Bellegroup
Models by this creator
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BELLE-7B-2M
186
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.
Updated 5/28/2024
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BELLE-LLaMA-EXT-13B
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The BELLE-LLaMA-EXT-13B is a large language model developed by the BelleGroup that builds upon the original LLaMA model released by Meta AI. The model was trained using a two-phase approach: Extending the vocabulary with an additional 50,000 tokens specific to Chinese and further pretraining the word embeddings on a Chinese corpus. Full-parameter finetuning the model with 4 million high-quality instruction-following examples. This approach allows the model to have strong Chinese language understanding and instruction-following capabilities, while retaining the robustness and broad knowledge of the original LLaMA model. Similar models like the BELLE-7B-2M and llama-7b-hf-transformers-4.29 also aim to extend the capabilities of the LLaMA architecture. Model inputs and outputs Inputs The model takes in natural language text as input, which can include instructions, questions, or general prompts. Outputs The model generates natural language text in response to the input, exhibiting strong performance on a variety of tasks like question answering, language understanding, and instruction following. Capabilities The BELLE-LLaMA-EXT-13B model demonstrates impressive capabilities in areas like Chinese language understanding, task-oriented dialogue, and following complex instructions. For example, the model can engage in nuanced conversations on Chinese cultural topics, answer questions about current events with up-to-date knowledge, and break down and complete multi-step tasks with high accuracy. What can I use it for? The BELLE-LLaMA-EXT-13B model could be useful for a wide range of applications, particularly those involving Chinese language processing or instruction-following. Some potential use cases include: Building chatbots or virtual assistants with strong Chinese language capabilities Powering question-answering systems for Chinese-speaking users Developing intelligent tutoring systems that can guide users through complex workflows Enhancing machine translation between Chinese and other languages Things to try One interesting aspect to explore with the BELLE-LLaMA-EXT-13B model is its ability to handle open-ended instructions and tasks. Try providing the model with detailed, multi-step prompts and see how well it can understand the requirements and generate a comprehensive, coherent response. You could also experiment with incorporating the model into a larger system, such as a dialogue agent or task planner, to leverage its unique strengths.
Updated 9/6/2024