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axolotl version: 0.4.1

adapter: lora
base_model: aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 9c074260b7c494a6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/9c074260b7c494a6_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
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 5
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 6
gradient_checkpointing: true
group_by_length: false
hub_model_id: dimasik1987/8daaa4ae-a55a-41cc-b2bd-80d36516e1dd
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: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 50
micro_batch_size: 4
mlflow_experiment_name: /tmp/9c074260b7c494a6_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 4056
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_dtype: bfloat16
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8daaa4ae-a55a-41cc-b2bd-80d36516e1dd
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8daaa4ae-a55a-41cc-b2bd-80d36516e1dd
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

8daaa4ae-a55a-41cc-b2bd-80d36516e1dd

This model is a fine-tuned version of aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct on the None dataset. It achieves the following results on the evaluation set:

  • Loss: nan

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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 6
  • total_train_batch_size: 24
  • optimizer: Use OptimizerNames.ADAMW_TORCH 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: 50

Training results

Training Loss Epoch Step Validation Loss
0.0 0.0005 1 nan
0.0 0.0020 4 nan
0.0 0.0040 8 nan
0.0 0.0059 12 nan
0.0 0.0079 16 nan
0.0 0.0099 20 nan
0.0 0.0119 24 nan
0.0 0.0139 28 nan
0.0 0.0159 32 nan
0.0 0.0178 36 nan
0.0 0.0198 40 nan
0.0 0.0218 44 nan
0.0 0.0238 48 nan

Framework versions

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