See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 66e1f5c3a87b9009_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/66e1f5c3a87b9009_train_data.json
  type:
    field_instruction: file_content
    field_output: extracted_data
    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: true
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: dimasik87/38c537af-903d-4208-9186-707aa28f3a5b
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: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/66e1f5c3a87b9009_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: 5
save_strategy: steps
sequence_len: 2028
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: 38c537af-903d-4208-9186-707aa28f3a5b
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 38c537af-903d-4208-9186-707aa28f3a5b
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

38c537af-903d-4208-9186-707aa28f3a5b

This model is a fine-tuned version of unsloth/Meta-Llama-3.1-8B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0142

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: 50

Training results

Training Loss	Epoch	Step	Validation Loss
0.1534	0.0044	1	0.1782
0.1692	0.0219	5	0.1293
0.0406	0.0438	10	0.0294
0.0064	0.0657	15	0.0184
0.0248	0.0876	20	0.0178
0.0087	0.1095	25	0.0161
0.0121	0.1314	30	0.0160
0.0049	0.1533	35	0.0151
0.0124	0.1752	40	0.0145
0.0076	0.1972	45	0.0142
0.0124	0.2191	50	0.0142

Framework versions

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