Repository: huggingface/transformers
Pull request: Remove mention of TensorFlow/Flax/JAX from English documentation

# What does this PR do?

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      Pull Request section?
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      [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?

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<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface, @SunMarc and @qgallouedec
- chat templates: @Rocketknight1

Integrations:

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Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

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Changed files:
filename: docs/source/en/add_new_model.md
patch: @@ -278,7 +278,7 @@ Every Transformers model output should have a precision or error tolerance of *1
 
 Here are some tips for an efficient debugging environment.
 
-- To debug intermediate results, it depends on the machine learning framework the original model repository is using. For PyTorch, you should write a script to decompose the original model into smaller sub-components to retrieve the intermediate values. For TensorFlow, you may need to use [tf.print](https://www.tensorflow.org/api_docs/python/tf/print). For Flax, make sure the model is *not jitted* during the forward pass (refer to this GitHub [Issue](https://github.com/google/jax/issues/196) for more details).
+- To debug intermediate results, it depends on the machine learning framework the original model repository is using. For PyTorch, you should write a script to decompose the original model into smaller sub-components to retrieve the intermediate values.
 
 - It is faster to debug with a smaller pretrained checkpoint versus a larger checkpoint where the forward pass takes more than 10 seconds. If only large checkpoints are available, create a dummy model with randomly initialized weights and save those weights to compare against the Transformers implementation.
 

filename: docs/source/en/internal/model_debugging_utils.md
patch: @@ -237,8 +237,7 @@ identical, but not quite. Looking through the file differences makes it easier t
 
 ### Limitations and scope
 
-This feature will only work for torch-based models, and would require more work and case-by-case approach for say
-`jax`-based models that are usually compiled. Models relying heavily on external kernel calls may work, but trace will
+This feature will only work for torch-based models. Models relying heavily on external kernel calls may work, but trace will
 probably miss some things. Regardless, any python implementation that aims at mimicking another implementation can be
 traced once instead of reran N times with breakpoints.
 
@@ -357,4 +356,4 @@ Skipped : 124/323 (38.4%)
 
 📄 JSON saved to /home/pablo/git/transformers/scan_test_inputs_embeds.json
 
-```
\ No newline at end of file
+```