See axolotl config

axolotl version: 0.6.0

adapter: lora
base_model: numind/NuExtract-v1.5
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - b4f0c40bccb7a455_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/b4f0c40bccb7a455_train_data.json
  type:
    field_instruction: prompt
    field_output: response
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: true
hub_model_id: Rodo-Sami/8ea5efad-f48a-417f-9324-cdc474b7c038
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: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1500
micro_batch_size: 2
mlflow_experiment_name: /tmp/b4f0c40bccb7a455_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
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: offline
wandb_name: bb8e283f-cc31-4e3a-b4c0-0d67dcaea216
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: bb8e283f-cc31-4e3a-b4c0-0d67dcaea216
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

8ea5efad-f48a-417f-9324-cdc474b7c038

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: 1.0783

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 adamw_bnb_8bit 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: 477

Training results

Training Loss	Epoch	Step	Validation Loss
1.274	0.2516	120	1.0973
0.9612	0.5031	240	1.0751
1.0177	0.7547	360	1.0783

Framework versions

• PEFT 0.14.0
• Transformers 4.46.3
• Pytorch 2.5.1+cu124
• Datasets 3.1.0
• Tokenizers 0.20.3