update readme (#531)

Author: wenhuach21

README.md

@@ -72,7 +72,7 @@ pip install auto-round-lib
 
 ## Model Quantization
 
-### Basic Usage (Gaudi/CPU/XPU/GPU)
+### Command Line Usage (Gaudi/CPU/XPU/GPU)
 
 A user guide detailing the full list of supported arguments is provided by calling ```auto-round -h``` on the terminal.
 Set the format you want in `format` and
@@ -161,7 +161,7 @@ from auto_round import AutoRound
 bits, group_size, sym = 4, 128, True
 autoround = AutoRound(model, tokenizer, bits=bits, group_size=group_size, sym=sym)
 
-## the best accuracy, 3X slower, low_gpu_mem_usage could save ~20G but ~30% slower
+## 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)
 
 ## 2-3X speedup, slight accuracy drop at W4G128
@@ -334,8 +334,8 @@ print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50)[0]))
 
 AutoRound automatically selects the best available backend based on the installed libraries and prompts the user to
 install additional libraries when a better backend is found. On CUDA, the default priority is Marlin > ExLLaMAV2 >
-Triton, but the final choice depends on factors such as bits group_size packing format compatibility, etc. Please refer
-to the following table for the details.
+Triton, but the final choice depends on factors such as bits, group_size, packing format compatibility, etc. And the backend may not always be the most suitable for certain devices. Please refer
+to the following table for the details and specify the backend you want.
 
 | Name                                 | Devices | Bits    | Dtypes    | Priority | Packing format  | Requirements                  |
 |--------------------------------------|---------|---------|-----------|----------|-----------------|-------------------------------|
@@ -345,12 +345,13 @@ to the following table for the details.
 | exllamav2 or<br/>gptqmodel:exllamav2 | cuda    | 4       | BF16/FP16 | 5        | gptq            | gptqmodel                     |
 | exllamav2 or<br/>gptq:exllamav2      | cuda    | 4       | FP16      | 5        | gptq_zp+-1      | auto-gptq                     |
 | gptq:cuda                            | cuda    | 2,3,4,8 | FP16      | 0        | gptq_zp+-1      | auto-gptq                     |
-| triton                               | cuda    | 2,3,8   | BF16/FP16 | 1        | gptq/gptq_zp+-1 | auto-round                    |
+| triton                               | cuda    | 2,4,8   | BF16/FP16 | 1        | gptq/gptq_zp+-1 | auto-round                    |
 | awq                                  | cuda    | 4       | FP16      | 5        | awq             | auto-awq                      |
 | hpu                                  | hpu     | 4       | BF16      | 0        | gptq/gptq_zp+-1 | auto-round                    |
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from auto_round import  AutoRoundConfig
 
 quantized_model_path = "./tmp_autoround"
 quantization_config = AutoRoundConfig(backend="auto")