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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.ipynb_checkpoints/README-checkpoint.md ADDED
@@ -0,0 +1,158 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ license: apache-2.0
4
+ base_model: Qwen/Qwen2.5-14B-Instruct
5
+ tags:
6
+ - generated_from_trainer
7
+ model-index:
8
+ - name: outputs/lora-out
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
16
+ <details><summary>See axolotl config</summary>
17
+
18
+ axolotl version: `0.5.0`
19
+ ```yaml
20
+ base_model: Qwen/Qwen2.5-14B-Instruct
21
+
22
+ load_in_8bit: false
23
+ load_in_4bit: false
24
+ strict: false
25
+
26
+ datasets:
27
+ - path: output.jsonl
28
+ type: alpaca
29
+
30
+ special_tokens:
31
+ bos_token:
32
+ eos_token: "<|im_end|>"
33
+ pad_token: "<|endoftext|>"
34
+
35
+ dataset_prepared_path:
36
+ val_set_size: 0.05
37
+ output_dir: ./outputs/lora-out
38
+
39
+ sequence_len: 4096
40
+ sample_packing: false
41
+ pad_to_sequence_len: true
42
+
43
+ adapter: lora
44
+ lora_model_dir:
45
+ lora_r: 8
46
+ lora_alpha: 16
47
+ lora_dropout: 0.05
48
+ lora_target_linear: true
49
+ lora_fan_in_fan_out:
50
+ lora_target_modules:
51
+ - gate_proj
52
+ - down_proj
53
+ - up_proj
54
+ - q_proj
55
+ - v_proj
56
+ - k_proj
57
+ - o_proj
58
+
59
+ wandb_project: axolotl_gmatrix
60
+ wandb_entity: mssong
61
+ wandb_watch:
62
+ wandb_run_id:
63
+ wandb_log_model:
64
+
65
+ gradient_accumulation_steps: 2
66
+ micro_batch_size: 1
67
+ num_epochs: 3
68
+ optimizer:
69
+ lr_scheduler: cosine
70
+ learning_rate: 0.00006
71
+ train_on_inputs:
72
+ group_by_length: false
73
+ bf16: true
74
+ fp16: false
75
+ tf32: false
76
+ gradient_checkpointing: true
77
+ early_stopping_patience: 4
78
+ local_rank:
79
+ logging_steps: 100
80
+ xformers_attention:
81
+ flash_attention: true
82
+ warmup_ratio: 0.05
83
+ #warmup_steps: 100
84
+ eval_steps: 100
85
+ save_steps: 100
86
+ save_total_limit: 2
87
+ eval_sample_packing:
88
+ debug:
89
+ deepspeed:
90
+ weight_decay: 0.01
91
+ fsdp:
92
+ fsdp_config:
93
+ trust_remote_code: true
94
+ ```
95
+
96
+ </details><br>
97
+
98
+ # outputs/lora-out
99
+
100
+ This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the None dataset.
101
+ It achieves the following results on the evaluation set:
102
+ - Loss: 0.0875
103
+
104
+ ## Model description
105
+
106
+ More information needed
107
+
108
+ ## Intended uses & limitations
109
+
110
+ More information needed
111
+
112
+ ## Training and evaluation data
113
+
114
+ More information needed
115
+
116
+ ## Training procedure
117
+
118
+ ### Training hyperparameters
119
+
120
+ The following hyperparameters were used during training:
121
+ - learning_rate: 6e-05
122
+ - train_batch_size: 1
123
+ - eval_batch_size: 1
124
+ - seed: 42
125
+ - gradient_accumulation_steps: 2
126
+ - total_train_batch_size: 2
127
+ - optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
128
+ - lr_scheduler_type: cosine
129
+ - lr_scheduler_warmup_steps: 162
130
+ - num_epochs: 3
131
+
132
+ ### Training results
133
+
134
+ | Training Loss | Epoch | Step | Validation Loss |
135
+ |:-------------:|:------:|:----:|:---------------:|
136
+ | No log | 0.0009 | 1 | 0.4243 |
137
+ | 0.2465 | 0.0923 | 100 | 0.1338 |
138
+ | 0.0425 | 0.1847 | 200 | 0.1110 |
139
+ | 0.0333 | 0.2770 | 300 | 0.1051 |
140
+ | 0.0319 | 0.3693 | 400 | 0.0933 |
141
+ | 0.0257 | 0.4617 | 500 | 0.0886 |
142
+ | 0.0245 | 0.5540 | 600 | 0.0898 |
143
+ | 0.0262 | 0.6464 | 700 | 0.0889 |
144
+ | 0.025 | 0.7387 | 800 | 0.0827 |
145
+ | 0.0221 | 0.8310 | 900 | 0.0813 |
146
+ | 0.0207 | 0.9234 | 1000 | 0.0901 |
147
+ | 0.0219 | 1.0157 | 1100 | 0.0878 |
148
+ | 0.0132 | 1.1080 | 1200 | 0.0890 |
149
+ | 0.0154 | 1.2004 | 1300 | 0.0875 |
150
+
151
+
152
+ ### Framework versions
153
+
154
+ - PEFT 0.13.2
155
+ - Transformers 4.46.1
156
+ - Pytorch 2.3.1+cu121
157
+ - Datasets 3.0.1
158
+ - Tokenizers 0.20.3
README.md ADDED
@@ -0,0 +1,158 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ license: apache-2.0
4
+ base_model: Qwen/Qwen2.5-14B-Instruct
5
+ tags:
6
+ - generated_from_trainer
7
+ model-index:
8
+ - name: outputs/lora-out
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
16
+ <details><summary>See axolotl config</summary>
17
+
18
+ axolotl version: `0.5.0`
19
+ ```yaml
20
+ base_model: Qwen/Qwen2.5-14B-Instruct
21
+
22
+ load_in_8bit: false
23
+ load_in_4bit: false
24
+ strict: false
25
+
26
+ datasets:
27
+ - path: output.jsonl
28
+ type: alpaca
29
+
30
+ special_tokens:
31
+ bos_token:
32
+ eos_token: "<|im_end|>"
33
+ pad_token: "<|endoftext|>"
34
+
35
+ dataset_prepared_path:
36
+ val_set_size: 0.05
37
+ output_dir: ./outputs/lora-out
38
+
39
+ sequence_len: 4096
40
+ sample_packing: false
41
+ pad_to_sequence_len: true
42
+
43
+ adapter: lora
44
+ lora_model_dir:
45
+ lora_r: 8
46
+ lora_alpha: 16
47
+ lora_dropout: 0.05
48
+ lora_target_linear: true
49
+ lora_fan_in_fan_out:
50
+ lora_target_modules:
51
+ - gate_proj
52
+ - down_proj
53
+ - up_proj
54
+ - q_proj
55
+ - v_proj
56
+ - k_proj
57
+ - o_proj
58
+
59
+ wandb_project: axolotl_gmatrix
60
+ wandb_entity: mssong
61
+ wandb_watch:
62
+ wandb_run_id:
63
+ wandb_log_model:
64
+
65
+ gradient_accumulation_steps: 2
66
+ micro_batch_size: 1
67
+ num_epochs: 3
68
+ optimizer:
69
+ lr_scheduler: cosine
70
+ learning_rate: 0.00006
71
+ train_on_inputs:
72
+ group_by_length: false
73
+ bf16: true
74
+ fp16: false
75
+ tf32: false
76
+ gradient_checkpointing: true
77
+ early_stopping_patience: 4
78
+ local_rank:
79
+ logging_steps: 100
80
+ xformers_attention:
81
+ flash_attention: true
82
+ warmup_ratio: 0.05
83
+ #warmup_steps: 100
84
+ eval_steps: 100
85
+ save_steps: 100
86
+ save_total_limit: 2
87
+ eval_sample_packing:
88
+ debug:
89
+ deepspeed:
90
+ weight_decay: 0.01
91
+ fsdp:
92
+ fsdp_config:
93
+ trust_remote_code: true
94
+ ```
95
+
96
+ </details><br>
97
+
98
+ # outputs/lora-out
99
+
100
+ This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the None dataset.
101
+ It achieves the following results on the evaluation set:
102
+ - Loss: 0.0875
103
+
104
+ ## Model description
105
+
106
+ More information needed
107
+
108
+ ## Intended uses & limitations
109
+
110
+ More information needed
111
+
112
+ ## Training and evaluation data
113
+
114
+ More information needed
115
+
116
+ ## Training procedure
117
+
118
+ ### Training hyperparameters
119
+
120
+ The following hyperparameters were used during training:
121
+ - learning_rate: 6e-05
122
+ - train_batch_size: 1
123
+ - eval_batch_size: 1
124
+ - seed: 42
125
+ - gradient_accumulation_steps: 2
126
+ - total_train_batch_size: 2
127
+ - optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
128
+ - lr_scheduler_type: cosine
129
+ - lr_scheduler_warmup_steps: 162
130
+ - num_epochs: 3
131
+
132
+ ### Training results
133
+
134
+ | Training Loss | Epoch | Step | Validation Loss |
135
+ |:-------------:|:------:|:----:|:---------------:|
136
+ | No log | 0.0009 | 1 | 0.4243 |
137
+ | 0.2465 | 0.0923 | 100 | 0.1338 |
138
+ | 0.0425 | 0.1847 | 200 | 0.1110 |
139
+ | 0.0333 | 0.2770 | 300 | 0.1051 |
140
+ | 0.0319 | 0.3693 | 400 | 0.0933 |
141
+ | 0.0257 | 0.4617 | 500 | 0.0886 |
142
+ | 0.0245 | 0.5540 | 600 | 0.0898 |
143
+ | 0.0262 | 0.6464 | 700 | 0.0889 |
144
+ | 0.025 | 0.7387 | 800 | 0.0827 |
145
+ | 0.0221 | 0.8310 | 900 | 0.0813 |
146
+ | 0.0207 | 0.9234 | 1000 | 0.0901 |
147
+ | 0.0219 | 1.0157 | 1100 | 0.0878 |
148
+ | 0.0132 | 1.1080 | 1200 | 0.0890 |
149
+ | 0.0154 | 1.2004 | 1300 | 0.0875 |
150
+
151
+
152
+ ### Framework versions
153
+
154
+ - PEFT 0.13.2
155
+ - Transformers 4.46.1
156
+ - Pytorch 2.3.1+cu121
157
+ - Datasets 3.0.1
158
+ - Tokenizers 0.20.3
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