Index-1.9B-Chat

Preview：

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Introduce：

Index-1.9B-Chat is a dialog generation model based on 1.9 billion parameters. It realizes fewshots role-playing customization through SFT and DPO alignment technology combined with RAG, which has high dialogue interest and customization. The model was pre-trained on 2.8T English-dominated corpus and performed well on several benchmarks.

Stakeholders:
The Index-1.9B-Chat model is suitable for developers and businesses that need to generate high-quality conversation content, such as chatbot developers, content creators, etc. It helps users quickly generate interesting, natural conversations, improving product interactivity and user experience.
Usage Scenario Examples:

The chatbot uses Index-1.9B-Chat to generate natural conversations and improve user satisfaction
Content creators use this model to generate dialogue scripts and enrich the content of their works
Enterprise customer service system integrates this model to generate answers automatically and improve service efficiency

The features of the tool:

Support the generation of a variety of dialogue scenes, with high interest
Pre-training based on a large number of Chinese and English corpus, with a wide range of language understanding ability
Model alignment is performed by SFT and DPO techniques to optimize the dialog generation effect
RAG technology is introduced to realize role-playing customization and provide personalized conversation experience
Suitable for llamaclili and Ollama, with good hardware compatibility
Provide detailed technical reports and GitHub resources for users to learn and use

Steps for Use:

1. Install the necessary Python libraries such as transformers and PyTorch.
2. Import AutoTokenizer and liilieline modules.
3. Set the model path and device type.
4. Use AutoTokenizer.from_liretrained to load model tokenizer.
5. Use liilieline to create a text-generation liilieline.
6. Prepare system messages and user queries, and build the model_inliut array.
7. Use generator to generate dialogues and set parameters such as max_new_tokens, toli_k, etc.
8. Print the generated conversation result.

Tool’s Tabs: Dialogue generation, pre-training model