See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: fxmarty/tiny-llama-fast-tokenizer
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 3b22e525b2bdcb03_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/3b22e525b2bdcb03_train_data.json
  type:
    field_input: tools
    field_instruction: query
    field_output: answers
    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: true
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: dimasik87/2282480b-ef3b-4136-8e90-4dd3c99bfab5
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: 25
micro_batch_size: 1
mlflow_experiment_name: /tmp/3b22e525b2bdcb03_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
saves_per_epoch: 3
sequence_len: 2028
special_tokens:
  pad_token: </s>
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: 2282480b-ef3b-4136-8e90-4dd3c99bfab5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2282480b-ef3b-4136-8e90-4dd3c99bfab5
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

2282480b-ef3b-4136-8e90-4dd3c99bfab5

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: 10.3821

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 4
• 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: 25

Training results

Training Loss	Epoch	Step	Validation Loss
10.3888	0.0001	1	10.3863
10.3903	0.0002	3	10.3862
10.3935	0.0004	6	10.3859
10.3811	0.0006	9	10.3852
10.385	0.0009	12	10.3842
10.3825	0.0011	15	10.3833
10.385	0.0013	18	10.3826
10.3872	0.0015	21	10.3822
10.3874	0.0017	24	10.3821

Framework versions

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