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9051d570-72f4-49e5-aa82-1537c64c08d3

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9051d570-72f4-49e5-aa82-1537c64c08d3 / README.md
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metadata
library_name: peft
license: mit
base_model: numind/NuExtract-v1.5
tags:
  - axolotl
  - generated_from_trainer
model-index:
  - name: 9051d570-72f4-49e5-aa82-1537c64c08d3
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: numind/NuExtract-v1.5
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - dab0833997ddbe5b_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/dab0833997ddbe5b_train_data.json
  type:
    field_instruction: prompt
    field_output: solution
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hub_model_id: dimasik2987/9051d570-72f4-49e5-aa82-1537c64c08d3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001004
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/G.O.D/dab0833997ddbe5b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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
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: 044dedb9-1fa6-4236-ae22-1fa7f561e7a9
wandb_project: cold6
wandb_run: your_name
wandb_runid: 044dedb9-1fa6-4236-ae22-1fa7f561e7a9
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

9051d570-72f4-49e5-aa82-1537c64c08d3

This model is a fine-tuned version of numind/NuExtract-v1.5 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5751

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: 0.0001004
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 10
  • training_steps: 200

Training results

Training Loss Epoch Step Validation Loss
0.6942 0.0018 1 0.9684
0.4115 0.0877 50 0.5967
0.5672 0.1754 100 0.5871
0.4335 0.2632 150 0.5781
0.4614 0.3509 200 0.5751

Framework versions

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