[Docs] Update ReadME (#1594)

SUMMARY:
- Link to deep-dive
- Update to mention updates to AWQ

---------

Co-authored-by: Kyle Sayers <kylesayrs@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

Author: 3 people

README.md

@@ -14,12 +14,14 @@
 
 ## 🚀 What's New!
 
-Big updates have landed in LLM Compressor! Check out these exciting new features:
+Big updates have landed in LLM Compressor! To get a more in-depth look, check out the [deep-dive](https://x.com/RedHat_AI/status/1937865425687093554).
+
+Some of the exciting new features include:
 
 * **Large Model Support with Sequential Onloading** As of llm-compressor>=0.6.0, you can now quantize very large language models on a single GPU. Models are broken into disjoint layers which are then onloaded to the GPU one layer at a time. For more information on sequential onloading, see [Big Modeling with Sequential Onloading](examples/big_models_with_sequential_onloading/README.md) as well as the [DeepSeek-R1 Example](examples/quantizing_moe/deepseek_r1_example.py).
 * **Preliminary FP4 Quantization Support:** Quantize weights and activations to FP4 and seamlessly run the compressed model in vLLM. Model weights and activations are quantized following the NVFP4 [configuration](https://github.com/neuralmagic/compressed-tensors/blob/f5dbfc336b9c9c361b9fe7ae085d5cb0673e56eb/src/compressed_tensors/quantization/quant_scheme.py#L104). See examples of [weight-only quantization](examples/quantization_w4a16_fp4/llama3_example.py) and [fp4 activation support](examples/quantization_w4a4_fp4/llama3_example.py). Support is currently preliminary and additional support will be added for MoEs.
+* **Updated AWQ Support:** Improved support for MoEs with better handling of larger models
 * **Axolotl Sparse Finetuning Integration:** Seamlessly finetune sparse LLMs with our Axolotl integration. Learn how to create [fast sparse open-source models with Axolotl and LLM Compressor](https://developers.redhat.com/articles/2025/06/17/axolotl-meets-llm-compressor-fast-sparse-open). See also the [Axolotl integration docs](https://docs.axolotl.ai/docs/custom_integrations.html#llmcompressor).
-* **AutoAWQ Integration:** Perform low-bit weight-only quantization efficiently using AutoAWQ, now part of LLM Compressor. *Note: This integration should be considered experimental for now. Enhanced support, including for MoE models and improved handling of larger models via layer sequential pipelining, is planned for upcoming releases.* [See the details](https://github.com/vllm-project/llm-compressor/pull/1177).
 * **Day 0 Llama 4 Support:** Meta utilized LLM Compressor to create the [FP8-quantized Llama-4-Maverick-17B-128E](https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8), optimized for vLLM inference using [compressed-tensors](https://github.com/neuralmagic/compressed-tensors) format.
 
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