Update README.md

README.md

@@ -10,11 +10,6 @@ tasks:
 
 [[中文]](#chinese)    [[English]](#english)
 
-#### Clone with HTTP
-```bash
- git clone https://www.modelscope.cn/codefuse-ai/CodeFuse-QWen-14B.git
-```
-
 <a id="english"></a>
 
 ## Model Description
@@ -29,9 +24,9 @@ CodeFuse-QWen-14B is a 14B Code-LLM finetuned by QLoRA of multiple code tasks on
 
   🔥🔥 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://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary) 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://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.
 
-🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary) has achived 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
+🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama34B](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B) has achived 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
 
 <br>
 
@@ -98,20 +93,17 @@ Bot 2nd round output<|endoftext|>
 ...
 ...
 <s>human
-Human nth round input
+Human n-th round input
 <s>bot
 {Bot output to be genreated}<|endoftext|>
 """
 ```
 
-When applying inference, you always make your input string end with "\<s\>bot" to ask the model generating answers.
+When applying inference, you always make your input string end with "\<s\>bot" to ask the model to generate answers.
 
 
 ## Quickstart
 
-```bash
- git clone https://www.modelscope.cn/codefuse-ai/CodeFuse-QWen-14B.git
-```
 
 ```bash
 pip install -r requirements.txt
@@ -119,13 +111,11 @@ pip install -r requirements.txt
 
 ```python
 import torch
-from modelscope import (
+from transformers import (
     AutoTokenizer, 
-    AutoModelForCausalLM,
-    snapshot_download
+    AutoModelForCausalLM
 )
-model_dir = snapshot_download('codefuse-ai/CodeFuse-QWen-14B',revision = 'v1.0.0')
-tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('codefuse-ai/CodeFuse-QWen-14B', trust_remote_code=True)
 tokenizer.padding_side = "left"
 tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("<|endoftext|>")
 tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("<|endoftext|>")
@@ -178,9 +168,9 @@ CodeFuse-QWen-14B 是一个通过QLoRA对基座模型QWen-14B进行多代码任
 
   🔥🔥 2023-09-27开源了CodeFuse-StarCoder-15B模型，在HumanEval pass@1(greedy decoding)上可以达到54.9%, 比StarCoder提高了21%的代码能力（HumanEval）
 
-🔥🔥🔥 2023-09-26 [CodeFuse-CodeLlama-34B 4bits](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B-4bits/summary)量化版本发布，量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
+🔥🔥🔥 2023-09-26 [CodeFuse-CodeLlama-34B 4bits](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B-4bits)量化版本发布，量化后模型在HumanEval pass@1指标为73.8% (贪婪解码)。
 
-🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama-34B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary)发布，HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
+🔥🔥🔥 2023-09-11 [CodeFuse-CodeLlama-34B](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B)发布，HumanEval pass@1指标达到74.4% (贪婪解码), 为当前开源SOTA。
 
 <br>
 
@@ -255,9 +245,6 @@ CodeFuse-QWen-14B 是一个通过QLoRA对基座模型QWen-14B进行多代码任
 
 ## 快速使用
 
-```bash
- git clone https://www.modelscope.cn/codefuse-ai/CodeFuse-QWen-14B.git
-```
 
 ```bash
 pip install -r requirements.txt
@@ -265,13 +252,11 @@ pip install -r requirements.txt
 
 ```python
 import torch
-from modelscope import (
+from transformers import (
     AutoTokenizer, 
-    AutoModelForCausalLM,
-    snapshot_download
+    AutoModelForCausalLM
 )
-model_dir = snapshot_download('codefuse-ai/CodeFuse-QWen-14B',revision = 'v1.0.0')
-tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('codefuse-ai/CodeFuse-QWen-14B', trust_remote_code=True)
 tokenizer.padding_side = "left"
 tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("<|endoftext|>")
 tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("<|endoftext|>")