Repository: huggingface/transformers
Pull request: Update test of Youtu-LLM to pr-aligned repos

# What does this PR do?

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To update the test of Youtu-LLM to pr-aligned repos, fixing issues mentioned here: https://github.com/huggingface/transformers/pull/43166#issuecomment-3795442700.


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
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      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

@molbap @vasqu @xenova 

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Models:

- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier

Library:

- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh

Integrations:

- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo

Devices/Backends:

- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad 

Documentation: @stevhliu

Research projects are not maintained and should be taken as is.

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Changed files:
filename: tests/models/youtu/test_modeling_youtu.py
patch: @@ -111,13 +111,10 @@ def test_dynamic_cache(self):
             "Simply put, the theory of relativity states that ",
             "My favorite all time favorite condiment is ketchup.",
         ]
-        tokenizer = AutoTokenizer.from_pretrained("Junrulu/Youtu-LLM-2B-Base-hf")
+        tokenizer = AutoTokenizer.from_pretrained("tencent/Youtu-LLM-2B-Base")
         model = YoutuForCausalLM.from_pretrained(
-            "Junrulu/Youtu-LLM-2B-Base-hf", device_map=torch_device, dtype=torch.float16
+            "tencent/Youtu-LLM-2B-Base", device_map=torch_device, dtype=torch.float16
         )
-        if model.config.tie_word_embeddings:
-            # Youtu-LLM-2B-Base contains extra repeated weights for the tied embeddings, we can tie weights here according to its config
-            model.tie_weights()
         inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
 
         # Dynamic Cache
@@ -142,13 +139,10 @@ def test_static_cache(self):
             "Simply put, the theory of relativity states that ",
             "My favorite all time favorite condiment is ketchup.",
         ]
-        tokenizer = AutoTokenizer.from_pretrained("Junrulu/Youtu-LLM-2B-Base-hf")
+        tokenizer = AutoTokenizer.from_pretrained("tencent/Youtu-LLM-2B-Base")
         model = YoutuForCausalLM.from_pretrained(
-            "Junrulu/Youtu-LLM-2B-Base-hf", device_map=torch_device, dtype=torch.float16
+            "tencent/Youtu-LLM-2B-Base", device_map=torch_device, dtype=torch.float16
         )
-        if model.config.tie_word_embeddings:
-            # Youtu-LLM-2B-Base contains extra repeated weights for the tied embeddings, we can tie weights here according to its config
-            model.tie_weights()
         inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
 
         # Static Cache
@@ -177,13 +171,10 @@ def test_compile_static_cache(self):
             "Simply put, the theory of relativity states that ",
             "My favorite all time favorite condiment is ketchup.",
         ]
-        tokenizer = AutoTokenizer.from_pretrained("Junrulu/Youtu-LLM-2B-Base-hf")
+        tokenizer = AutoTokenizer.from_pretrained("tencent/Youtu-LLM-2B-Base")
         model = YoutuForCausalLM.from_pretrained(
-            "Junrulu/Youtu-LLM-2B-Base-hf", device_map=torch_device, dtype=torch.float16
+            "tencent/Youtu-LLM-2B-Base", device_map=torch_device, dtype=torch.float16
         )
-        if model.config.tie_word_embeddings:
-            # Youtu-LLM-2B-Base contains extra repeated weights for the tied embeddings, we can tie weights here according to its config
-            model.tie_weights()
         inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
 
         # Static Cache + compile