[Feature] Add HumanV model architecture
Author: humanprojectceo
Pull request: yes

# What does this PR do?

This Pull Request introduces the `HumanV` model architecture to the `transformers` library. `HumanV` is a highly resource-efficient, causal language model architecture designed specifically to optimize Key-Value (KV) cache memory footprint and compute scaling on limited hardware (such as single NVIDIA T4 GPUs and TPUs).

### Key Architectural Highlights:
* **Hybrid Self-Attention**: Leverages a balanced attention pipeline. The initial layers implement full dense self-attention to establish global context, while the subsequent layers utilize a custom, hardware-friendly **Local-Global Block Sparse Attention** (Block size: 16, Window: 128) to significantly cut down quadratic attention complexity during long-context processing.
* **Grouped-Query Attention (GQA)**: Implements a 3:1 Query-to-Group ratio (12 Query heads, 4 Key-Value heads) to drastically reduce memory bandwidth and inference-time KV cache storage.
* **Advanced Transformer Components**: Built using the SwiGLU activation function, Rotary Position Embeddings (RoPE) for stable positional generalization, and RMSNorm ($\epsilon = 10^{-5}$) with tied word embeddings.

### Technical Validation & Convergence Proof:
The math, gradient flow, and architectural stability of the HumanV codebase have been verified by successfully pre-training a 199M parameter model: **`humanvcompany/HumanV-0.2B-Base`** on the `TinyStories` dataset for 1,000 steps. 

During training on GPU (CUDA) and TPU (PyTorch-XLA), the model exhibited solid convergence with zero gradient explosions, achieving:
* **Validation Loss**: 2.964
* **Evaluation Perplexity (PPL)**: 19.384
* **Inference Pipeline**: Autoregressive decoding verified with stable KV caching under temperature-based sampling.

The files added in this PR include:
* `src/transformers/models/humanv/configuration_humanv.py`
* `src/transformers/models/humanv/modeling_humanv.py`
* `tests/models/humanv/test_modeling_humanv.py`

---

- [x] I confirm that this is not a pure code agent PR.

## 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://huggingface.co/docs/transformers/contributing) and the [Pull Request](https://huggingface.co/docs/transformers/pr_checks) checks?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.
- [x] Did you make sure to update the documentation with your changes according to the [guidelines](https://github.com/huggingface/transformers/tree/main/docs)?
- [x] Did you write any new necessary [tests](https://huggingface.co/docs/transformers/testing)?

## Who can review?

For text models, attention mechanisms, and model loading:
@ArthurZucker @Cyrilvallez @vasqu

Comments:
Hey, sorry! We generally don't add new architectures to the core library until there are major pretrained checkpoints; a demo 1000 step training run on `TinyStories` doesn't really count.

You can, however, share the model checkpoints as custom code in the repo itself, and users can load them with `trust_remote_code=True`!

> Hey, sorry! We generally don't add new architectures to the core library until there are major pretrained checkpoints; a demo 1000 step training run on `TinyStories` doesn't really count.
> 
> You can, however, share the model checkpoints as custom code in the repo itself, and users can load them with `trust_remote_code=True`!

Thank you for the prompt review and the helpful feedback.

We completely understand the policy regarding the inclusion of new architectures in the core library and the necessity for substantial, fully pretrained checkpoints.

Our team is currently establishing the necessary compute infrastructure to conduct a comprehensive, full-scale training run over a significantly higher number of epochs. Once this rigorous training phase is complete, we intend to publish the finalized checkpoints, along with detailed metrics and documentation, directly on Hugging Face.

In the meantime, we will gladly follow your recommendation and share our model utilizing the custom code feature (trust_remote_code=True) in our repository.

Thank you again for your time, attention, and valuable guidance.

Best regards,

HumanV team.