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0.4.11adapter: qlora
2auto_resume_from_checkpoints: true
3base_model: fxmarty/really-tiny-falcon-testing
4bf16: auto
5chat_template: llama3
6dataset_prepared_path: null
7dataset_processes: 6
8datasets:
9- data_files:
10 - e87224c8eb065bc2_train_data.json
11 ds_type: json
12 format: custom
13 path: /workspace/input_data/e87224c8eb065bc2_train_data.json
14 type:
15 field_input: intent
16 field_instruction: instruction
17 field_output: response
18 format: '{instruction} {input}'
19 no_input_format: '{instruction}'
20 system_format: '{system}'
21 system_prompt: ''
22debug: null
23deepspeed: null
24early_stopping_patience: 3
25eval_max_new_tokens: 128
26eval_steps: 2000
27eval_table_size: null
28evals_per_epoch: null
29flash_attention: false
30fp16: false
31fsdp: null
32fsdp_config: null
33gradient_accumulation_steps: 1
34gradient_checkpointing: false
35group_by_length: false
36hub_model_id: error577/f4e4984a-1046-4a12-80b7-8c5f44f7a124
37hub_repo: null
38hub_strategy: checkpoint
39hub_token: null
40learning_rate: 0.0002
41load_in_4bit: true
42load_in_8bit: false
43local_rank: null
44logging_steps: 1
45lora_alpha: 16
46lora_dropout: 0.1
47lora_fan_in_fan_out: null
48lora_model_dir: null
49lora_r: 8
50lora_target_linear: true
51lr_scheduler: cosine
52max_grad_norm: 1.0
53max_steps: null
54micro_batch_size: 8
55mlflow_experiment_name: /tmp/e87224c8eb065bc2_train_data.json
56model_type: AutoModelForCausalLM
57num_epochs: 3
58optimizer: adamw_bnb_8bit
59output_dir: miner_id_24
60pad_to_sequence_len: true
61resume_from_checkpoint: null
62s2_attention: null
63sample_packing: false
64save_steps: 2000
65sequence_len: 512
66strict: false
67tf32: false
68tokenizer_type: AutoTokenizer
69train_on_inputs: false
70trust_remote_code: true
71val_set_size: 0.005
72wandb_entity: null
73wandb_mode: online
74wandb_name: 1cd1f24c-cd92-4120-99af-dc7e5e1a2626
75wandb_project: Gradients-On-Demand
76wandb_run: your_name
77wandb_runid: 1cd1f24c-cd92-4120-99af-dc7e5e1a2626
78warmup_steps: 30
79weight_decay: 0.0
80xformers_attention: null
81| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 11.0862 | 0.0000 | 1 | 11.0843 |
| 10.9987 | 0.0538 | 2000 | 10.9976 |
| 10.9932 | 0.1076 | 4000 | 10.9906 |
| 10.9885 | 0.1614 | 6000 | 10.9861 |
| 10.995 | 0.2152 | 8000 | 10.9831 |
| 10.9707 | 0.2690 | 10000 | 10.9803 |
| 10.9651 | 0.3228 | 12000 | 10.9776 |
| 10.9695 | 0.3766 | 14000 | 10.9759 |
| 10.9875 | 0.4304 | 16000 | 10.9745 |
| 10.9761 | 0.4842 | 18000 | 10.9730 |
| 10.966 | 0.5380 | 20000 | 10.9719 |
| 10.9683 | 0.5918 | 22000 | 10.9714 |
| 10.9792 | 0.6456 | 24000 | 10.9708 |
| 10.9706 | 0.6994 | 26000 | 10.9704 |
| 10.9697 | 0.7532 | 28000 | 10.9696 |
| 10.9873 | 0.8070 | 30000 | 10.9694 |
| 10.97 | 0.8608 | 32000 | 10.9688 |
| 10.9856 | 0.9146 | 34000 | 10.9686 |
| 10.9675 | 0.9684 | 36000 | 10.9682 |
| 10.9662 | 1.0222 | 38000 | 10.9679 |
| 10.9676 | 1.0760 | 40000 | 10.9676 |
| 10.9814 | 1.1298 | 42000 | 10.9676 |
| 10.9753 | 1.1836 | 44000 | 10.9673 |
| 10.971 | 1.2374 | 46000 | 10.9671 |
| 10.9726 | 1.2912 | 48000 | 10.9669 |
| 10.9625 | 1.3450 | 50000 | 10.9668 |
| 10.964 | 1.3988 | 52000 | 10.9665 |
| 10.973 | 1.4526 | 54000 | 10.9663 |
| 10.9662 | 1.5063 | 56000 | 10.9661 |
| 10.9552 | 1.5601 | 58000 | 10.9661 |
| 10.9673 | 1.6139 | 60000 | 10.9659 |
| 10.9705 | 1.6677 | 62000 | 10.9657 |
| 10.9763 | 1.7215 | 64000 | 10.9658 |
| 10.9759 | 1.7753 | 66000 | 10.9655 |
| 10.9692 | 1.8291 | 68000 | 10.9655 |
| 10.9806 | 1.8829 | 70000 | 10.9655 |
| 10.9717 | 1.9367 | 72000 | 10.9652 |
| 10.9796 | 1.9905 | 74000 | 10.9652 |
| 10.9789 | 2.0443 | 76000 | 10.9651 |
| 10.979 | 2.0981 | 78000 | 10.9651 |
| 10.9706 | 2.1519 | 80000 | 10.9650 |
| 10.9829 | 2.2057 | 82000 | 10.9649 |
| 10.9865 | 2.2595 | 84000 | 10.9649 |
| 10.9722 | 2.3133 | 86000 | 10.9648 |
| 10.975 | 2.3671 | 88000 | 10.9648 |
| 10.9728 | 2.4209 | 90000 | 10.9647 |
| 10.9681 | 2.4747 | 92000 | 10.9647 |
| 10.9694 | 2.5285 | 94000 | 10.9647 |
| 10.9733 | 2.5823 | 96000 | 10.9646 |
| 10.9622 | 2.6361 | 98000 | 10.9647 |
| 10.9594 | 2.6899 | 100000 | 10.9646 |
| 10.9764 | 2.7437 | 102000 | 10.9646 |
| 10.9628 | 2.7975 | 104000 | 10.9646 |
| 10.9667 | 2.8513 | 106000 | 10.9646 |