feat: Add native FP8 model support with scale_inv dequantization

[nyo16]

Summary

Add native FP8 quantized model support for models like Qwen3-FP8. This enables loading and running FP8 models with per-block
scale factors (scale_inv) for dequantization.

Changes

bumblebee.ex

• Add :preserve_source_types option to load_model/2 to keep FP8 types during loading

pytorch_params.ex

• Pass preserve_source_types through param loading pipeline
• Modify ensure_type/3 to preserve FP8 types when option is set

layers.ex

• Add fp8_aware_dense/3 layer that handles FP8 quantized weights
• Implements block-wise dequantization using scale_inv parameter
• Automatically falls back to identity scaling (1.0) for non-FP8 models

layers/transformer.ex

• Add :attention_dense option to blocks/2, block/2, multi_head_attention/4
• Allows custom dense function for Q, K, V, and output projections

text/qwen3.ex

• Update decoder to use fp8_aware_dense for attention via attention_dense option
• Update gated_ffn to use fp8_aware_dense for FFN layers
• Add scale_inv to params_mapping for all attention and FFN layers

Test plan

• FP8 model (Qwen3-0.6B-FP8) generates correct output ("Paris" for capital of France)
• Non-FP8 model (Qwen3-0.6B) still works correctly (backward compatible)
• Tested on RTX 5070 Ti (Blackwell, SM 12.0)

Dependencies

Requires (merge in order):

1. elixir-nx/safetensors - FP8 file I/O
2. elixir-nx/nx - FP8 type system support

Usage

# Load FP8 model with native weights
{:ok, model_info} = Bumblebee.load_model(
  {:hf, "Qwen/Qwen3-0.6B-FP8"},
  architecture: :for_causal_language_modeling,
  preserve_source_types: true
)

# Use normally - scale_inv dequantization happens automatically
serving = Bumblebee.Text.generation(model_info, tokenizer, generation_config)
Nx.Serving.run(serving, "The capital of France is")
# => "Paris..."

[josevalim]

Thank you! This PR should probably wait until this is done: elixir-nx/nx#1657 (comment)