hf offline decorator for tests to workaround rate limits (#2452) [skip ci]

* hf offline decorator for tests to workaround rate limits

* fail quicker so we can see logs

* try new cache name

* limit files downloaded

* phi mini predownload

* offline decorator for phi tokenizer

* handle meta llama 8b offline too

* make sure to return fixtures if they are wrapped too

* more fixes

* more things offline

* more offline things

* fix the env var

* fix the model name

* handle gemma also

* force reload of modules to recheck offline status

* prefetch mistral too

* use reset_sessions so hub picks up offline mode

* more fixes

* rename so it doesn't seem like a context manager

* fix backoff

* switch out tinyshakespeare dataset since it runs a py script to fetch data and doesn't work offline

* include additional dataset

* more fixes

* more fixes

* replace tiny shakespeaere dataset

* skip some tests for now

* use more robust check using snapshot download to determine if a dataset name is on the hub

* typo for skip reason

* use local_files_only

* more fixtures

* remove local only

* use tiny shakespeare as pretrain dataset and streaming can't be offline even if precached

* make sure fixtures aren't offline

improve the offline reset
try bumping version of datasets
reorder reloading and setting
prime a new cache
run the tests now with fresh cache
try with a static cache

* now run all the ci again with hopefully a correct cache

* skip wonky tests for now

* skip wonky tests for now

* handle offline mode for model card creation

tests/test_data.py

@@ -5,6 +5,7 @@ test module for the axolotl.utils.data module
 import unittest
 
 from transformers import LlamaTokenizer
+from utils import enable_hf_offline
 
 from axolotl.utils.data import encode_pretraining, md5
 
@@ -14,6 +15,7 @@ class TestEncodePretraining(unittest.TestCase):
     test class for encode pretraining and md5 helper
     """
 
+    @enable_hf_offline
     def setUp(self):
         self.tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b")
         self.tokenizer.add_special_tokens(