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cimol/
68ee0c38-130c-4932-a6bf-dc0fba1d0a21

generated_from_trainer

peft-internal-testing/tiny-dummy-qwen2

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: peft-internal-testing/tiny-dummy-qwen2
bf16: true
chat_template: llama3
data_processes: 24
dataset_prepared_path: null
datasets:
- data_files:
  - 207e75bcba405bc5_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/207e75bcba405bc5_train_data.json
  type:
    field_input: content
    field_instruction: question
    field_output: answer
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: cimol/68ee0c38-130c-4932-a6bf-dc0fba1d0a21
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: 10
lora_alpha: 64
lora_dropout: 0.2
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
lr_scheduler_warmup_steps: 50
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 600
micro_batch_size: 8
mlflow_experiment_name: /tmp/207e75bcba405bc5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-05
optimizer: adamw_bnb_8bit
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
seed: 17333
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
total_train_batch_size: 32
train_batch_size: 8
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9dc8c0e5-0f7f-4837-a3cb-337a685a0733
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9dc8c0e5-0f7f-4837-a3cb-337a685a0733
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

68ee0c38-130c-4932-a6bf-dc0fba1d0a21

This model is a fine-tuned version of peft-internal-testing/tiny-dummy-qwen2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 11.8881

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: 8
eval_batch_size: 4
seed: 17333
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 600

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0032	1	11.9325
11.9042	0.1622	50	11.9005
11.8926	0.3244	100	11.8923
11.8872	0.4866	150	11.8894
11.8886	0.6488	200	11.8886
11.8889	0.8110	250	11.8888
11.8893	0.9732	300	11.8887
11.793	1.1354	350	11.8886
11.9225	1.2976	400	11.8881
11.958	1.4599	450	11.8886
11.9655	1.6221	500	11.8879
11.9678	1.7843	550	11.8883
11.7859	1.9465	600	11.8881

Framework versions

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