Update README.md

README.md

@@ -1,50 +1,55 @@
 ---
-license: other
-license_name: llama3
-license_link: LICENSE
+license: llama3
 library_name: transformers
+pipeline_tag: text-generation
 base_model: meta-llama/Meta-Llama-3-8B-Instruct
 language:
 - en
 - zh
-pipeline_tag: text-generation
 tags:
 - llama-factory
 - orpo
 ---
 
-This model is developed by [Shenzhi Wang](https://shenzhi-wang.netlify.app) (王慎执) and [Yaowei Zheng](https://github.com/hiyouga) (郑耀威).
-
 🌟 We included all instructions on how to download, use, and reproduce our various kinds of models at [this GitHub repo](https://github.com/Shenzhi-Wang/Llama3-Chinese-Chat). If you like our models, we would greatly appreciate it if you could star our Github repository. Additionally, please click "like" on our HuggingFace repositories. Thank you!
 
+# Updates
 
-# Updates:
 - 🔥 We provide an online interactive demo for Llama3-8B-Chinese-Chat-v2 [here](https://huggingface.co/spaces/llamafactory/Llama3-8B-Chinese-Chat). Have fun with our latest model!
-- 🚀🚀🚀 [Apr. 29, 2024] We now introduce Llama3-8B-Chinese-Chat-**v2**! Compared to v1, the training dataset of v2 is **5 times larger** (~100K preference pairs), and it exhibits significant enhancements, especially in **roleplay**, **function calling**, and **math** capabilities! The training dataset of Llama3-8B-Chinese-Chat-v2 will be released soon. If you love our Llama3-8B-Chinese-Chat-v1, you won't want to miss out on Llama3-8B-Chinese-Chat-v2!
+- 🚀🚀🚀 [Apr. 29, 2024] We now introduce Llama3-8B-Chinese-Chat-**v2**! Compared to v1, the training dataset of v2 is **5x larger** (~100K preference pairs), and it exhibits significant enhancements, especially in **roleplay**, **function calling**, and **math** capabilities! The training dataset of Llama3-8B-Chinese-Chat-v2 will be released soon. If you love our Llama3-8B-Chinese-Chat-v1, you won't want to miss out on Llama3-8B-Chinese-Chat-v2!
 
 The following are updates for [Llama3-8B-Chinese-Chat-v1](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat/tree/v1):
+
 - 🔥 We provide the official Ollama model for the FP16 GGUF version of Llama3-8B-Chinese-Chat at [wangshenzhi/llama3-8b-chinese-chat-ollama-fp16](https://ollama.com/wangshenzhi/llama3-8b-chinese-chat-ollama-fp16)! Run the following command for quick use of this model: `ollama run wangshenzhi/llama3-8b-chinese-chat-ollama-fp16`.
 - 🔥 We provide the official Ollama model for the 8bit-quantized GGUF version of Llama3-8B-Chinese-Chat at [wangshenzhi/llama3-8b-chinese-chat-ollama-q8](https://ollama.com/wangshenzhi/llama3-8b-chinese-chat-ollama-q8)! Run the following command for quick use of this model: `ollama run wangshenzhi/llama3-8b-chinese-chat-ollama-q8`.
 - 🔥 We provide the official FP16 GGUF version of Llama3-8B-Chinese-Chat at [shenzhi-wang/Llama3-8B-Chinese-Chat-GGUF-fp16](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat-GGUF-fp16)!
 - 🔥 We provide the official 8bit-quantized GGUF version of Llama3-8B-Chinese-Chat at [shenzhi-wang/Llama3-8B-Chinese-Chat-GGUF-8bit](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat-GGUF-8bit)!
 - 🌟 If you are in China, you can download our model from our [Gitee AI repository](https://ai.gitee.com/hf-models/shenzhi-wang/Llama3-8B-Chinese-Chat).
 
-# 1. Introduction
+# Model Summary
 
-❗️❗️❗️NOTICE: The main branch contains the files for Llama3-8B-Chinese-Chat-**v2**, if you want to use our Llama3-8B-Chinese-Chat-**v1**, please refer to [the `v1` branch](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat/tree/v1).
+Llama3-8B-Chinese-Chat is an instruction-tuned language model for Chinese & English users with various abilities such as roleplaying & tool-using built upon the Meta-Llama-3-8B-Instruct model.
 
+Developed by: [Shenzhi Wang](https://shenzhi-wang.netlify.app) (王慎执) and [Yaowei Zheng](https://github.com/hiyouga) (郑耀威)
 
-This is the first Chinese chat model specifically fine-tuned for Chinese through ORPO [1] based on the [Meta-Llama-3-8B-Instruct model](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
+- License: [Llama-3 License](https://llama.meta.com/llama3/license/)
+- Base Model: Meta-Llama-3-8B-Instruct
+- Model Size: 8.02B
+- Context length: 8K
+
+# 1. Introduction
 
-**Compared to the original [Meta-Llama-3-8B-Instruct model](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), our Llama3-8B-Chinese-Chat-v1 model significantly reduces the issues of "Chinese questions with English answers" and the mixing of Chinese and English in responses. Additionally, compared to the original model, our model greatly reduces the number of emojis in the answers, making the responses more formal.**
+❗️❗️❗️NOTICE: The main branch contains the files for Llama3-8B-Chinese-Chat-**v2**, if you want to use our Llama3-8B-Chinese-Chat-**v1**, please refer to [the `v1` branch](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat/tree/v1).
 
-**Compared to [Llama3-8B-Chinese-Chat-v1](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat/tree/v1), our Llama3-8B-Chinese-Chat-v2 model significantly increases the training data size (from 20K to 100K), which introduces great performance enhancement, especially in roleplay, function calling, and math.**
+This is the first model specifically fine-tuned for Chinese & English user through ORPO [1] based on the [Meta-Llama-3-8B-Instruct model](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
 
-[1] Hong, Jiwoo, Noah Lee, and James Thorne. "Reference-free Monolithic Preference Optimization with Odds Ratio." arXiv preprint arXiv:2403.07691 (2024).
+**Compared to the original [Meta-Llama-3-8B-Instruct model](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), our Llama3-8B-Chinese-Chat-v1 model significantly reduces the issues of "Chinese questions with English answers" and the mixing of Chinese and English in responses.**
 
+**Compared to [Llama3-8B-Chinese-Chat-v1](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat/tree/v1), our Llama3-8B-Chinese-Chat-v2 model significantly increases the training data size (from 20K to 100K), which introduces great performance enhancement, especially in roleplay, tool using, and math.**
 
-Training framework: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory/tree/main) (commit id: 32347901d4af94ccd72b3c7e1afaaceb5cb3d26a).
+[1] Hong, Jiwoo, Noah Lee, and James Thorne. "Reference-free Monolithic Preference Optimization with Odds Ratio." arXiv preprint arXiv:2403.07691 (2024).
 
+Training framework: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory).
 
 Training details:
 - epochs: 3
@@ -57,6 +62,8 @@ Training details:
 - fine-tuning type: full parameters
 - optimizer: paged_adamw_32bit
 
+<details>
+<summary>To reproduce the model</summary>
 
 To reproduce Llama3-8B-Chinese-Chat-**v2** (to reproduce Llama3-8B-Chinese-Chat-**v1**, please refer to [this link](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat/blob/v1/README.md#1-introduction)):
 
@@ -99,6 +106,8 @@ deepspeed --num_gpus 8 src/train_bash.py \
     --optim paged_adamw_32bit
 ```
 
+</details>
+
 # 2. Usage
 
 ```python
@@ -112,7 +121,6 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 
 messages = [
-    {"role": "system", "content": "You are Llama3-8B-Chinese-Chat-v2, which is finetuned on Llama3-8B-Instruct with Chinese-English mixed data by the ORPO alignment algorithm. You are a helpful assistant."},
     {"role": "user", "content": "介绍一下你自己"},
 ]
 
@@ -122,7 +130,7 @@ input_ids = tokenizer.apply_chat_template(
 
 outputs = model.generate(
     input_ids,
-    max_new_tokens=8196,
+    max_new_tokens=8192,
     do_sample=True,
     temperature=0.6,
     top_p=0.9,
@@ -134,6 +142,7 @@ print(tokenizer.decode(response, skip_special_tokens=True))
 # 3. Examples
 
 The following are some examples generated by Llama3-8B-Chinese-Chat-**v2**, including examples of roleplay, function calling, math, RuoZhiBa (弱智吧), safety, writing, and coding, etc. 
+
 For the examples generated by Llama3-8B-Chinese-Chat-**v1**, please refer to [this link](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat/blob/v1/README.md#3-examples).
 
 ---
@@ -172,8 +181,6 @@ To my dear fans, I'd sing a song of gratitude, for the love and light you bring
 
 ---
 
-
-
 **User:** 
 
 ```
@@ -386,13 +393,12 @@ def directly_answer():
     pass
 ```
 
-Write 'Action:' followed by a JSON in the following format. e.g.
+Write 'Action:' followed by a list of actions in JSON that you want to call, e.g.
 Action:
-
 ```json
 [
     {
-        "tool_name": "title of the tool in the specification",
+        "tool_name": "tool name (one of [internet_search, directly_answer])",
         "parameters": "the input to the tool"
     }
 ]
@@ -404,7 +410,6 @@ What's the biggest penguin in the world?
 **Assistant:**
 
 Action:
-
 ```json
 [
     {
@@ -421,45 +426,51 @@ Action:
 **User:**
 
 ````
-用中文给刘总发个邮件，需要遵循标准邮件的格式，告诉他我们对于进货价的要求是不能超过100元每件。
 
+You have access to the following tools:
 ```json
-[{
- "name": "send_email",
- "description": "send email assistant",
- "parameters": {
-  "type": "object",
-  "properties": {
-   "receiver": {
-    "type": "string",
-    "description": "email receiver"
-   },
-   "content": {
-    "type": "string",
-    "description": "email content"
-   }
-  },
-  "required": ["receiver", "content"]
- }
-}]
-```
-
-请按以下格式写出以“Action:”开头的 JSON。例如：
-Action:
+[
+    {
+        "name": "send_email",
+        "description": "send email assistant",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "receiver": {
+                    "type": "string",
+                    "description": "email receiver"
+                },
+                "content": {
+                    "type": "string",
+                    "description": "email content"
+                }
+            },
+            "required": [
+                "receiver", "content"
+            ]
+        }
+    }
+]
+```
 
+Write 'Action:' followed by a list of actions in JSON that you want to call, e.g.
+Action:
 ```json
 [
     {
-        "tool_name": "title of the tool in the specification",
+        "tool_name": "tool name (one of [internet_search, directly_answer])",
         "parameters": "the input to the tool"
     }
 ]
 ```
+
+用中文给刘总发个邮件，需要遵循标准邮件的格式，告诉他我们对于进货价的要求是不能超过100元每件。
+
 ````
 
 **Assistant:**
-Action:
 
+Action:
 ```json
 [
     {
@@ -966,4 +977,4 @@ int main() {
 
 在`main`函数中，我们提供了一个例子，搜索文本`"ABC ABCDAB ABCDABCDABDE"`中的模式`"ABCDABD"`。如果找到匹配，输出将是`"Pattern found at index"`，随后是匹配的起始索引。如果没有找到匹配，输出将是`"Pattern not found"`。
 
-请注意，这段代码假设输入文本和模式字符串只包含小写英文字母和中文字符。如果需要处理其他字符集，可能需要适当调整。
+请注意，这段代码假设输入文本和模式字符串只包含小写英文字母和中文字符。如果需要处理其他字符集，可能需要适当调整。