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Error code: ConfigNamesError Exception: DataFilesNotFoundError Message: No (supported) data files found in EnumaInc/ko-TinyLlama-1.1B-vocab-extend-mix-corpus-basic-tokenizer-eeve-stage1 Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 73, in compute_config_names_response config_names = get_dataset_config_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1873, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1854, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1245, in get_module module_name, default_builder_kwargs = infer_module_for_data_files( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 595, in infer_module_for_data_files raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else "")) datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in EnumaInc/ko-TinyLlama-1.1B-vocab-extend-mix-corpus-basic-tokenizer-eeve-stage1
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See axolotl config
axolotl version: 0.4.0
base_model: EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: /root/axolotl/datasets/mix_corpus_extended_validated.json
type: completion
field: text
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project: language-transfer-eeve-v2
wandb_entity:
wandb_watch:
wandb_name: eeve-v2-stage1
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 32
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00015
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 500
evals_per_epoch: 1
eval_table_size:
eval_max_new_tokens: 128
save_strategy: steps
save_steps: 100
save_total_limit: 5
#saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
# for curriculum learning
shuffle_merged_datasets: false
unfrozen_parameters:
- ^model.embed_tokens.weight$[32000:]
# - model.layers.2[0-9]+.block_sparse_moe.gate
# - model.layers.2[0-9]+.block_sparse_moe.experts
# - model.layers.3[0-9]+.block_sparse_moe.gate
# - model.layers.3[0-9]+.block_sparse_moe.experts
out
This model is a fine-tuned version of EnumaInc/ko-TinyLlama-1.1B-intermediate-step-1431k-3Tb-vocab-extend-45000-untrained-v1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6306
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: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.651 | 1.0 | 1918 | 1.6306 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0
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