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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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8
tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py
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@@ -32,12 +32,12 @@ from axolotl.utils.dict import DictDefault
MODEL_CONFIGS = [
{
"name": "trl-internal-testing/tiny-MistralForCausalLM-0.2",
"name": "axolotl-ai-co/tiny-mistral-25m",
"expected_activation": apply_lora_mlp_swiglu,
"dtype": torch.float16,
},
{
"name": "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
"name": "axolotl-ai-co/tiny-qwen2-129m",
"expected_activation": apply_lora_mlp_swiglu,
"dtype": torch.float16,
},
@@ -47,7 +47,7 @@ MODEL_CONFIGS = [
"dtype": torch.float32,
},
{
"name": "trl-internal-testing/tiny-Gemma2ForCausalLM",
"name": "axolotl-ai-co/tiny-gemma2-137m",
"expected_activation": apply_lora_mlp_geglu,
"dtype": torch.float16,
},
@@ -159,7 +159,7 @@ def test_swiglu_mlp_integration(small_llama_model):
def test_geglu_model_integration():
"""Test GeGLU activation with Gemma model."""
model = AutoModelForCausalLM.from_pretrained(
"trl-internal-testing/tiny-Gemma2ForCausalLM",
"axolotl-ai-co/tiny-gemma2-137m",
dtype=torch.float16,
device_map="cuda:0",
)