Update README.md

README.md

@@ -13,7 +13,7 @@
     </sup>
     <div>&nbsp;</div>
   </div>
-  
+
 [![evaluation](https://github.com/InternLM/InternLM/assets/22529082/f80a2a58-5ddf-471a-8da4-32ab65c8fd3b)](https://github.com/internLM/OpenCompass/)
 
 [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new)
@@ -23,9 +23,8 @@
 
 ## Introduction
 
-InternLM has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics:
+InternLM has open-sourced a 7 billion parameter base model tailored for practical scenarios. The model has the following characteristics:
 - It leverages trillions of high-quality tokens for training to establish a powerful knowledge base.
-- It supports an 8k context window length, enabling longer input sequences and stronger reasoning capabilities.
 - It provides a versatile toolset for users to flexibly build their own workflows.
 
 ## InternLM-7B
@@ -57,34 +56,18 @@ We conducted a comprehensive evaluation of InternLM using the open-source evalua
 To load the InternLM 7B Chat model using Transformers, use the following code:
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
->>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
->>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).cuda()
+>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-7b", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-7b", trust_remote_code=True).cuda()
 >>> model = model.eval()
->>> response, history = model.chat(tokenizer, "hello", history=[])
->>> print(response)
-Hello! How can I help you today?
->>> response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history)
->>> print(response)
-Sure, here are three tips for effective time management:
-
-1. Prioritize tasks based on importance and urgency: Make a list of all your tasks and categorize them into "important and urgent," "important but not urgent," and "not important but urgent." Focus on completing the tasks in the first category before moving on to the others.
-2. Use a calendar or planner: Write down deadlines and appointments in a calendar or planner so you don't forget them. This will also help you schedule your time more effectively and avoid overbooking yourself.
-3. Minimize distractions: Try to eliminate any potential distractions when working on important tasks. Turn off notifications on your phone, close unnecessary tabs on your computer, and find a quiet place to work if possible.
-
-Remember, good time management skills take practice and patience. Start with small steps and gradually incorporate these habits into your daily routine.
-```
-
-### Dialogue
-
-You can interact with the InternLM Chat 7B model through a frontend interface by running the following code:
-```bash
-pip install streamlit==1.24.0
-pip install transformers==4.30.2
-streamlit run web_demo.py
+>>> inputs = tokenizer(["A beautiful flower"], return_tensors="pt")
+>>> for k,v in inputs.items():
+        inputs[k] = v.cuda()
+>>> gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.1}
+>>> output = model.generate(**inputs, **gen_kwargs)
+>>> print(output)
+<s> A beautiful flower box made of white rose wood. It is a perfect gift for weddings, birthdays and anniversaries.
+All the roses are from our farm Roses Flanders. Therefor you know that these flowers last much longer than those in store or online!</s>
 ```
-The effect is as follows
-
-![demo](https://github.com/InternLM/InternLM/assets/9102141/11b60ee0-47e4-42c0-8278-3051b2f17fe4)
 
 ## Open Source License
 
@@ -92,9 +75,8 @@ The InternLM weights are fully open for academic research and also allow commerc
 
 
 ## 简介
-InternLM ，即书生·浦语大模型，包含面向实用场景的70亿参数基础模型与对话模型 （InternLM-7B）。模型具有以下特点：
+InternLM ，即书生·浦语大模型，包含面向实用场景的70亿参数基础模型 （InternLM-7B）。模型具有以下特点：
 - 使用上万亿高质量预料，建立模型超强知识体系；
-- 支持8k语境窗口长度，实现更长输入与更强推理体验；
 - 通用工具调用能力，支持用户灵活自助搭建流程；
 
 ## InternLM-7B
@@ -125,30 +107,18 @@ InternLM ，即书生·浦语大模型，包含面向实用场景的70亿参数
 通过以下的代码加载 InternLM 7B Chat 模型
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
->>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
->>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).cuda()
+>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-7b", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-7b", trust_remote_code=True).cuda()
 >>> model = model.eval()
->>> response, history = model.chat(tokenizer, "你好", history=[])
->>> print(response)
-你好！有什么我可以帮助你的吗？
->>> response, history = model.chat(tokenizer, "请提供三个管理时间的建议。", history=history)
->>> print(response)
-当然可以！以下是三个管理时间的建议：
-1. 制定计划：制定一个详细的计划，包括每天要完成的任务和活动。这将有助于您更好地组织时间，并确保您能够按时完成任务。
-2. 优先级：将任务按照优先级排序，先完成最重要的任务。这将确保您能够在最短的时间内完成最重要的任务，从而节省时间。
-3. 集中注意力：避免分心，集中注意力完成任务。关闭社交媒体和电子邮件通知，专注于任务，这将帮助您更快地完成任务，并减少错误的可能性。
-```
-
-### 通过前端网页对话
-可以通过以下代码启动一个前端的界面来与 InternLM Chat 7B 模型进行交互
-```bash
-pip install streamlit==1.24.0
-pip install transformers==4.30.2
-streamlit run web_demo.py
+>>> inputs = tokenizer(["来到美丽的大自然，我们发现"], return_tensors="pt")
+>>> for k,v in inputs.items():
+        inputs[k] = v.cuda()
+>>> gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.1}
+>>> output = model.generate(**inputs, **gen_kwargs)
+>>> print(output)
+来到美丽的大自然，我们发现各种各样的花千奇百怪。有的颜色鲜艳亮丽,使人感觉生机勃勃；有的是红色的花瓣儿粉嫩嫩的像少女害羞的脸庞一样让人爱不释手．有的小巧玲珑; 还有的花瓣粗大看似枯黄实则暗藏玄机！
+不同的花卉有不同的“脾气”,它们都有着属于自己的故事和人生道理.这些鲜花都是大自然中最为原始的物种,每一朵都绽放出别样的美令人陶醉、着迷!
 ```
-效果如下 
-
-![效果](https://github.com/InternLM/InternLM/assets/9102141/11b60ee0-47e4-42c0-8278-3051b2f17fe4)
 
 ## 开源许可证