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prxy5604/
1d6e49ab-72d8-438d-b528-f836692c9169

Name: 1d6e49ab-72d8-438d-b528-f836692c9169
Availability: InStock

generated_from_trainer

katuni4ka/tiny-random-olmo-hf

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: katuni4ka/tiny-random-olmo-hf
bf16: true
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 2ea5b3d3cd852c55_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/2ea5b3d3cd852c55_train_data.json
  type:
    field_instruction: input
    field_output: reference_answer
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 16
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: prxy5604/1d6e49ab-72d8-438d-b528-f836692c9169
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 64
lora_dropout: 0.2
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 750
micro_batch_size: 16
mlflow_experiment_name: /tmp/2ea5b3d3cd852c55_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 8
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1e-8
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 150
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 091b707d-1c57-490d-9a44-42caaec1af5f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 091b707d-1c57-490d-9a44-42caaec1af5f
warmup_steps: 100
weight_decay: 0.01
xformers_attention: null

1d6e49ab-72d8-438d-b528-f836692c9169

This model is a fine-tuned version of katuni4ka/tiny-random-olmo-hf on the None dataset. It achieves the following results on the evaluation set:

Loss: 10.7077

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: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 750

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0004	1	10.8318
10.7458	0.0669	150	10.7363
10.7343	0.1339	300	10.7212
10.7245	0.2008	450	10.7133
10.7273	0.2678	600	10.7085
10.7176	0.3347	750	10.7077

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1