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use smaller pretrained models for ci

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This commit is contained in:
Wing Lian
2026-04-23 13:51:01 +00:00
parent 1bf65c500e
commit 431888c1de
24 changed files with 614 additions and 205 deletions
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56
tests/e2e/patched/test_falcon_samplepack.py
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@@ -4,14 +4,16 @@ E2E tests for falcon
import unittest
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 ..utils import check_model_output_exists, with_temp_dir
from ..utils import (
check_model_output_exists,
check_tensorboard_loss_decreased,
with_temp_dir,
)
class TestFalconPatched(unittest.TestCase):
@@ -19,13 +21,12 @@ class TestFalconPatched(unittest.TestCase):
Test case for Falcon models
"""
@pytest.mark.skip(reason="no tiny models for testing with safetensors")
@with_temp_dir
def test_qlora(self, temp_dir):
cfg = DictDefault(
{
"base_model": "illuin/tiny-random-FalconForCausalLM",
"flash_attention": True,
"base_model": "axolotl-ai-co/tiny-falcon-42m",
"flash_attention": False,
"sample_packing": True,
"sequence_len": 2048,
"load_in_4bit": True,
@@ -47,17 +48,19 @@ class TestFalconPatched(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": 2e-4,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
"max_steps": 50,
"logging_steps": 1,
"save_steps": 50,
"eval_steps": 50,
"bf16": "auto",
"save_first_step": False,
"use_tensorboard": True,
}
)
cfg = validate_config(cfg)
@@ -66,14 +69,20 @@ class TestFalconPatched(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,
)
@pytest.mark.skip(reason="no tiny models for testing with safetensors")
@with_temp_dir
def test_ft(self, temp_dir):
cfg = DictDefault(
{
"base_model": "illuin/tiny-random-FalconForCausalLM",
"flash_attention": True,
"base_model": "axolotl-ai-co/tiny-falcon-42m",
"flash_attention": False,
"sample_packing": True,
"sequence_len": 2048,
"val_set_size": 0.05,
@@ -88,17 +97,19 @@ class TestFalconPatched(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": 2e-4,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
"max_steps": 50,
"logging_steps": 1,
"save_steps": 50,
"eval_steps": 50,
"bf16": "auto",
"save_first_step": False,
"use_tensorboard": True,
}
)
cfg = validate_config(cfg)
@@ -107,3 +118,10 @@ class TestFalconPatched(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,
)