Index-1.9B-Chat

Index-1.9B-Chat is a dialogue generation model based on 1.9 billion parameters. It uses SFT and DPO alignment technology and combines RAG to realize fewshots role-play customization, which has high dialogue fun and customization. The model was pre-trained on 2.8T corpus of mainly Chinese and English, and performed well on multiple evaluation benchmarks.

Demand group:

" Index-1.9B-Chat model is suitable for developers and enterprises that need to generate high-quality conversational content, such as chatbot developers, content creators, etc. It can help users quickly generate interesting and natural conversations, improve product interactivity and user experience."

Example of usage scenario:

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

The enterprise customer service system integrates this model to automatically generate answers and improve service efficiency.

Product features:

Supports the generation of a variety of dialogue scenes, which is highly interesting

Pre-training based on a large amount of Chinese and English corpora, with extensive language understanding capabilities

Model alignment through SFT and DPO technology to optimize dialogue generation effects

Introducing RAG technology to realize role-play customization and provide personalized dialogue experience

Adapted to llamacpp and Ollama, with good hardware compatibility

Provide detailed technical reports and GitHub resources to facilitate users to learn and use

Usage tutorial:

1. Install necessary Python libraries such as transformers and PyTorch.

2. Import the AutoTokenizer and pipeline modules.

3. Set the model path and device type.

4. Use AutoTokenizer.from_pretrained to load the tokenizer of the model.

5. Create a text-generation pipeline through pipeline.

6. Prepare system messages and user queries, and build the model_input array.

7. Use generator to generate dialogue and set parameters such as max_new_tokens, top_k, etc.

8. Print the generated conversation results.