Upload 2 files

README.md

@@ -9,6 +9,15 @@ license_link: LICENSE
 </h1>
 </div>
 
+## Model Introduction
+
+We are excited to announce the release of a lightweight version from the Index series models: the Index-1.9B series.
+ The open-source Index-1.9B series includes the following models:
+- Index-1.9B base: The base model, with 1.9 billion non-embedding parameters, pre-trained on a 2.8T corpus mainly in Chinese and English. It leads in multiple evaluation benchmarks compared to models of the same level. 
+- Index-1.9B pure : A control version of the base model with the same parameters and training strategy, but strictly filtered out all instruction-related data from the corpus to verify the impact of instructions on benchmarks.
+- **Index-1.9B chat (this repository's model)** : A dialogue model aligned with SFT and DPO based on the Index-1.9B base. We found that due to the introduction of a lot of internet community corpus in our pre-training, the model has significantly more interesting chatting capabilities.
+- Index-1.9B character : Introduces RAG on top of SFT and DPO to achieve few-shots role-playing customization.
+
 ## 模型介绍
 
 我们很高兴首次发布Index系列模型中的轻量版本：Index-1.9B系列
@@ -18,13 +27,13 @@ license_link: LICENSE
 - **Index-1.9B chat(本仓库模型)** : 基于index-1.9B base通过SFT和DPO对齐后的对话模型，我们发现由于预训练中引入了较多定向清洗的对话类语料，聊天的趣味性明显更强。
 - Index-1.9B character : 在SFT和DPO的基础上引入了RAG来实现fewshots角色扮演定制。
 
-已适配llamacpp和Ollama，详见[Index-1.9B-Chat-GGUF](https://huggingface.co/IndexTeam/Index-1.9B-Chat-GGUF)
+Adapted to llamacpp and Ollama, see [Index-1.9B-Chat-GGUF](https://huggingface.co/IndexTeam/Index-1.9B-Chat-GGUF)
 
-更多细节详见我们的[GitHub](https://github.com/bilibili/Index-1.9B)和[Index-1.9B技术报告](https://github.com/bilibili/Index-1.9B/blob/main/Index-1.9B%20%E6%8A%80%E6%9C%AF%E6%8A%A5%E5%91%8A.pdf)
+For more details, see our [GitHub](https://github.com/bilibili/Index-1.9B) and [Index-1.9B Technical Report](https://github.com/bilibili/Index-1.9B/blob/main/Index-1.9B%20%E6%8A%80%E6%9C%AF%E6%8A%A5%E5%91%8A.pdf)
 
-### Transformers 加载方式
+### Loading with Transformers
 
-可通过以下代码加载 Index-1.9B-Chat 模型来进行对话：
+You can load the Index-1.9B-Chat model for dialogue using the following code:
 
 ```python
 import argparse

README_zh.md

@@ -0,0 +1,56 @@
+---
+license: other
+license_name: license
+license_link: LICENSE
+---
+<div align="center">
+<h1>
+  Index-1.9B-Chat
+</h1>
+</div>
+
+## 模型介绍
+
+我们很高兴首次发布Index系列模型中的轻量版本：Index-1.9B系列
+ 本次开源的Index-1.9B 系列包含以下模型： 
+- Index-1.9B base : 基座模型，具有 19亿 非词嵌入参数量，在2.8T 中英文为主的语料上预训练，多个评测基准上与同级别模型比处于领先。
+- Index-1.9B pure : 基座模型的对照组，与base具有相同的参数和训练策略，不同之处在于我们严格过滤了该版本语料中所有指令相关的数据，以此来验证指令对benchmark的影响。
+- **Index-1.9B chat(本仓库模型)** : 基于index-1.9B base通过SFT和DPO对齐后的对话模型，我们发现由于预训练中引入了较多定向清洗的对话类语料，聊天的趣味性明显更强。
+- Index-1.9B character : 在SFT和DPO的基础上引入了RAG来实现fewshots角色扮演定制。
+
+已适配llamacpp和Ollama，详见[Index-1.9B-Chat-GGUF](https://huggingface.co/IndexTeam/Index-1.9B-Chat-GGUF)
+
+更多细节详见我们的[GitHub](https://github.com/bilibili/Index-1.9B)和[Index-1.9B技术报告](https://github.com/bilibili/Index-1.9B/blob/main/Index-1.9B%20%E6%8A%80%E6%9C%AF%E6%8A%A5%E5%91%8A.pdf)
+
+### Transformers 加载方式
+
+可通过以下代码加载 Index-1.9B-Chat 模型来进行对话：
+
+```python
+import argparse
+from transformers import AutoTokenizer, pipeline
+
+# 注意！目录不能含有"."，可以替换成"_"
+parser = argparse.ArgumentParser()
+parser.add_argument('--model_path', default="IndexTeam/Index-1.9B-Chat", type=str, help="")
+parser.add_argument('--device', default="cpu", type=str, help="") # also could be "cuda" or "mps" for Apple silicon
+args = parser.parse_args()
+
+tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
+generator = pipeline("text-generation",
+                    model=args.model_path,
+                    tokenizer=tokenizer, trust_remote_code=True, 
+                    device=args.device)
+
+
+system_message = "你是由哔哩哔哩自主研发的大语言模型，名为“Index”。你能够根据用户传入的信息，帮助用户完成指定的任务，并生成恰当的、符合要求的回复。"
+query = "续写 天不生我金坷垃"
+model_input = []
+model_input.append({"role": "system", "content": system_message})
+model_input.append({"role": "user", "content": query})
+
+model_output = generator(model_input, max_new_tokens=300, top_k=5, top_p=0.8, temperature=0.3, repetition_penalty=1.1, do_sample=True)
+
+print('User:', query)
+print('Model:', model_output)
+```