Update README.md

Signed-off-by: Mamta Singh <[email protected]>

README.md

@@ -99,8 +99,8 @@ In summary:
 
 | High Level APIs | Sample use | Arguments         |
 |-----------------|------------|-------------------|
-| QEfficient.cloud.infer           |   [click here](#1-use-qefficientcloudinfer)         |  <li>model_name : $\color{green} {Mandatory}$</li> <li>num_cores : $\color{green} {Mandatory}$</li> <li>device_group : $\color{green} {Mandatory}$</li><li>batch_size : Optional [Default-1]</li> <li>prompt_len : Optional [Default-32]</li> <li>ctx_len : Optional [Default-128]</li><li>mxfp6 : Optional </li> <li>hf_token : Optional </li><li>cache_dir : Optional ["cache_dir" in current working directory]</li><li>prompt : Optinoal [Default-"My name is"]</li> |
-| QEfficient.cloud.execute  |     [click here](#2-use-of-qefficientcloudexcute)       |   <li>model_name : $\color{green} {Mandatory}$</li> <li>device_group : $\color{green} {Mandatory}$</li><li>qpc_path : $\color{green} {Mandatory}$</li><li>prompt : Optional [Default-"My name is"]</li> <li>cache_dir : Optional ["cache_dir" in current working directory]</li><li>hf_token : Optional </li>             |
+| QEfficient.cloud.infer           |   [click here](#1-use-qefficientcloudinfer)         |  <li>model_name : $\color{green} {Mandatory}$</li> <li>num_cores : $\color{green} {Mandatory}$</li> <li>device_group : $\color{green} {Mandatory}$</li><li>batch_size : Optional [Default-1]</li> <li>prompt_len : Optional [Default-32]</li> <li>ctx_len : Optional [Default-128]</li><li>mxfp6 : Optional </li> <li>hf_token : Optional </li><li>cache_dir : Optional ["cache_dir" in current working directory]</li><li>prompt : Optional</li><li>prompts_txt_file_path : Optional</li><li>*only one argument, prompt or prompts_txt_file_path should be passed*</li> |
+| QEfficient.cloud.execute  |     [click here](#2-use-of-qefficientcloudexcute)       |   <li>model_name : $\color{green} {Mandatory}$</li> <li>device_group : $\color{green} {Mandatory}$</li><li>qpc_path : $\color{green} {Mandatory}$</li> <li>cache_dir : Optional ["cache_dir" in current working directory]</li><li>hf_token : Optional </li><li>prompt : Optional</li><li>prompts_txt_file_path : Optional</li> <li>*only one argument, prompt or prompts_txt_file_path should be passed*</li>             |
 
 
 ### 1. Use QEfficient.cloud.infer 
@@ -116,7 +116,7 @@ This is the single e2e python api in the library, which takes model_card name as
 python -m QEfficient.cloud.infer --help
 python -m QEfficient.cloud.infer --model_name gpt2 --batch_size 1 --prompt_len 32 --ctx_len 128 --mxfp6 --num_cores 16 --device_group [0] --prompt "My name is" --mos 1 --aic_enable_depth_first  
 
-# If executing for batch size>1, pass path of txt file with input prompts, Example below
+# If executing for batch size>1, pass path of txt file with input prompts, Example below, sample txt file(prompts.txt) is present in examples folder .
 
 python -m QEfficient.cloud.infer --model_name gpt2 --batch_size 3 --prompt_len 32 --ctx_len 128 --num_cores 16 --device_group [0] --prompts_txt_file_path examples/prompts.txt --mxfp6 --mos 1 --aic_enable_depth_first  
  ```
@@ -128,7 +128,7 @@ Once we have compiled the QPC, we can now use the precompiled QPC in execute API
 python -m QEfficient.cloud.execute --model_name gpt2 --qpc_path qeff_models/gpt2/qpc_16cores_1BS_32PL_128CL_1devices_mxfp6/qpcs/ --prompt "Once upon a time in" --device_group [0]  
 ```
 
-We can also enable MQ, just based on the number of devices. Based on the "--device_group" as input it will create TS config on the fly. If "--device-group [0,1]" it will create TS config for 2 devices and use it for compilation, if "--device-group 0" then TS compilation is skipped and single soc execution is enabled.
+We can also enable MQ, just based on the number of devices. Based on the "--device-group" as input it will create TS config on the fly. If "--device-group [0,1]" it will create TS config for 2 devices and use it for compilation, if "--device-group 0" then TS compilation is skipped and single soc execution is enabled.
 
 ```bash
 python -m QEfficient.cloud.infer --model_name Salesforce/codegen-2B-mono --batch_size 1 --prompt_len 32 --ctx_len 128 --mxfp6 --num_cores 16 --device-group [0,1] --prompt "def fibonacci(n):" --mos 2 --aic_enable_depth_first  
@@ -145,7 +145,7 @@ python -m QEfficient.cloud.infer --model_name gpt2 --batch_size 1 --prompt_len 3
 
 | High Level APIs | Single SoC | Tensor Slicing         |
 |-----------------|------------|-------------------|
-| QEfficient.cloud.infer           | python -m QEfficient.cloud.infer --model_name $\color{green} {model}$  --batch_size 8 --prompt_len 128 --ctx_len 1024 --num_cores 16 --device-group [0] --prompt "My name is" --mxfp6 --hf_token  $\color{green}{xyz}$  --mos 1 --aic_enable_depth_first |  python -m QEfficient.cloud.infer --model_name $\color{green}{model}$  --batch_size 8 --prompt_len 128 --ctx_len 1024--num_cores 16 --device-group [0,1,2,3] --prompt "My name is" --mxfp6 --hf_token  $\color{green}{xyz}$  --mos 4 --aic_enable_depth_first |
+| QEfficient.cloud.infer           | python -m QEfficient.cloud.infer --model_name $\color{green} {model}$  --batch_size 1 --prompt_len 128 --ctx_len 1024 --num_cores 16 --device-group [0] --prompt "My name is" --mxfp6 --hf_token  $\color{green}{xyz}$  --mos 1 --aic_enable_depth_first |  python -m QEfficient.cloud.infer --model_name $\color{green}{model}$  --batch_size 1 --prompt_len 128 --ctx_len 1024--num_cores 16 --device-group [0,1,2,3] --prompt "My name is" --mxfp6 --hf_token  $\color{green}{xyz}$  --mos 4 --aic_enable_depth_first |
 | QEfficient.cloud.excute  |   python -m QEfficient.cloud.execute --model_name $\color{green}{model}$  --device_group [0] --qpc_path  $\color{green}{path}$  --prompt "My name is"  --hf_token  $\color{green}{xyz}$   |  python -m QEfficient.cloud.execute --model_name $\color{green}{model}$  --device_group [0,1,2,3] --qpc_path  $\color{green}{path}$  --prompt "My name is"  --hf_token  $\color{green}{xyz}$   |
 
 :memo: Replace $\color{green}{model}$ ,  $\color{green}{path}$  and  $\color{green}{xyz}$  with preffered model card name, qpc path and hf token respectively.