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dimasik1987
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9de0dce6-b524-4295-808d-779fc72463bc

PEFT
Safetensors
phi3
axolotl
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custom_code
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9de0dce6-b524-4295-808d-779fc72463bc / 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: 9de0dce6-b524-4295-808d-779fc72463bc
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: numind/NuExtract-v1.5
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 232e986344baa6bf_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/232e986344baa6bf_train_data.json
  type:
    field_input: choices
    field_instruction: full_prompt
    field_output: example
    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: 4
flash_attention: false
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: dimasik1987/9de0dce6-b524-4295-808d-779fc72463bc
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 78GiB
max_steps: 30
micro_batch_size: 2
mlflow_experiment_name: /tmp/232e986344baa6bf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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: 10
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 924d5d9c-d1f3-4e2d-a5aa-ab8e009d8956
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 924d5d9c-d1f3-4e2d-a5aa-ab8e009d8956
warmup_steps: 10
weight_decay: 0.01
xformers_attention: true

9de0dce6-b524-4295-808d-779fc72463bc

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: 0.0000

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: 0.0002
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • 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: 30

Training results

Training Loss Epoch Step Validation Loss
No log 0.0240 1 0.5040
1.7005 0.1916 8 0.0248
0.0011 0.3832 16 0.0001
0.0001 0.5749 24 0.0000

Framework versions

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