Support : Adding Support for LlamaForQuestionAnswering class
Author: Tanmaypatil123
Pull request: yes

# Adding Support for `LlamaForQuestionAnswering`.



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Fixes # issue #28265 

Added `LlamaForQuestionAnswering` model class in [modeling_llama](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py) with required test cases.

Following is the example for uitilization of `LlamaForQuestionAnswering` for question-answering.

```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch

model = AutoModelForQuestionAnswering.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v0.3")
tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v0.3")

text = """
The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain "Amazonas" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.
"""

question = "Which name is also used to describe the Amazon rainforest in English?"
inputs = tokenizer(question, text, add_special_tokens=True, return_tensors="pt")

input_ids = inputs["input_ids"].tolist()[0]

text_tokens = tokenizer.convert_ids_to_tokens(input_ids)

output = model(**inputs)

answer_start = torch.argmax(
        output.start_logits
    )  # Get the most likely beginning of answer with the argmax of the score
answer_end = torch.argmax(output.start_logits) + 1  # Get the most likely end of answer with the argmax of the score

answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))

print(f"Question: {question}")
print(f"Answer: {answer}\n")

```

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- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@NielsRogge  @ArthurZucker @younesbelkada

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

Documentation: @stevhliu and @MKhalusova

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):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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Comments:
hey @NielsRogge @ArthurZucker @younesbelkada can anyone from you guys review this PR ,

Hey @ArthurZucker, about this comment:

> Hey! Thanks for contributing! I don't mind having this merged, but I think it should be pretty easy for anyone to build on top of transformers and add their custom logic. If the community has a lot of interest for this let's merge it, usually we wait for the feature request to have interest (cc @NielsRogge )

I just wanted to clarify that I am interested in this merge. `LlamaForQuestionAnswering` is something I would appreciate as an in-built utility in the library!

Happy 2024 to everyone 🤗


@ArthurZucker can i copy from modeling_falcon. I think they are pretty similar.

sure, whichever fits best here

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