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use smaller pretrained models for ci (#3620) [skip ci]

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* use smaller pretrained models for ci

* more steps for loss check

* fix tests

* more train steps

* fix losses
This commit is contained in:
Wing Lian
2026-04-27 13:22:56 -04:00
committed by GitHub
parent 798c8fba89
commit ac77da96da
24 changed files with 716 additions and 288 deletions
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58
tests/e2e/patched/test_mixtral_samplepack.py
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@@ -9,7 +9,11 @@ from axolotl.train import train
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from ..utils import check_model_output_exists, with_temp_dir
from ..utils import (
check_model_output_exists,
check_tensorboard_loss_decreased,
with_temp_dir,
)
class TestMixtral(unittest.TestCase):
@@ -21,8 +25,7 @@ class TestMixtral(unittest.TestCase):
def test_qlora(self, temp_dir):
cfg = DictDefault(
{
"base_model": "hf-internal-testing/Mixtral-tiny",
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
"base_model": "axolotl-ai-co/tiny-mixtral-30m",
"flash_attention": True,
"sample_packing": True,
"sequence_len": 2048,
@@ -30,7 +33,7 @@ class TestMixtral(unittest.TestCase):
"adapter": "qlora",
"lora_r": 16,
"lora_alpha": 32,
"lora_dropout": 0.1,
"lora_dropout": 0.0,
"lora_target_linear": True,
"val_set_size": 0.05,
"special_tokens": {},
@@ -41,17 +44,21 @@ class TestMixtral(unittest.TestCase):
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"micro_batch_size": 4,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"learning_rate": 3e-3,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 5,
"save_steps": 3,
"eval_steps": 4,
"max_steps": 80,
"warmup_steps": 5,
"logging_steps": 1,
"save_steps": 80,
"eval_steps": 80,
"bf16": "auto",
"save_first_step": False,
"use_tensorboard": True,
"seed": 42,
}
)
cfg = validate_config(cfg)
@@ -60,13 +67,19 @@ class TestMixtral(unittest.TestCase):
train(cfg=cfg, dataset_meta=dataset_meta)
check_model_output_exists(temp_dir, cfg)
check_tensorboard_loss_decreased(
temp_dir + "/runs",
initial_window=10,
final_window=10,
max_initial=6.0,
max_final=4.7,
)
@with_temp_dir
def test_ft(self, temp_dir):
cfg = DictDefault(
{
"base_model": "hf-internal-testing/Mixtral-tiny",
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF",
"base_model": "axolotl-ai-co/tiny-mixtral-30m",
"flash_attention": True,
"sample_packing": True,
"sequence_len": 2048,
@@ -79,17 +92,21 @@ class TestMixtral(unittest.TestCase):
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"micro_batch_size": 4,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"learning_rate": 5e-4,
"optimizer": "adamw_torch_fused",
"lr_scheduler": "cosine",
"max_steps": 5,
"save_steps": 3,
"eval_steps": 4,
"max_steps": 80,
"warmup_steps": 5,
"logging_steps": 1,
"save_steps": 80,
"eval_steps": 80,
"bf16": "auto",
"save_first_step": False,
"use_tensorboard": True,
"seed": 42,
}
)
cfg = validate_config(cfg)
@@ -98,3 +115,10 @@ class TestMixtral(unittest.TestCase):
train(cfg=cfg, dataset_meta=dataset_meta)
check_model_output_exists(temp_dir, cfg)
check_tensorboard_loss_decreased(
temp_dir + "/runs",
initial_window=5,
final_window=5,
max_initial=6.0,
max_final=4.7,
)