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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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22
tests/e2e/test_optimizers.py
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@@ -13,6 +13,7 @@ from axolotl.utils.dict import DictDefault
from .utils import (
check_model_output_exists,
check_tensorboard_loss_decreased,
require_torch_2_5_1,
require_torch_2_6_0,
require_torch_2_7_0,
@@ -243,8 +244,8 @@ class TestCustomOptimizers(unittest.TestCase):
def test_came_pytorch(self, temp_dir):
cfg = DictDefault(
{
"base_model": "JackFram/llama-68m",
"tokenizer_type": "LlamaTokenizer",
"base_model": "axolotl-ai-co/tiny-llama-50m",
"tokenizer_type": "AutoTokenizer",
"sequence_len": 1024,
"load_in_8bit": True,
"adapter": "lora",
@@ -254,9 +255,7 @@ class TestCustomOptimizers(unittest.TestCase):
"lora_target_linear": True,
"val_set_size": 0.1,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
@@ -268,13 +267,15 @@ class TestCustomOptimizers(unittest.TestCase):
"micro_batch_size": 8,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"learning_rate": 2e-4,
"optimizer": "came_pytorch",
"adam_beta3": 0.9999,
"adam_epsilon2": 1e-16,
"max_steps": 5,
"max_steps": 50,
"logging_steps": 1,
"lr_scheduler": "cosine",
"save_first_step": False,
"use_tensorboard": True,
}
)
@@ -284,6 +285,13 @@ class TestCustomOptimizers(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=4.5,
max_final=4.3,
)
@require_torch_2_7_0