From 075648cd70140e56dc8fd8f9cea6b4f2d113a7a4 Mon Sep 17 00:00:00 2001
From: Kai Chen <chenkaidev@gmail.com>
Date: Tue, 22 Aug 2023 08:08:44 +0800
Subject: [PATCH] update readme related to internlm-chat-7v-v1.1 (#214)

---
 README-ja-JP.md   | 8 ++++++--
 README-zh-Hans.md | 4 ++--
 README.md         | 7 +++----
 3 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/README-ja-JP.md b/README-ja-JP.md
index 1a01d8f..6c3dab4 100644
--- a/README-ja-JP.md
+++ b/README-ja-JP.md
@@ -40,6 +40,10 @@ InternLM は、70 億のパラメータを持つベースモデルと、実用
 
 さらに、大規模な依存関係を必要とせずにモデルの事前学習をサポートする軽量な学習フレームワークが提供されます。単一のコードベースで、数千の GPU を持つ大規模クラスタでの事前学習と、単一の GPU での微調整をサポートし、顕著な性能最適化を達成します。InternLM は、1024GPU でのトレーニングにおいて 90% 近いアクセラレーション効率を達成しています。
 
+## 新闻
+
+InternLM-7B-Chat v1.1 は、コード インタプリタと関数呼び出し機能を備えてリリースされました。 [Lagent](https://github.com/InternLM/lagent) で試すことができます。
+
 ## InternLM-7B
 
 ### パフォーマンス評価
@@ -80,8 +84,8 @@ Transformers を使用して InternLM 7B チャットモデルをロードする
 
 ```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-chat-7b-v1.1", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b-v1.1", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> response, history = model.chat(tokenizer, "こんにちは", history=[])
 >>> print(response)
diff --git a/README-zh-Hans.md b/README-zh-Hans.md
index e7c0a45..75c362b 100644
--- a/README-zh-Hans.md
+++ b/README-zh-Hans.md
@@ -90,8 +90,8 @@ InternLM ，即书生·浦语大模型，包含面向实用场景的70亿参数
 
 ```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-chat-7b-v1.1", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b-v1.1", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> response, history = model.chat(tokenizer, "你好", history=[])
 >>> print(response)
diff --git a/README.md b/README.md
index b7cd781..78116f8 100644
--- a/README.md
+++ b/README.md
@@ -47,8 +47,7 @@ Additionally, a lightweight training framework is offered to support model pre-t
 
 ## News
 
-InternLM-7B-Chat v1.1 is released with code interpreter and function calling capability. You can try it with [Lagent](https://github.com/InternLM/lagent)
--
+InternLM-7B-Chat v1.1 is released with code interpreter and function calling capability. You can try it with [Lagent](https://github.com/InternLM/lagent).
 
 ## InternLM-7B
 
@@ -91,8 +90,8 @@ 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-chat-7b-v1.1", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b-v1.1", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> response, history = model.chat(tokenizer, "hello", history=[])
 >>> print(response)