See axolotl config

axolotl version: 0.4.1

adapter: lora
auto_find_batch_size: true
base_model: katuni4ka/tiny-random-qwen1.5-moe
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 18a5afee19d07bd3_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/18a5afee19d07bd3_train_data.json
  type:
    field_input: captions
    field_instruction: ASR
    field_output: whole_caption
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: true
hub_model_id: lesso15/88f1be1e-60bc-40f1-b83e-84e274d81dc0
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000215
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
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_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/18a5afee19d07bd3_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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: 50
saves_per_epoch: null
seed: 150
sequence_len: 512
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: 19d65acb-908a-4ff2-b1f6-0eb0b9d338a3
wandb_project: 15a
wandb_run: your_name
wandb_runid: 19d65acb-908a-4ff2-b1f6-0eb0b9d338a3
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null

88f1be1e-60bc-40f1-b83e-84e274d81dc0

This model is a fine-tuned version of katuni4ka/tiny-random-qwen1.5-moe on the None dataset. It achieves the following results on the evaluation set:

• Loss: 11.8173

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: 0.000215
• train_batch_size: 4
• eval_batch_size: 4
• seed: 150
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_BNB 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: 50
• training_steps: 500

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0000	1	11.9326
11.8764	0.0016	50	11.8651
11.843	0.0032	100	11.8445
11.833	0.0048	150	11.8337
11.8239	0.0064	200	11.8266
11.8237	0.0081	250	11.8225
11.8179	0.0097	300	11.8207
11.8168	0.0113	350	11.8190
11.8152	0.0129	400	11.8179
11.8169	0.0145	450	11.8174
11.8159	0.0161	500	11.8173

Framework versions

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