See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: HuggingFaceM4/tiny-random-LlamaForCausalLM
bf16: true
chat_template: llama3
datasets:
- data_files:
  - 86c8aba6e3180404_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/86c8aba6e3180404_train_data.json
  type:
    field_input: option1
    field_instruction: sentence
    field_output: definition
    format: '{instruction} {input}'
    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: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: lesso01/e5e8bc2a-ecd5-4c49-8a28-c4fb3c511b06
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: 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: 80GiB
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/86c8aba6e3180404_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: 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: e5e8bc2a-ecd5-4c49-8a28-c4fb3c511b06
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e5e8bc2a-ecd5-4c49-8a28-c4fb3c511b06
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false

e5e8bc2a-ecd5-4c49-8a28-c4fb3c511b06

This model is a fine-tuned version of HuggingFaceM4/tiny-random-LlamaForCausalLM on the None dataset. It achieves the following results on the evaluation set:

• Loss: 10.3560

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• 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: 100

Training results

Training Loss	Epoch	Step	Validation Loss
10.3611	0.0074	1	10.3640
10.3609	0.0664	9	10.3636
10.3655	0.1328	18	10.3624
10.3698	0.1993	27	10.3612
10.3556	0.2657	36	10.3601
10.3614	0.3321	45	10.3590
10.3564	0.3985	54	10.3580
10.3531	0.4649	63	10.3571
10.3611	0.5314	72	10.3565
10.3598	0.5978	81	10.3562
10.3604	0.6642	90	10.3560
10.3548	0.7306	99	10.3560

Framework versions

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