chatglm-6b-int4
Maintainer: THUDM
409
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| Property | Value |
|---|---|
| Model Link | View 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
chatglm-6b-int4 is an open-source, large language model developed by the Tsinghua University Department of Machine Learning (THUDM). It is a 6B parameter model that has been quantized to INT4 precision for efficient inference on CPUs. The model is based on the General Language Model (GLM) architecture and has been trained on a large corpus of bilingual (Chinese-English) text.
chatglm-6b-int4 retains many of the excellent features of earlier ChatGLM models, such as smooth dialogue and low deployment threshold. Key improvements include:
- Stronger Performance: The model has undergone further pretraining and fine-tuning, resulting in substantial performance gains on benchmarks like MMLU (+23%), CEval (+33%), GSM8K (+571%), and BBH (+60%) compared to earlier ChatGLM models.
- Longer Context: The model's context length has been extended from 2K tokens to 32K tokens, allowing for more extensive dialogue.
- More Efficient Inference: The use of techniques like Multi-Query Attention has improved the model's inference speed by 42% and increased the dialogue length supported by 6GB of GPU memory from 1K to 8K tokens.
Model inputs and outputs
Inputs
- Text: The model accepts text input, which can be used to initiate a dialogue or provide context for the model's response.
- Dialogue History: The model can maintain a dialogue history, allowing it to understand and respond to the current context of the conversation.
Outputs
- Text Response: The model generates a textual response based on the provided input and dialogue history.
- Dialogue History: The model updates the dialogue history with the new input and response, allowing for continued conversation.
Capabilities
chatglm-6b-int4 is a highly capable language model that can engage in open-ended dialogue, answer questions, and assist with a variety of language-related tasks. It demonstrates strong performance on benchmarks covering semantics, mathematics, reasoning, and more. The model's ability to maintain context over long conversations makes it well-suited for applications that require sustained interactions, such as customer service chatbots or virtual assistants.
What can I use it for?
chatglm-6b-int4 can be used for a wide range of language-based applications, such as:
- Conversational AI: The model's fluent dialogue capabilities make it suitable for building chatbots, virtual assistants, and other conversational interfaces.
- Content Generation: The model can be used to generate coherent and contextual text, such as articles, stories, or product descriptions.
- Question Answering: The model can be leveraged to build question-answering systems that can provide informative and relevant responses.
- Tutoring and Education: The model's strong reasoning and language understanding abilities could be utilized to create intelligent tutoring systems or educational tools.
Things to try
One interesting aspect of chatglm-6b-int4 is its ability to maintain context and engage in multi-turn dialogues. Developers could explore building applications that leverage this capability, such as personal assistants that can remember and refer back to previous parts of a conversation. Additionally, the model's quantization to INT4 precision makes it well-suited for deployment on CPU-based systems, opening up opportunities for edge computing and on-device applications.
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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