feat: update model

README.md

@@ -1,201 +0,0 @@
----
-license: gemma
-widget:
-- messages:
-  - role: user
-    content: 大脑是如何工作的?
-inference:
-  parameters:
-    max_new_tokens: 200
-language:
-- en
-- zh
-library_name: transformers
-pipeline_tag: text-generation
-tags:
-- gemma
-- chinese
-- sft
----
-
-# Updates
-- [May 23, 2024] 🔥 support function calling
-
-
-# Model Summary
-
-[Gemma-1.1-7B-Chinese-Chat](https://huggingface.co/ycjcl868/Gemma-1.1-7B-Chinese-Chat) is an instruction-tuned language model for Chinese & English users built upon the gemma-1.1-7b-it model.([Github](https://github.com/ycjcl868/Gemma-1.1-7B-Chinese-Chat/tree/main))
-
-Developed by: [ycjcl868](https://github.com/ycjcl868)
-
-- License: [Gemma License](https://www.kaggle.com/models/google/gemma/license/consent)
-- Base Model: gemma-1.1-7b-it
-- Model Size: 8.54B
-- Context length: 8K
-
-# Introduction
-
-This is the first model specifically fine-tuned for Chinese & English user through SFT based on the [gemma-1.1-7b-it model](https://huggingface.co/google/gemma-1.1-7b-it).
-
-**Compared to the original [gemma-1.1-7b-it model](https://huggingface.co/google/gemma-1.1-7b-it), our Gemma-1.1-7B-Chinese-Chat model significantly reduces the issues of "Chinese questions with English answers" and the mixing of Chinese and English in responses.**
-
-Training details:
-
-- epochs: 3
-- learning rate: 0.0001
-- learning rate scheduler type: cosine
-- Warmup ratio: 0.1
-- cutoff len (i.e. context length): 8192
-- global batch size: 2
-
-<br />
-
-# Usage
-
-## Chat Template
-
-The instruction-tuned models use a chat template that must be adhered to for conversational use.
-The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
-
-Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
-
-```py
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import transformers
-import torch
-
-model_id = "ycjcl868/Gemma-1.1-7B-Chinese-Chat"
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    device_map="auto",
-    torch_dtype="auto",
-)
-
-messages = [
-    { "role": "user", "content": "写一首诗" },
-]
-input_ids = tokenizer.apply_chat_template(
-    messages, add_generation_prompt=True, return_tensors="pt"
-).to(model.device)
-
-outputs = model.generate(
-    input_ids,
-    max_new_tokens=8192,
-    do_sample=True,
-    temperature=0.6,
-    top_p=0.9,
-)
-response = outputs[0][input_ids.shape[-1]:]
-print(tokenizer.decode(response, skip_special_tokens=True))
-
-# 春风拂柳，春雨轻轻，
-# 花香满园，鸟鸣清脆。
-# 一朵红梅，红彤彤的，
-# 美不胜收，美不胜收。
-```
-
-# Examples
-
-```
-User: 参加四年级数学竞赛同学中，男同学获奖人数比女同学多2人，女同学获奖人数比男同学人数的一半多2人，男女同学各有多少人获奖?
-Assistant:
-设男同学的获奖人数
-为x，则女同学的获奖人数为y，所以有以下两式：
-x=y+2
-y=2(x+2)
-将第一式代入第二式中，得：
-y=2(y+2)+2
-y=2y+4+2
-y=2y+6
-2y=y+6
-y=6
-因此，女同学获奖人数为6人，男同学获奖人数为8人。
-```
-
-```
-User: 我的蓝牙耳机坏了，我应该去看耳鼻喉科还是牙科？
-Assistant: 蓝牙耳机是属于电子产品的，而不是耳鼻喉科或牙科的范畴。如果你怀疑耳机有故障，建议去电子产品维修店或者原厂维修中心进行检查。如果需要新的耳机，你也可以去电器商店或电子产品市场购买。
-```
-
-```
-User: 来几句成语
-Assistant: “一日三尺”是指过时间一切都会改变。
-```
-
-```
-User: 列出5种可以改善睡眠质量的方法
-Assistant:
-1. 保持规律的睡眠时间表
-2. 避免下午摄入咖啡因
-3. 避免睡前摄入酒精
-4. 避免睡前摄入高糖饮食
-5. 每天至少进行30分钟的运动
-```
-
-```
-User: 回答一个开放性的问题：人类存在的意义是什么？
-Assistant: 人类存在的意义是一个复杂的哲学和哲学问题，因为人类存在于自然界中，我们作为人类有责任和义务来保护地球和生物，而我们作为人类也有责任来发展自己的精神和思想，来创造更好的未来。
-我从哲学角度来看，人类存在的意义是通过我们自己的思想和行动，来实现我们对精神和精神的追求。通过我们自己的努力和创造力，我们可以创造出更多的艺术、文化和思想，来促进人类的进步和发展。
-```
-
-```
-User: 解释以下缩写： JSON、CEO、PDF、HTML、NASA
-Assistant:
-JSON（JavaScript Object Notation）：JavaScript对象的表示法，用来在JavaScript和许多其他编程语言之间传递数据。
-CEO（Chief Executive Officer）：首席执行官，指企业的高级管理人员，通常是指企业的主席或总裁。
-PDF（Portable Document Format）：可移植文档格式，是一种电子文档格式，可以确保文档在不同的操作系统���设备上都能以一致的方式呈现。
-HTML（HyperText Markup Language）：超文本标记语言，是网页内容的标记语言，用来定义网页的结构和内容。
-NASA（National Aeronautics and Space Administration）：美国国家航空航天局，是美国政府的宇航机构，负责美国在太空和航空方面的研究和发展。
-```
-
-## Function call
-
-**User**:
-````
-以下是您可以使用的工具列表:
-
-```python
-def internet_search(query: str):
-    \"\"\"
-    Returns a list of relevant document snippets for a textual query retrieved from the internet
-
-    Args:
-        query (str): Query to search the internet with
-    \"\"\"
-    pass
-```
-
-```python
-def directly_answer():
-    \"\"\"
-    Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history
-    \"\"\"
-    pass
-```
-
-写 'Action:'，后跟要调用的 JSON 中的操作列表，例如.
-Action:
-```json
-[
-    {
-        "tool_name": "tool name (one of [internet_search, directly_answer])",
-        "parameters": "the input to the tool"
-    }
-]
-```
-
-帮我找到今天的新闻有哪些:
-````
-
-**Response**:
-```
-Action:
-[
-  {
-    "tool_name": "internet_search", 
-    "parameters": "今天有哪些新闻"
-  }
-]
-```

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/root/llms/gemma-1-1-7b-instruct/gemma-1-1-7b-it",
+  "_name_or_path": "google/gemma-1.1-7b-it",
   "architectures": [
     "GemmaForCausalLM"
   ],
@@ -22,7 +22,7 @@
   "rms_norm_eps": 1e-06,
   "rope_theta": 10000.0,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.41.0",
+  "transformers_version": "4.41.2",
   "use_cache": true,
   "vocab_size": 256000
 }

generation_config.json

@@ -3,5 +3,5 @@
   "bos_token_id": 2,
   "eos_token_id": 1,
   "pad_token_id": 0,
-  "transformers_version": "4.41.0"
+  "transformers_version": "4.41.2"
 }

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