See axolotl config

axolotl version: 0.4.1

\base_model: NousResearch/Meta-Llama-3-8B-Instruct
adapter: lora
base_model: fxmarty/tiny-llama-fast-tokenizer
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - a2d801a38e640d38_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/a2d801a38e640d38_train_data.json
  type:
    field_input: series
    field_instruction: description
    field_output: dreams
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: mamung/36c09471-e4b7-495c-aaea-aa4768c8ed14
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.00015
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 5
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/a2d801a38e640d38_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 2.0e-05
optimizer: adamw_torch
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: 2048
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: eddysang
wandb_mode: online
wandb_name: 070ae428-1955-4fe6-ae86-b3b59af05b6f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 070ae428-1955-4fe6-ae86-b3b59af05b6f
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

36c09471-e4b7-495c-aaea-aa4768c8ed14

This model is a fine-tuned version of fxmarty/tiny-llama-fast-tokenizer on the None dataset. It achieves the following results on the evaluation set:

• Loss: 10.3464

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: 0.00015
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=2e-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 20
• training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0016	1	10.3766
10.377	0.0144	9	10.3757
10.3746	0.0288	18	10.3723
10.3684	0.0432	27	10.3620
10.3545	0.0576	36	10.3512
10.3486	0.072	45	10.3480
10.3465	0.0864	54	10.3471
10.3468	0.1008	63	10.3468
10.3467	0.1152	72	10.3466
10.3471	0.1296	81	10.3465
10.3451	0.144	90	10.3464
10.3463	0.1584	99	10.3464

Framework versions

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