chatglm-6b
Maintainer: THUDM
2.8K
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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 is an open bilingual language model based on the General Language Model (GLM) framework, with 6.2 billion parameters. Using quantization techniques, users can deploy the model locally on consumer-grade graphics cards, requiring only 6GB of GPU memory at the INT4 quantization level. chatglm-6b uses technology similar to ChatGPT, optimized for Chinese Q&A and dialogue. The model is trained on approximately 1 trillion tokens of Chinese and English corpus, supplemented by supervised fine-tuning, feedback bootstrapping, and reinforcement learning with human feedback. Despite its relatively small size of around 6.2 billion parameters, the model is able to generate answers that are aligned with human preferences.
Similar open-source models in the ChatGLM series include ChatGLM2-6B and ChatGLM3-6B, which build upon chatglm-6b with improvements in performance, context length, and efficiency. These models are all developed by the THUDM team.
Model Inputs and Outputs
Inputs
- Text prompts for the model to generate responses to
Outputs
- Generated text responses based on the input prompts
- Dialogue history to support multi-turn conversational interactions
Capabilities
chatglm-6b demonstrates strong performance in Chinese Q&A and dialogue, leveraging its bilingual training corpus and optimization for these use cases. The model can engage in coherent, multi-turn conversations, drawing upon its broad knowledge to provide informative and relevant responses.
What Can I Use It For?
chatglm-6b can be a valuable tool for a variety of applications, such as:
- Chatbots and virtual assistants: The model's capabilities in natural language understanding and generation make it well-suited for building conversational AI assistants.
- Content creation and generation: The model can be fine-tuned or prompted to generate various types of text content, such as articles, stories, or scripts.
- Education and research: The model can be used for tasks like question answering, text summarization, and language learning, supporting educational and academic applications.
- Customer service and support: The model's dialogue skills can be leveraged to provide efficient and personalized customer service experiences.
Things to Try
One interesting aspect of chatglm-6b is its ability to handle code-switching between Chinese and English within the same conversation. This can be useful for users who communicate in a mix of both languages, as the model can seamlessly understand and respond to such inputs.
Another unique feature is the model's support for multi-turn dialogue, which allows for more natural and contextual conversations. Users can engage in extended exchanges with the model, building upon previous responses to explore topics in-depth.
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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chatglm2-6b-int4
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ChatGLM2-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model ChatGLM-6B. It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing several new features. Based on the development experience of the first-generation ChatGLM model, the base model of ChatGLM2-6B has been fully upgraded. It uses the hybrid objective function of GLM and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. Evaluations show that ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%) compared to the first-generation model. Model inputs and outputs ChatGLM2-6B is a large language model that can engage in open-ended dialogue. It takes text prompts as input and generates relevant and coherent responses. The model supports both Chinese and English prompts, and can maintain a multi-turn conversation history of up to 8,192 tokens. Inputs Text prompt**: The initial prompt or query provided to the model to start a conversation. Conversation history**: The previous messages exchanged during the conversation, which the model can use to provide relevant and contextual responses. Outputs Generated text response**: The model's response to the provided prompt, generated using its language understanding and generation capabilities. Conversation history**: The updated conversation history, including the new response, which can be used for further exchanges. Capabilities ChatGLM2-6B demonstrates strong performance across a variety of tasks, including open-ended dialogue, question answering, and text generation. For example, the model can engage in fluent conversations, provide insightful answers to complex questions, and generate coherent and contextually relevant text. The model's capabilities have been significantly improved compared to the first-generation ChatGLM model, as evidenced by the substantial gains in performance on benchmark datasets. What can I use it for? ChatGLM2-6B can be used for a wide range of applications that involve natural language processing and generation, such as: Conversational AI**: The model can be used to build intelligent chatbots and virtual assistants that can engage in natural conversations with users, providing helpful information and insights. Content generation**: The model can be used to generate high-quality text content, such as articles, reports, or creative writing, by providing it with appropriate prompts. Question answering**: The model can be used to answer a variety of questions, drawing upon its broad knowledge and language understanding capabilities. Task assistance**: The model can be used to help with tasks such as code generation, writing assistance, and problem-solving, by providing relevant information and suggestions based on the user's input. Things to try One interesting aspect of ChatGLM2-6B is its ability to maintain a long conversation history of up to 8,192 tokens. This allows the model to engage in more in-depth and contextual dialogues, where it can refer back to previous messages and provide responses that are tailored to the flow of the conversation. You can try engaging the model in longer, multi-turn exchanges to see how it handles maintaining coherence and relevance over an extended dialogue. Another notable feature of ChatGLM2-6B is its improved efficiency, which allows for faster inference and lower GPU memory usage. This makes the model more accessible for deployment in a wider range of settings, including on lower-end hardware. You can experiment with running the model on different hardware configurations to see how it performs and explore the trade-offs between performance and resource requirements.
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ChatGLM3-6B is the latest open-source model in the ChatGLM series from THUDM. It retains many excellent features from previous generations, such as smooth dialogue and low deployment threshold, while introducing several new capabilities. The base model, ChatGLM3-6B-Base, employs a more diverse training dataset, more sufficient training steps, and a more reasonable training strategy, making it one of the strongest pre-trained models under 10B parameters. In addition to the standard multi-turn dialogue, ChatGLM3-6B adopts a newly designed Prompt format that natively supports function call, code interpreter, and complex scenarios such as agent tasks. The open-source series also includes the base model ChatGLM-6B-Base and the long-text dialogue model ChatGLM3-6B-32K. Model Inputs and Outputs Inputs Text**: The model takes text input, which can be in the form of a multi-turn dialogue or a prompt for the model to respond to. Outputs Text**: The model generates human-readable text in response to the input. This can include dialogue responses, code, or task outputs depending on the prompt. Capabilities ChatGLM3-6B is a powerful generative language model capable of engaging in smooth, coherent dialogue while also supporting more advanced functionalities like code generation and task completion. Evaluations show the base model, ChatGLM3-6B-Base, has strong performance across a variety of datasets including semantics, mathematics, reasoning, code, and knowledge. What Can I Use It For? ChatGLM3-6B is well-suited for a wide range of natural language processing tasks, from chatbots and virtual assistants to code generation and task automation. The model's diverse capabilities mean it could be useful in industries like customer service, education, programming, and research. Some potential use cases include: Building conversational AI agents for customer support or personal assistance Generating code snippets or even complete programs based on textual descriptions Automating repetitive tasks through the model's ability to interpret and execute instructions Enhancing language learning and tutoring applications Aiding in research and analysis by summarizing information or drawing insights from text The open licensing of the model also makes it accessible for academic and non-commercial use. Things to Try One interesting aspect of ChatGLM3-6B is its ability to handle complex, multi-step prompts and tasks. Try providing the model with a detailed, multi-part instruction or scenario and see how it responds. For example, you could ask it to write a short story with specific plot points and characters, or to solve a complex problem by breaking it down into a series of subtasks. Another intriguing possibility is to explore the model's code generation and interpretation capabilities. See if you can prompt it to write a working program in a programming language, or to analyze and explain the functionality of a given code snippet. By pushing the boundaries of what you ask the model to do, you can gain a better understanding of its true capabilities and limitations. The combination of fluent dialogue and more advanced task-completion skills makes ChatGLM3-6B a fascinating model to experiment with.
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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.
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