Update README.md

README.md

@@ -1,21 +1,14 @@
 ---
-frameworks:
-- Pytorch
-license: apache-2.0
-tasks:
-- text-generation
+license: other
+tasks: 
+- code-generation
 ---
 # Model Card for CodeFuse-DeepSeek-33B
-<p align="center">
-    <img src="https://modelscope.cn/api/v1/models/codefuse-ai/CodeFuse-DeepSeek-33B/repo?Revision=master&FilePath=LOGO.jpg&View=true" width="800"/>
-<p>
+![logo](LOGO.jpg)
 
 [[中文]](#chinese)    [[English]](#english)
 
-#### Clone with HTTP
-```bash
- git clone https://www.modelscope.cn/codefuse-ai/CodeFuse-DeepSeek-33B.git
-```
+
 
 <a id="english"></a>
 
@@ -27,7 +20,7 @@ CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-rela
 
 ## News and Updates
 
-🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achiving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
+🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B has been released, achieving a pass@1 (greedy decoding) score of 78.65% on HumanEval.
 
   🔥🔥 2023-11-10 CodeFuse-CodeGeeX2-6B has been released, achieving a pass@1 (greedy decoding) score of 45.12% on HumanEval, which is a 9.22% increase compared to CodeGeeX2 35.9%.
 
@@ -39,7 +32,7 @@ CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-rela
 
 🔥🔥🔥 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.
 
-🔥🔥🔥 2023-09-11 [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://modelscope.cn/models/codefuse-ai/CodeFuse-CodeLlama-34B/summary) has achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
 
 <br>
 
@@ -133,10 +126,10 @@ In this format, the system section is optional and the conversation can be eithe
 
 ```python
 import torch
-from modelscope import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, snapshot_download
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
  
 def load_model_tokenizer(model_path):
-    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False, legacy=False)
+    tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B", trust_remote_code=True, use_fast=False, legacy=False)
     tokenizer.eos_token = "<｜end▁of▁sentence｜>"
     tokenizer.pad_token = "<｜end▁of▁sentence｜>"
     tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
@@ -295,10 +288,10 @@ User prompt...
 
 ```python
 import torch
-from modelscope import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, snapshot_download
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
  
 def load_model_tokenizer(model_path):
-    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False, legacy=False)
+    tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B", trust_remote_code=True, use_fast=False, legacy=False)
     tokenizer.eos_token = "<｜end▁of▁sentence｜>"
     tokenizer.pad_token = "<｜end▁of▁sentence｜>"
     tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)