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axolotl/tests/e2e/test_deepseekv3.py
Dan Saunders 79ddaebe9a Add ruff, remove black, isort, flake8, pylint (#3092)
* black, isort, flake8 -> ruff

* remove unused

* add back needed import

* fix
2025-08-23 23:37:33 -04:00

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"""
E2E tests for deepseekv3
"""
from pathlib import Path
import pytest
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from tests.hf_offline_utils import enable_hf_offline
class TestDeepseekV3:
"""
Test case for DeepseekV3 models
"""
@enable_hf_offline
@pytest.mark.parametrize(
"sample_packing",
[True, False],
)
def test_lora_deepseekv3(self, temp_dir, sample_packing):
cfg = DictDefault(
{
"base_model": "axolotl-ai-co/DeepSeek-V3-11M",
"trust_remote_code": True,
"sample_packing": sample_packing,
"flash_attention": True,
"sequence_len": 2048,
"adapter": "lora",
"lora_r": 8,
"lora_alpha": 16,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0,
"datasets": [
{
"path": "mlabonne/FineTome-100k",
"type": "chat_template",
"field_messages": "conversations",
"message_property_mappings": {
"role": "from",
"content": "value",
},
"drop_system_message": True,
"split": "train[:1%]",
},
],
"special_tokens": {
"bos_token": "<begin▁of▁sentence>",
"eos_token": "<end▁of▁sentence>",
},
"chat_template": "deepseek_v3",
"num_epochs": 1,
"micro_batch_size": 1,
"gradient_accumulation_steps": 2,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 5,
"save_safetensors": True,
"bf16": True,
"save_first_step": False,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
dataset_meta = load_datasets(cfg=cfg)
train(cfg=cfg, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "adapter_model.safetensors").exists()
@enable_hf_offline
@pytest.mark.parametrize(
"sample_packing",
[True, False],
)
def test_fft_deepseekv3(self, temp_dir, sample_packing):
cfg = DictDefault(
{
"base_model": "axolotl-ai-co/DeepSeek-V3-11M",
"trust_remote_code": True,
"sample_packing": sample_packing,
"flash_attention": True,
"sequence_len": 2048,
"val_set_size": 0,
"datasets": [
{
"path": "mlabonne/FineTome-100k",
"type": "chat_template",
"field_messages": "conversations",
"message_field_role": "from",
"message_field_content": "value",
"split": "train[:1%]",
},
],
"chat_template": "deepseek_v3",
"special_tokens": {
"bos_token": "<begin▁of▁sentence>",
"eos_token": "<end▁of▁sentence>",
},
"num_epochs": 1,
"micro_batch_size": 1,
"gradient_accumulation_steps": 2,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 5,
"save_safetensors": True,
"bf16": True,
"save_first_step": False,
}
)
cfg = validate_config(cfg)
normalize_config(cfg)
dataset_meta = load_datasets(cfg=cfg)
train(cfg=cfg, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "model.safetensors").exists()