See axolotl config

axolotl version: 0.4.1

adapter: lora
auto_find_batch_size: true
base_model: facebook/opt-125m
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 8e75bfdfe82368df_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/8e75bfdfe82368df_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
early_stopping_threshold: 0.001
eval_max_new_tokens: 128
eval_steps: 40
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/9a280c4f-a956-4f14-827e-3f17cf5c6bf4
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 100
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 32
mlflow_experiment_name: /tmp/8e75bfdfe82368df_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 50
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: null
sample_packing: false
save_steps: 40
saves_per_epoch: 0
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.02
wandb_entity: null
wandb_mode: online
wandb_name: 504b77d9-e9a3-4005-942d-11c5b37c5aa1
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 504b77d9-e9a3-4005-942d-11c5b37c5aa1
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null

9a280c4f-a956-4f14-827e-3f17cf5c6bf4

This model is a fine-tuned version of facebook/opt-125m on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1430

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0003
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 126
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0196	1	1.5935
No log	0.7843	40	0.5400
No log	1.5686	80	0.2514
1.5496	2.3529	120	0.1984
1.5496	3.1373	160	0.1715
0.3822	3.9216	200	0.1687
0.3822	4.7059	240	0.1424
0.3822	5.4902	280	0.1504
0.268	6.2745	320	0.1425
0.268	7.0588	360	0.1326
0.2038	7.8431	400	0.1440
0.2038	8.6275	440	0.1305
0.2038	9.4118	480	0.1417
0.163	10.1961	520	0.1537
0.163	10.9804	560	0.1430

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.3.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1