riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-08-02 19:21:31 +06:00
Code Issues Actions 3 Packages Projects Releases Wiki Activity

Fixing gated repo issues (#37463)

Browse Source 
using unsloth model
This commit is contained in:
Mohamed Mekkouri 2025-04-14 17:19:10 +02:00 committed by GitHub
parent a5dfb98977
commit d228f50acc
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 1 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
tests/quantization/quark_integration/test_quark.py
View File

@ -19,7 +19,6 @@ from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_quark,
require_read_token,
require_torch_gpu,
require_torch_multi_gpu,
slow,
@ -44,7 +43,7 @@ class QuarkConfigTest(unittest.TestCase):
@require_quark
@require_torch_gpu
class QuarkTest(unittest.TestCase):
reference_model_name = "meta-llama/Llama-3.1-8B-Instruct"
reference_model_name = "unsloth/Meta-Llama-3.1-8B-Instruct"
quantized_model_name = "amd/Llama-3.1-8B-Instruct-w-int8-a-int8-sym-test"
input_text = "Today I am in Paris and"
@ -76,13 +75,11 @@ class QuarkTest(unittest.TestCase):
device_map=cls.device_map,
)
@require_read_token
def test_memory_footprint(self):
mem_quantized = self.quantized_model.get_memory_footprint()
self.assertTrue(self.mem_fp16 / mem_quantized > self.EXPECTED_RELATIVE_DIFFERENCE)
@require_read_token
def test_device_and_dtype_assignment(self):
r"""
Test whether trying to cast (or assigning a device to) a model after quantization will throw an error.
@ -96,7 +93,6 @@ class QuarkTest(unittest.TestCase):
# Tries with a `dtype``
self.quantized_model.to(torch.float16)
@require_read_token
def test_original_dtype(self):
r"""
A simple test to check if the model succesfully stores the original dtype
@ -107,7 +103,6 @@ class QuarkTest(unittest.TestCase):
self.assertTrue(isinstance(self.quantized_model.model.layers[0].mlp.gate_proj, QParamsLinear))
@require_read_token
def check_inference_correctness(self, model):
r"""
Test the generation quality of the quantized model and see that we are matching the expected output.
@ -131,7 +126,6 @@ class QuarkTest(unittest.TestCase):
# Get the generation
self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
@require_read_token
def test_generate_quality(self):
"""
Simple test to check the quality of the model by comparing the generated tokens with the expected tokens