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abaddon182/
b2aa1a11-96d3-4e22-8876-0b504590d7f8

Name: b2aa1a11-96d3-4e22-8876-0b504590d7f8
Availability: InStock

generated_from_trainer

fxmarty/tiny-random-GemmaForCausalLM

adapter

mit

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: fxmarty/tiny-random-GemmaForCausalLM
bf16: true
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 989e1c74c4e7931f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/989e1c74c4e7931f_train_data.json
  type:
    field_instruction: topic
    field_output: argument
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 8
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: abaddon182/b2aa1a11-96d3-4e22-8876-0b504590d7f8
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: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1500
micro_batch_size: 8
mlflow_experiment_name: /tmp/989e1c74c4e7931f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1e-8
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: 150
saves_per_epoch: null
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: online
wandb_name: 6c0ef854-2452-4a33-98fa-224ba969bea5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6c0ef854-2452-4a33-98fa-224ba969bea5
warmup_steps: 50
weight_decay: 0.1
xformers_attention: null

b2aa1a11-96d3-4e22-8876-0b504590d7f8

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

Loss: 12.3837

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=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
training_steps: 1500

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0006	1	12.4561
12.3976	0.0837	150	12.3978
12.3874	0.1673	300	12.3854
12.3853	0.2510	450	12.3845
12.3842	0.3346	600	12.3841
12.3852	0.4183	750	12.3840
12.3848	0.5020	900	12.3838
12.383	0.5856	1050	12.3838
12.3841	0.6693	1200	12.3838
12.3891	0.7529	1350	12.3837
12.3824	0.8366	1500	12.3837

Framework versions

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