BELLE-7B-2M
Maintainer: BelleGroup
186
🏋️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
BELLE-7B-2M is a 7 billion parameter language model fine-tuned by the BelleGroup on a dataset of 2 million Chinese and 50,000 English samples. It is based on the Bloomz-7b1-mt model and has good Chinese instruction understanding and response generation capabilities. The model can be easily loaded using the AutoModelForCausalLM from Transformers.
Similar models include the Llama-2-13B-GGML model created by TheBloke, which is a GGML version of Meta's Llama 2 13B model. Both models are large language models trained on internet data and optimized for instructional tasks.
Model inputs and outputs
Inputs
- Text input in the format
Human: {input} \n\nAssistant:
Outputs
- Textual responses generated by the model, continuing the conversation from the provided input
Capabilities
The BELLE-7B-2M model demonstrates strong performance on Chinese instruction understanding and response generation tasks. It can engage in open-ended conversations, provide informative answers to questions, and assist with a variety of language-based tasks.
What can I use it for?
The BELLE-7B-2M model could be useful for building conversational AI assistants, chatbots, or language-based applications targeting Chinese and English users. Its robust performance on instructional tasks makes it well-suited for applications that require understanding and following user instructions.
Things to try
You could try prompting the BELLE-7B-2M model with open-ended questions or tasks to see the breadth of its capabilities. For example, you could ask it to summarize an article, generate creative writing, or provide step-by-step instructions for a DIY project. Experimenting with different prompts and use cases can help you better understand the model's strengths and limitations.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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