adapter: qlora
base_model: OpenBuddy/openbuddy-llama2-13b-v8.1-fp16
bf16: auto
chat_template: llama3
dataloader_num_workers: 6
dataset_prepared_path: null
datasets:
- data_files:
  - 258997f5c768a462_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/258997f5c768a462_train_data.json
  type:
    field_instruction: abstractText
    field_output: meshMajor
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: true
hub_model_id: error577/b3b5f3c6-cda5-4eaf-958b-be1f5e6069f7
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.3
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: 300
micro_batch_size: 1
mlflow_experiment_name: /tmp/258997f5c768a462_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 50
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
sequence_len: 256
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.005
wandb_entity: null
wandb_mode: online
wandb_name: 6d543dd5-ac1c-4d90-9cb2-8669fccd9d2c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6d543dd5-ac1c-4d90-9cb2-8669fccd9d2c
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

b3b5f3c6-cda5-4eaf-958b-be1f5e6069f7

This model is a fine-tuned version of OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4757

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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
training_steps: 300

Training results

Training Loss	Epoch	Step	Validation Loss
0.9502	0.0003	1	1.3877
0.6039	0.0161	50	0.5690
0.5687	0.0323	100	0.5499
0.5451	0.0484	150	0.5127
0.6351	0.0646	200	0.4887
0.6928	0.0807	250	0.4805
0.6263	0.0969	300	0.4757

Framework versions

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