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.ipynb_checkpoints/README-checkpoint.md ADDED
@@ -0,0 +1,169 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: apache-2.0
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+ library_name: peft
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+ tags:
5
+ - generated_from_trainer
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+ base_model: alnrg2arg/blockchainlabs_7B_merged_test2_4
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+ model-index:
8
+ - name: qlora-out
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+ results: []
10
+ ---
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+
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/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)
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+ <details><summary>See axolotl config</summary>
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+
18
+ axolotl version: `0.3.0`
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+ ```yaml
20
+ base_model: alnrg2arg/blockchainlabs_7B_merged_test2_4
21
+ model_type: MistralForCausalLM
22
+ tokenizer_type: LlamaTokenizer
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+ is_mistral_derived_model: true
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+
25
+ load_in_8bit: false
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+ load_in_4bit: true
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+ strict: false
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+
29
+ datasets:
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+ - path: NeuralNovel/Neural-Story-v1
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+ type: completion
32
+ dataset_prepared_path: last_run_prepared
33
+ val_set_size: 0.1
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+ output_dir: ./qlora-out
35
+
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+ adapter: qlora
37
+ lora_model_dir:
38
+
39
+ sequence_len: 8192
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+ sample_packing: false
41
+ pad_to_sequence_len: true
42
+
43
+ lora_r: 32
44
+ lora_alpha: 16
45
+ lora_dropout: 0.05
46
+ lora_target_linear: true
47
+ lora_fan_in_fan_out:
48
+ lora_target_modules:
49
+ - gate_proj
50
+ - down_proj
51
+ - up_proj
52
+ - q_proj
53
+ - v_proj
54
+ - k_proj
55
+ - o_proj
56
+
57
+ wandb_project:
58
+ wandb_entity:
59
+ wandb_watch:
60
+ wandb_name:
61
+ wandb_log_model:
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+
63
+ gradient_accumulation_steps: 4
64
+ micro_batch_size: 2
65
+ num_epochs: 1
66
+ optimizer: adamw_bnb_8bit
67
+ lr_scheduler: cosine
68
+ learning_rate: 0.0002
69
+
70
+ train_on_inputs: false
71
+ group_by_length: false
72
+ bf16: true
73
+ fp16: false
74
+ tf32: false
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+
76
+ gradient_checkpointing: 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
+ loss_watchdog_threshold: 5.0
85
+ loss_watchdog_patience: 3
86
+
87
+ warmup_steps: 10
88
+ evals_per_epoch: 4
89
+ eval_table_size:
90
+ eval_table_max_new_tokens: 128
91
+ saves_per_epoch: 1
92
+ debug:
93
+ deepspeed:
94
+ weight_decay: 0.0
95
+ fsdp:
96
+ fsdp_config:
97
+ special_tokens:
98
+ bos_token: "<s>"
99
+ eos_token: "</s>"
100
+ unk_token: "<unk>"
101
+
102
+ ```
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+
104
+ </details><br>
105
+
106
+ # qlora-out
107
+
108
+ This model is a fine-tuned version of [alnrg2arg/blockchainlabs_7B_merged_test2_4](https://huggingface.co/alnrg2arg/blockchainlabs_7B_merged_test2_4) on the Neural-Story-v1.
109
+ It achieves the following results on the evaluation set:
110
+ - Loss: 2.1411
111
+
112
+ ## Model description
113
+
114
+ More information needed
115
+
116
+ ## Intended uses & limitations
117
+
118
+ More information needed
119
+
120
+ ## Training and evaluation data
121
+
122
+ More information needed
123
+
124
+ ## Training procedure
125
+
126
+
127
+ The following `bitsandbytes` quantization config was used during training:
128
+ - quant_method: bitsandbytes
129
+ - load_in_8bit: False
130
+ - load_in_4bit: True
131
+ - llm_int8_threshold: 6.0
132
+ - llm_int8_skip_modules: None
133
+ - llm_int8_enable_fp32_cpu_offload: False
134
+ - llm_int8_has_fp16_weight: False
135
+ - bnb_4bit_quant_type: nf4
136
+ - bnb_4bit_use_double_quant: True
137
+ - bnb_4bit_compute_dtype: bfloat16
138
+
139
+ ### Training hyperparameters
140
+
141
+ The following hyperparameters were used during training:
142
+ - learning_rate: 0.0002
143
+ - train_batch_size: 2
144
+ - eval_batch_size: 2
145
+ - seed: 42
146
+ - gradient_accumulation_steps: 4
147
+ - total_train_batch_size: 8
148
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
149
+ - lr_scheduler_type: cosine
150
+ - lr_scheduler_warmup_steps: 10
151
+ - num_epochs: 1
152
+
153
+ ### Training results
154
+
155
+ | Training Loss | Epoch | Step | Validation Loss |
156
+ |:-------------:|:-----:|:----:|:---------------:|
157
+ | 2.3251 | 0.06 | 1 | 2.8409 |
158
+ | 2.5318 | 0.25 | 4 | 2.7634 |
159
+ | 1.7316 | 0.51 | 8 | 2.3662 |
160
+ | 1.5196 | 0.76 | 12 | 2.1411 |
161
+
162
+
163
+ ### Framework versions
164
+
165
+ - PEFT 0.7.0
166
+ - Transformers 4.37.0.dev0
167
+ - Pytorch 2.0.1+cu117
168
+ - Datasets 2.16.1
169
+ - Tokenizers 0.15.0
README.md CHANGED
@@ -1,3 +1,169 @@
1
  ---
2
  license: apache-2.0
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
  license: apache-2.0
3
+ library_name: peft
4
+ tags:
5
+ - generated_from_trainer
6
+ base_model: alnrg2arg/blockchainlabs_7B_merged_test2_4
7
+ model-index:
8
+ - name: qlora-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/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)
16
+ <details><summary>See axolotl config</summary>
17
+
18
+ axolotl version: `0.3.0`
19
+ ```yaml
20
+ base_model: alnrg2arg/blockchainlabs_7B_merged_test2_4
21
+ model_type: MistralForCausalLM
22
+ tokenizer_type: LlamaTokenizer
23
+ is_mistral_derived_model: true
24
+
25
+ load_in_8bit: false
26
+ load_in_4bit: true
27
+ strict: false
28
+
29
+ datasets:
30
+ - path: NeuralNovel/Neural-Story-v1
31
+ type: completion
32
+ dataset_prepared_path: last_run_prepared
33
+ val_set_size: 0.1
34
+ output_dir: ./qlora-out
35
+
36
+ adapter: qlora
37
+ lora_model_dir:
38
+
39
+ sequence_len: 8192
40
+ sample_packing: false
41
+ pad_to_sequence_len: true
42
+
43
+ lora_r: 32
44
+ lora_alpha: 16
45
+ lora_dropout: 0.05
46
+ lora_target_linear: true
47
+ lora_fan_in_fan_out:
48
+ lora_target_modules:
49
+ - gate_proj
50
+ - down_proj
51
+ - up_proj
52
+ - q_proj
53
+ - v_proj
54
+ - k_proj
55
+ - o_proj
56
+
57
+ wandb_project:
58
+ wandb_entity:
59
+ wandb_watch:
60
+ wandb_name:
61
+ wandb_log_model:
62
+
63
+ gradient_accumulation_steps: 4
64
+ micro_batch_size: 2
65
+ num_epochs: 1
66
+ optimizer: adamw_bnb_8bit
67
+ lr_scheduler: cosine
68
+ learning_rate: 0.0002
69
+
70
+ train_on_inputs: false
71
+ group_by_length: false
72
+ bf16: true
73
+ fp16: false
74
+ tf32: false
75
+
76
+ gradient_checkpointing: 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
+ loss_watchdog_threshold: 5.0
85
+ loss_watchdog_patience: 3
86
+
87
+ warmup_steps: 10
88
+ evals_per_epoch: 4
89
+ eval_table_size:
90
+ eval_table_max_new_tokens: 128
91
+ saves_per_epoch: 1
92
+ debug:
93
+ deepspeed:
94
+ weight_decay: 0.0
95
+ fsdp:
96
+ fsdp_config:
97
+ special_tokens:
98
+ bos_token: "<s>"
99
+ eos_token: "</s>"
100
+ unk_token: "<unk>"
101
+
102
+ ```
103
+
104
+ </details><br>
105
+
106
+ # qlora-out
107
+
108
+ This model is a fine-tuned version of [alnrg2arg/blockchainlabs_7B_merged_test2_4](https://huggingface.co/alnrg2arg/blockchainlabs_7B_merged_test2_4) on the Neural-Story-v1.
109
+ It achieves the following results on the evaluation set:
110
+ - Loss: 2.1411
111
+
112
+ ## Model description
113
+
114
+ More information needed
115
+
116
+ ## Intended uses & limitations
117
+
118
+ More information needed
119
+
120
+ ## Training and evaluation data
121
+
122
+ More information needed
123
+
124
+ ## Training procedure
125
+
126
+
127
+ The following `bitsandbytes` quantization config was used during training:
128
+ - quant_method: bitsandbytes
129
+ - load_in_8bit: False
130
+ - load_in_4bit: True
131
+ - llm_int8_threshold: 6.0
132
+ - llm_int8_skip_modules: None
133
+ - llm_int8_enable_fp32_cpu_offload: False
134
+ - llm_int8_has_fp16_weight: False
135
+ - bnb_4bit_quant_type: nf4
136
+ - bnb_4bit_use_double_quant: True
137
+ - bnb_4bit_compute_dtype: bfloat16
138
+
139
+ ### Training hyperparameters
140
+
141
+ The following hyperparameters were used during training:
142
+ - learning_rate: 0.0002
143
+ - train_batch_size: 2
144
+ - eval_batch_size: 2
145
+ - seed: 42
146
+ - gradient_accumulation_steps: 4
147
+ - total_train_batch_size: 8
148
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
149
+ - lr_scheduler_type: cosine
150
+ - lr_scheduler_warmup_steps: 10
151
+ - num_epochs: 1
152
+
153
+ ### Training results
154
+
155
+ | Training Loss | Epoch | Step | Validation Loss |
156
+ |:-------------:|:-----:|:----:|:---------------:|
157
+ | 2.3251 | 0.06 | 1 | 2.8409 |
158
+ | 2.5318 | 0.25 | 4 | 2.7634 |
159
+ | 1.7316 | 0.51 | 8 | 2.3662 |
160
+ | 1.5196 | 0.76 | 12 | 2.1411 |
161
+
162
+
163
+ ### Framework versions
164
+
165
+ - PEFT 0.7.0
166
+ - Transformers 4.37.0.dev0
167
+ - Pytorch 2.0.1+cu117
168
+ - Datasets 2.16.1
169
+ - Tokenizers 0.15.0
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