Update README.md

README.md

@@ -22,17 +22,19 @@ CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-rela
 
 🔥🔥🔥 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%.
+🔥🔥🔥 2024-01-12 CodeFuse-Mixtral-8x7B has been released, achieving a pass@1 (greedy decoding) score of 56.1% on HumanEval, which is a 15% increase compared to Mixtral-8x7b's 40%.
 
-  🔥🔥 2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw)
+🔥🔥 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%.
 
-  🔥🔥 2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
+🔥🔥 2023-10-20 CodeFuse-QWen-14B technical documentation has been released. For those interested, please refer to the CodeFuse article on our WeChat official account via the provided link.(https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw)
 
-  🔥🔥 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%.
+🔥🔥 2023-10-16 CodeFuse-QWen-14B has been released, achieving a pass@1 (greedy decoding) score of 48.78% on HumanEval, which is a 16% increase compared to Qwen-14b's 32.3%.
 
-🔥🔥🔥 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.
+🔥🔥 2023-09-27 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%.
 
-🔥🔥🔥 2023-09-11 [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.
+🔥🔥 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 achieved 74.4% of pass@1 (greedy decoding) on HumanEval, which is SOTA results for openspurced LLMs at present.
 
 <br>
 
@@ -54,8 +56,13 @@ CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-rela
 
 | Model                       | HumanEval(pass@1) |  Date   |
 |:----------------------------|:-----------------:|:-------:|
+| **CodeFuse-DeepSeek-33B**   |     **78.65%**    | 2024.01 |
+| **CodeFuse-Mixtral-8x7B**   |     **56.10%**    | 2024.01 |
 | **CodeFuse-CodeLlama-34B**  |     74.4%      | 2023.9  |
 |**CodeFuse-CodeLlama-34B-4bits** |     73.8%  |  2023.9 |
+| **CodeFuse-StarCoder-15B**  |     54.9%         | 2023.9  |
+| **CodeFuse-QWen-14B**       |     48.78%        | 2023.10 |
+| **CodeFuse-CodeGeeX2-6B**   |     45.12%        | 2023.11 |
 | WizardCoder-Python-34B-V1.0 |       73.2%       | 2023.8  |
 | GPT-4(zero-shot)            |       67.0%       | 2023.3  |
 | PanGu-Coder2 15B            |       61.6%       | 2023.8  |
@@ -65,10 +72,8 @@ CodeFuse-DeepSeek-33B is a 33B Code-LLM finetuned by QLoRA on multiple code-rela
 | OctoCoder                   |       46.2%       | 2023.8  |
 | StarCoder-15B               |       33.6%       | 2023.5  |
 | Qwen-14b                    |       32.3%       | 2023.10 |
-| **CodeFuse-StarCoder-15B**  |     54.9%         | 2023.9  |
-| **CodeFuse-QWen-14B**       |     48.78%        | 2023.10 |
-| **CodeFuse-CodeGeeX2-6B**   |     45.12%        | 2023.11 |
-| **CodeFuse-DeepSeek-33B**   |     **78.65%**    | 2024.01 |
+
+
 
 
 ### NLP
@@ -127,7 +132,7 @@ In this format, the system section is optional and the conversation can be eithe
 
 For example, the format used to infer HumanEval is like the following:
 
-```python
+```
 <s>human
 # language: Python
 from typing import List
@@ -153,7 +158,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
  
 def load_model_tokenizer(model_path):
-    tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B", trust_remote_code=True, use_fast=False, legacy=False)
+    tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B", trust_remote_code=True)
     tokenizer.eos_token = "<｜end▁of▁sentence｜>"
     tokenizer.pad_token = "<｜end▁of▁sentence｜>"
     tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
@@ -177,7 +182,7 @@ attention_mask = inputs["attention_mask"]
 generation_config = GenerationConfig(
         eos_token_id=tokenizer.eos_token_id,
         pad_token_id=tokenizer.pad_token_id,
-        temperature=0.2,
+        temperature=0.1,
         max_new_tokens=512,
         num_return_sequences=1,
         num_beams=1,
@@ -211,13 +216,13 @@ CodeFuse-DeepSeek-33B 是一个通过QLoRA对基座模型DeepSeek-Coder-33B进
 
 🔥🔥🔥 2024-01-12 CodeFuse-DeepSeek-33B模型发布，模型在HumanEval pass@1指标为78.65% (贪婪解码)。
 
-  🔥🔥 2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型，在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力（HumanEval）
+🔥🔥🔥 2023-11-10 开源了CodeFuse-CodeGeeX2-6B模型，在HumanEval pass@1(greedy decoding)上可以达到48.12%, 比CodeGeeX2提高了9.22%的代码能力（HumanEval）
 
-  🔥🔥 2023-10-20 公布了CodeFuse-QWen-14B技术文档，感兴趣详见微信公众号CodeFuse文章：https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw
+🔥🔥🔥 2023-10-20 公布了CodeFuse-QWen-14B技术文档，感兴趣详见微信公众号CodeFuse文章：https://mp.weixin.qq.com/s/PCQPkvbvfxSPzsqjOILCDw
 
-  🔥🔥 2023-10-16开源了CodeFuse-QWen-14B模型，在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力（HumanEval）
+🔥🔥🔥 2023-10-16开源了CodeFuse-QWen-14B模型，在HumanEval pass@1(greedy decoding)上可以达到48.78%, 比Qwen-14b提高了16%的代码能力（HumanEval）
 
-  🔥🔥 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）
+🔥🔥🔥 2023-09-27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)。