi-2
4
+ tags:
5
+ - generated_from_trainer
6
+ model-index:
7
+ - name: phi-sft-out
8
+ results: []
9
  ---
10
+
11
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
+ should probably proofread and complete it, then remove this comment. -->
13
+
14
+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
15
+ <details><summary>See axolotl config</summary>
16
+
17
+ axolotl version: `0.4.0`
18
+ ```yaml
19
+ base_model: microsoft/phi-2
20
+ model_type: AutoModelForCausalLM
21
+ tokenizer_type: AutoTokenizer
22
+
23
+ load_in_8bit: false
24
+ load_in_4bit: false
25
+ strict: false
26
+
27
+ datasets:
28
+ - path: Intel/orca_dpo_pairs
29
+ type:
30
+ system_prompt: ""
31
+ field_system: system
32
+ field_instruction: question
33
+ field_output: rejected
34
+ field_output: chosen
35
+
36
+ dataset_prepared_path:
37
+ val_set_size: 0.05
38
+ output_dir: ./phi-sft-out
39
+
40
+ sequence_len: 2048
41
+ sample_packing: true
42
+ pad_to_sequence_len: true
43
+
44
+ adapter:
45
+ lora_model_dir:
46
+ lora_r:
47
+ lora_alpha:
48
+ lora_dropout:
49
+ lora_target_linear:
50
+ lora_fan_in_fan_out:
51
+
52
+ wandb_project:
53
+ wandb_entity:
54
+ wandb_watch:
55
+ wandb_name:
56
+ wandb_log_model:
57
+
58
+ gradient_accumulation_steps: 1
59
+ micro_batch_size: 2
60
+ num_epochs: 2
61
+ optimizer: adamw_torch
62
+ adam_beta2: 0.95
63
+ adam_epsilon: 0.00001
64
+ max_grad_norm: 1.0
65
+ lr_scheduler: cosine
66
+ learning_rate: 0.000003
67
+
68
+ train_on_inputs: false
69
+ group_by_length: false
70
+ bf16: auto
71
+ fp16:
72
+ tf32: true
73
+
74
+ gradient_checkpointing: true
75
+ gradient_checkpointing_kwargs:
76
+ use_reentrant: True
77
+ early_stopping_patience:
78
+ resume_from_checkpoint:
79
+ local_rank:
80
+ logging_steps: 1
81
+ xformers_attention:
82
+ flash_attention: true
83
+
84
+ warmup_steps: 100
85
+ evals_per_epoch: 4
86
+ saves_per_epoch: 1
87
+ debug:
88
+ deepspeed:
89
+ weight_decay: 0.1
90
+ fsdp:
91
+ fsdp_config:
92
+ resize_token_embeddings_to_32x: true
93
+ special_tokens:
94
+ pad_token: "<|endoftext|>"
95
+
96
+ ```
97
+
98
+ </details><br>
99
+
100
+ # phi-sft-out
101
+
102
+ This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
103
+ It achieves the following results on the evaluation set:
104
+ - Loss: 1.2999
105
+
106
+ ## Model description
107
+
108
+ More information needed
109
+
110
+ ## Intended uses & limitations
111
+
112
+ More information needed
113
+
114
+ ## Training and evaluation data
115
+
116
+ More information needed
117
+
118
+ ## Training procedure
119
+
120
+ ### Training hyperparameters
121
+
122
+ The following hyperparameters were used during training:
123
+ - learning_rate: 3e-06
124
+ - train_batch_size: 2
125
+ - eval_batch_size: 2
126
+ - seed: 42
127
+ - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
128
+ - lr_scheduler_type: cosine
129
+ - lr_scheduler_warmup_steps: 100
130
+ - num_epochs: 2
131
+
132
+ ### Training results
133
+
134
+ | Training Loss | Epoch | Step | Validation Loss |
135
+ |:-------------:|:-----:|:----:|:---------------:|
136
+ | 1.3053 | 0.0 | 1 | 1.3288 |
137
+ | 1.2314 | 0.25 | 287 | 1.3183 |
138
+ | 1.1664 | 0.5 | 574 | 1.3090 |
139
+ | 1.4349 | 0.75 | 861 | 1.3034 |
140
+ | 1.4875 | 1.0 | 1148 | 1.3012 |
141
+ | 1.3461 | 1.23 | 1435 | 1.3006 |
142
+ | 1.3247 | 1.48 | 1722 | 1.2998 |
143
+ | 1.2906 | 1.73 | 2009 | 1.2999 |
144
+
145
+
146
+ ### Framework versions
147
+
148
+ - Transformers 4.37.0
149
+ - Pytorch 2.1.2+cu121
150
+ - Datasets 2.16.1
151
+ - Tokenizers 0.15.0
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+ "vocab_size": 51200
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+ }
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