Update README.md

README.md

@@ -34,7 +34,7 @@ After undergoing 4-bit quantization, the CodeFuse-DeepSeek-33B-4bits model can b
 
   🔥🔥 2023-09-27 CodeFuse-StarCoder-15B has been released, achieving a pass@1 (greedy decoding) score of 54.9% on HumanEval, which is a 21% increase compared to StarCoder's 33.6%.
 
-🔥🔥🔥 2023-09-26 We are pleased to announce the release of the [4-bit quantized version](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B-4bits) of [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary). Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
+🔥🔥🔥 2023-09-26 We are pleased to announce the release of the [4-bit quantized version](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B-4bits) of [CodeFuse-CodeLlama-34B](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B). Despite the quantization process, the model still achieves a remarkable 73.8% accuracy (greedy decoding) on the HumanEval pass@1 metric.
 
 🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama34B](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B-4bits) has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for open-sourced LLMs at present.
 
@@ -162,7 +162,7 @@ os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 def load_model_tokenizer(model_path):
     """
-    Load model and tokenizer based on the given model name or local path of downloaded model.
+    Load model and tokenizer based on the given model name or local path of the downloaded model.
     """
     tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B-4bits", 
                                               trust_remote_code=True, 
@@ -396,7 +396,7 @@ def load_model_tokenizer(model_path):
 
 def inference(model, tokenizer, prompt):
     """
-    Uset the given model and tokenizer to generate an answer for the speicifed prompt.
+    Uset the given model and tokenizer to generate an answer for the specified prompt.
     """
     st = time.time()
     prompt = prompt if prompt.endswith('\n') else f'{prompt}\n'