refine readme

README.md

@@ -16,10 +16,10 @@ AutoRound
 
 ## 🚀 What is AutoRound?
 
-AutoRound is an advanced quantization library designed for Large Language Models (LLMs) and Vision-Language Models (VLMs). It delivers high accuracy at ultra-low bit widths (2–4 bits) with minimal tuning by leveraging sign-gradient descent and offering broad hardware compatibility. Check out our paper on [arxiv](https://arxiv.org/pdf/2309.05516) for more details and quantized models in several
-Hugging Face Spaces,
-e.g. [Intel](https://huggingface.co/Intel), [OPEA](https://huggingface.co/OPEA),  [Kaitchup](https://huggingface.co/kaitchup)
-and [fbaldassarri](https://huggingface.co/fbaldassarri). Please check out [User guide](./docs/step_by_step.md) for more details
+AutoRound is an advanced quantization library designed for Large Language Models (LLMs) and Vision-Language Models (VLMs). 
+It delivers high accuracy at ultra-low bit widths (2–4 bits) with minimal tuning by leveraging sign-gradient descent and offering broad hardware compatibility. 
+For more details, see our [paper](https://arxiv.org/pdf/2309.05516) for more details and explore quantized models available on several Hugging Face Spaces, e.g. [Intel](https://huggingface.co/Intel), [OPEA](https://huggingface.co/OPEA),  [Kaitchup](https://huggingface.co/kaitchup)
+and [fbaldassarri](https://huggingface.co/fbaldassarri).For usage instructions, please refer to  [User Guide](./docs/step_by_step.md).
 
 <p align="center">
   <img src="docs/imgs/autoround_overview.png" alt="AutoRound Overview" width="80%">
@@ -84,7 +84,7 @@ Choose from `auto-round-best`, `auto-round`, and `auto-round-light` to suit your
 ✅ Advanced Utilities
 Includes [multiple gpus quantization](https://github.com/intel/auto-round/blob/main/docs/step_by_step.md#devicemulti-gpu-setting-in-quantization), [multiple calibration datasets](https://github.com/intel/auto-round/blob/main/docs/step_by_step.md#default-dataset) and support for [10+ runtime backends](https://github.com/intel/auto-round/blob/main/docs/step_by_step.md#specify-inference-backend).
 
-🟨 Beyond weight only quantization. We are actively expanding support for additional datatypes such as **MXFP**, NVFP, W8A8, and more.
+✅ Beyond weight only quantization. We are actively expanding support for additional datatypes such as **MXFP**, NVFP, W8A8, and more.
 
 
 ## Installation
@@ -164,25 +164,25 @@ configuration to suit your specific requirements and available resources.
 ### API Usage
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
 from auto_round import AutoRound
 
-model_name = "Qwen/Qwen3-0.6B"
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Load a model (supports BF16/FP16/FP8/FP32)
+model_name_or_path = "Qwen/Qwen3-0.6B"
 
-bits, group_size, sym = 4, 128, True
-autoround = AutoRound(model, tokenizer, bits=bits, group_size=group_size, sym=sym)
+# Available schemes: "W2A16", "W3A16", "W4A16", "W8A16", "NVFP4", "MXFP4" (no real kernels), "GGUF:Q4_K_M", etc.
+ar = AutoRound(model_name_or_path, scheme="W4A16")
 
-## the best accuracy, 4-5X slower, low_gpu_mem_usage could save ~20G but ~30% slower
-# autoround = AutoRound(model, tokenizer, nsamples=512, iters=1000, low_gpu_mem_usage=True, bits=bits, group_size=group_size, sym=sym)
+# Highest accuracy (4–5× slower).
+# `low_gpu_mem_usage=True` saves ~20GB VRAM but runs ~30% slower.
+# ar = AutoRound(model_name_or_path, tokenizer, nsamples=512, iters=1000, low_gpu_mem_usage=True)
 
-## 2-3X speedup, slight accuracy drop at W4G128
-# autoround = AutoRound(model, tokenizer, nsamples=128, iters=50, lr=5e-3, bits=bits, group_size=group_size, sym=sym )
+# Faster quantization (2–3× speedup) with slight accuracy drop at W4G128.
+# ar = AutoRound(model_name_or_path, tokenizer, nsamples=128, iters=50, lr=5e-3)
 
+# Save quantized model
 output_dir = "./tmp_autoround"
-## format= 'auto_round'(default), 'auto_gptq', 'auto_awq'
-autoround.quantize_and_save(output_dir, format="auto_round")
+# Supported formats: "auto_round" (default), "auto_gptq", "auto_awq", "llm_compressor", "gguf:q4_k_m"
+ar.quantize_and_save(output_dir, format="auto_round")
 ```
 
 <details>

auto_round/schemes.py

@@ -142,7 +142,7 @@ def is_preset_scheme(name: str) -> bool:
 #     "act_data_type": "fp",
 # }))
 
-FPW8_STATIC = QuantizationScheme.from_dict(
+FP8_STATIC = QuantizationScheme.from_dict(
     {
         "bits": 8,
         "group_size": -1,
@@ -163,7 +163,7 @@ def is_preset_scheme(name: str) -> bool:
     "MXFP8": MXFP8,
     "NVFP4": NVFP4,
     "FPW8A16": FPW8A16,
-    "FPW8_STATIC": FPW8_STATIC,
+    "FP8_STATIC": FP8_STATIC,
 }
 from auto_round.export.export_to_gguf.config import GGUF_CONFIG