HuggingSpace

Modular - Phi

5 min read

15:08 pm

Sure! Here are the Markdown blogs for each pull request:

Blog 1: Modular phi

Modular phi

What is the Issue?

In the huggingface/transformers repository, there was an issue where the make repo-consistency command was failing due to inconsistencies in the modular models’ documentation and implementation. This issue was causing confusion and errors when trying to ensure the consistency of the repository.

What does the PR do?

This pull request (PR) addresses the issue by adding Modular Phi, which helps in maintaining consistency across the modular models. It ensures that the documentation and implementation of the modular models are in sync.

Why is it Important?

Maintaining consistency in a large codebase like huggingface/transformers is crucial for ensuring that all parts of the code work well together. This PR helps in achieving that by fixing the inconsistencies, making it easier for developers to work with the modular models.

Code Snippet

Here is a code snippet that shows the changes made in the PR:

# Before
initializer_range (`<fill_type>`, *optional*, defaults to 0.02): <fill_docstring>
 
# After
initializer_range (`float`, *optional*, defaults to 0.02):
    The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

You can view the full PR here.

Blog 2: Clarify Initializer Range Parameter Description

Clarify Initializer Range Parameter Description in Idefics3VisionConfig

What is the Issue?

The Idefics3VisionConfig had a confusing description for the initializer_range parameter. This ambiguity could lead to misunderstandings and incorrect usage of the parameter by developers.

What does the PR do?

This PR clarifies the description of the initializer_range parameter in the Idefics3VisionConfig. The updated description provides a clear explanation of what the parameter does and its default value.

Why is it Important?

Clear and accurate documentation is essential for developers to understand and use the parameters correctly. This PR ensures that the description of the initializer_range parameter is easy to understand, reducing the chances of errors and confusion.

Code Snippet

Here is a code snippet that shows the changes made in the PR:

# Before
initializer_range (`<fill_type>`, *optional*, defaults to 0.02): <fill_docstring>
 
# After
initializer_range (`float`, *optional*, defaults to 0.02):
    The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

You can view the full PR here.

Blog 3: Update Documentation for MAMBA2 and MISTRAL Models

Update Documentation for MAMBA2 and MISTRAL Models

What is the Issue?

The input documentation for the MAMBA2 and MISTRAL models was not matching with the forward pass of their respective models. This documentation mismatch was causing issues when trying to subclass from Mistral and use the models.

What does the PR do?

This PR updates the input documentation for the MAMBA2 and MISTRAL models to include details about cache_position and attention_mask. It ensures that the documentation accurately reflects the implementation of the models.

Why is it Important?

Accurate documentation is crucial for developers to understand how to use the models correctly. This PR ensures that the documentation is consistent with the implementation, making it easier for developers to work with the MAMBA2 and MISTRAL models.

Code Snippet

Here is a code snippet that shows the changes made in the PR:

# Before
# Documentation did not include details about cache_position and attention_mask
 
# After
# Updated documentation to include details about cache_position and attention_mask
cache_position (`torch.Tensor`, *optional*): The cache position tensor.
attention_mask (`torch.Tensor`, *optional*): The attention mask tensor.

You can view the full PR here.

Blog 4: Add Code Sample Docstrings and Checkpoint Reference for GLM Models

Add Code Sample Docstrings and Checkpoint Reference for GLM Models

What is the Issue?

There was a lack of code sample docstrings and checkpoint references for the GLM models in the huggingface/transformers repository. This made it difficult for developers to understand how to use these models effectively.

What does the PR do?

This PR adds code sample docstrings and checkpoint references for the GLM models. These additions provide clear examples and references for developers to follow when using the GLM models.

Why is it Important?

Providing code samples and checkpoint references makes it easier for developers to understand how to use the models. This PR helps in improving the usability of the GLM models by providing clear and concise documentation.

Code Snippet

Here is a code snippet that shows the changes made in the PR:

# Before
# No code sample docstrings and checkpoint references
 
# After
# Added code sample docstrings and checkpoint references
@add_code_sample_docstrings(
    checkpoint=_CHECKPOINT_FOR_DOC,
    output_type=TokenClassifierOutput,
    config_class=_CONFIG_FOR_DOC,
)
def forward(self, input_ids: Optional[torch.LongTensor] = None, ...):
    # Model forward pass

You can view the full PR here.

Blog 5: Example Doc for Token Classification of Llama and Dependent/Copied Models

Example Doc for Token Classification of Llama and Dependent/Copied Models

What is the Issue?

There was a need for example documentation for token classification using the Llama and its dependent/copied models. This lack of documentation made it difficult for developers to understand how to perform token classification with these models.

What does the PR do?

This PR adds example documentation for token classification using the Llama and its dependent/copied models. The models include Llama, Mistral, Mixttral, Nemotron, Persimmon, Qwen2, Qwen2Moe, StableLM, StarCoder2, Gemma (Modular), and Gemma2 (Modular).

Why is it Important?

Providing example documentation helps developers understand how to use the models for specific tasks, such as token classification. This PR makes it easier for developers to perform token classification with the Llama and its dependent/copied models.

Code Snippet

Here is a code snippet that shows the example documentation added in the PR:

# Example of token classification using LlamaForTokenClassification
from transformers import LlamaForTokenClassification, LlamaTokenizer
 
tokenizer = LlamaTokenizer.from_pretrained("llama-base")
model = LlamaForTokenClassification.from_pretrained("llama-base")
 
inputs = tokenizer("Hello, my name is Llama.", return_tensors="pt")
outputs = model(**inputs)

You can view the full PR here.

Feel free to use these blogs as needed. Let me know if there are any other details you would like to add or modify!

Links : TODO

Tags :

Date : 16th March, Sunday, 2025, (Wikilinks: 16th March, March 25, March, 2025. Sunday)

Category : Others

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