[NFC] fix typo applications/ and colossalai/ (#3735)

applications/Chat/examples/community/peft/README.md

@@ -18,7 +18,7 @@ For SFT training, just call train_peft_sft.py
 Its arguments are almost identical to train_sft.py instead adding a new eval_dataset if you have a eval_dataset file. The data file is just a plain datafile, please check the format in the easy_dataset.py.
 
 For stage-3 rlhf training, call train_peft_prompts.py.
-Its arguments are almost idential to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported.
+Its arguments are almost identical to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported.
 
 # Dataformat
 Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt.

applications/Chat/inference/README.md

@@ -75,7 +75,7 @@ E.g. you can set `export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`.
 
 Please ensure you have downloaded HF-format model weights of LLaMA models first.
 
-Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight convertion script.
+Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight conversion script.
 
 After installing this lib, we may convert the original HF-format LLaMA model weights to 4-bit version.
 

applications/Chat/inference/benchmark.py

@@ -123,7 +123,7 @@ if __name__ == "__main__":
     start = time()
     for instruction in instructions:
         print(f"Instruction: {instruction}")
-        resp, tokens = evaluate(model, tokenizer, instruction, temparature=0.2, num_beams=1)
+        resp, tokens = evaluate(model, tokenizer, instruction, temperature=0.2, num_beams=1)
         total_tokens += tokens
         print(f"Response: {resp}")
         print('\n----------------------------\n')