chatglm3-6b
Maintainer: THUDM
1.0K
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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
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.
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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chatglm3-6b-base
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The chatglm3-6b-base model is the latest open-source model in the ChatGLM series from THUDM. While retaining many excellent features from previous generations like smooth dialogue and low deployment threshold, ChatGLM3-6B-Base introduces several key improvements. It employs a more diverse training dataset, more training steps, and a more reasonable training strategy, resulting in the strongest performance among pre-trained models under 10B parameters as evaluated on datasets like semantics, mathematics, reasoning, code, and knowledge. Additionally, ChatGLM3-6B-Base adopts a new prompt format that supports not just multi-turn dialogue, but also function calls, code interpretation, and complex agent tasks. The model is part of a comprehensive open-source series that includes the base ChatGLM-6B-Base and the long-text dialogue ChatGLM3-6B-32K, all with fully open weights for academic research and free commercial use after completing a registration questionnaire. Model inputs and outputs The chatglm3-6b-base model is a text-to-text AI model that can engage in open-ended dialogue, perform code interpretation, and execute complex tasks. It takes natural language prompts as input and generates coherent and relevant text responses. Inputs Natural language prompts in either English or Chinese Requests for the model to perform specific tasks like generating code or interpreting programming language Outputs Coherent and contextually appropriate text responses Executable code or interpretations of programming language Capabilities The ChatGLM3-6B-Base model has been trained to excel at a variety of language understanding and generation tasks. It demonstrates strong performance on benchmarks evaluating semantic understanding, mathematical reasoning, and code generation. The model can engage in smooth, multi-turn dialogues, understand complex prompts, and provide insightful responses. Additionally, it can interpret and generate code, making it a useful tool for developers. What can I use it for? The versatile chatglm3-6b-base model can be applied to a wide range of use cases. Potential applications include: Interactive AI assistants that can engage in open-ended conversation, answer questions, and provide explanations Code generation and interpretation tools to boost developer productivity Educational applications that can tutor students, explain concepts, and provide feedback Creative writing aids that can generate engaging narratives and content Multilingual communication tools that can translate between Chinese and English With its robust capabilities and open licensing, the chatglm3-6b-base model presents exciting opportunities for innovators and researchers to explore the frontiers of large language models. Things to try One compelling aspect of the ChatGLM3-6B-Base model is its ability to handle complex, multi-part prompts and execute a series of related tasks. Try providing the model with a high-level objective, like "Write a Python script that calculates the area of a circle given its radius," and see how it breaks down the request, generates the necessary code, and explains its reasoning step-by-step. The model's flexible prompt format and strong task-completion skills make it well-suited for tackling sophisticated challenges. Another intriguing avenue to explore is the model's potential for cross-lingual understanding and generation. Provide prompts in both English and Chinese, and observe how the model seamlessly translates between the two languages while maintaining coherence and context. This capability opens up possibilities for building multilingual applications and bridging language barriers.
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chatglm3-6b-128k
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chatglm3-6b-128k is a larger version of the ChatGLM3-6B model developed by THUDM. Based on ChatGLM3-6B, chatglm3-6b-128k further strengthens the model's ability to understand long texts by updating the position encoding and using a 128K context length during training. This allows the model to better handle conversations with longer contexts than the 8K supported by the base ChatGLM3-6B model. The key features of chatglm3-6b-128k include: Improved long text understanding:** The model can handle contexts up to 128K tokens in length, making it better suited for lengthy conversations or tasks that require processing large amounts of text. Retained excellent features:** The model retains the smooth dialogue flow and low deployment threshold of the previous ChatGLM generations. Comprehensive open-source series:** In addition to chatglm3-6b-128k, THUDM has also open-sourced the base chatglm3-6b model and the chatglm3-6b-base model, providing a range of options for different use cases. Model inputs and outputs Inputs Natural language text:** The model can accept natural language text as input, including questions, commands, or conversational prompts. Outputs Natural language responses:** The model generates coherent, context-aware natural language responses based on the provided input. Capabilities chatglm3-6b-128k is capable of engaging in open-ended dialogue, answering questions, providing explanations, and assisting with a variety of tasks such as research, analysis, and creative writing. The model's improved ability to handle long-form text input makes it well-suited for use cases that require processing and summarizing large amounts of information. What can I use it for? chatglm3-6b-128k can be useful for a wide range of applications, including: Research and analysis:** The model can help researchers and analysts by summarizing large amounts of text, extracting key insights, and providing detailed explanations on complex topics. Conversational AI:** The model can be used to develop intelligent chatbots and virtual assistants that can engage in natural, context-aware conversations. Content creation:** The model can assist with tasks like report writing, creative writing, and even software documentation by providing relevant information and ideas. Education and training:** The model can be used to create interactive learning experiences, answer student questions, and provide personalized explanations of complex topics. Things to try One interesting thing to try with chatglm3-6b-128k is to see how it handles longer, more complex prompts and queries that require processing and summarizing large amounts of information. You could try giving the model detailed research questions, complex analytical tasks, or lengthy creative writing prompts and see how it responds. Another interesting experiment would be to compare the performance of chatglm3-6b-128k to the base chatglm3-6b model on tasks that require handling longer contexts. This could help you understand the specific benefits and trade-offs of the enhanced long-text processing capabilities in chatglm3-6b-128k.
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chatglm3-6b-32k
242
The chatglm3-6b-32k is a large language model developed by THUDM. It is the latest open-source model in the ChatGLM series, which retains many excellent features from previous generations such as smooth dialogue and low deployment threshold, while introducing several key improvements. Compared to the earlier ChatGLM3-6B model, chatglm3-6b-32k further strengthens the ability to understand long texts and can better handle contexts up to 32K in length. Specifically, the model updates the position encoding and uses a more targeted long text training method, with a context length of 32K during the conversation stage. This allows chatglm3-6b-32k to effectively process longer inputs compared to the 8K context length of ChatGLM3-6B. The base model for chatglm3-6b-32k, called ChatGLM3-6B-Base, employs a more diverse training dataset, more training steps, and a refined training strategy. Evaluations show that ChatGLM3-6B-Base has the strongest performance among pre-trained models under 10B parameters on datasets covering semantics, mathematics, reasoning, code, and knowledge. Model Inputs and Outputs Inputs Text**: The model can take text inputs of varying length, up to 32K tokens, and process them in a multi-turn dialogue setting. Outputs Text response**: The model will generate relevant text responses based on the provided input and dialog history. Capabilities chatglm3-6b-32k is a powerful language model that can engage in open-ended dialog, answer questions, provide explanations, and assist with a variety of language-based tasks. Some key capabilities include: Long-form text understanding**: The model's 32K context length allows it to effectively process and reason about long-form inputs, making it well-suited for tasks involving lengthy documents or multi-turn conversations. Multi-modal understanding**: In addition to regular text-based dialog, chatglm3-6b-32k also supports prompts that include functions, code, and other specialized inputs, allowing for more comprehensive task completion. Strong general knowledge**: Evaluations show the underlying ChatGLM3-6B-Base model has impressive performance on a wide range of benchmarks, demonstrating broad and deep language understanding capabilities. What Can I Use It For? The chatglm3-6b-32k model can be useful for a wide range of applications that require natural language processing and generation, especially those involving long-form text or multi-modal inputs. Some potential use cases include: Conversational AI assistants**: The model's ability to engage in smooth, context-aware dialog makes it well-suited for building virtual assistants that can handle open-ended queries and maintain coherent conversations. Content generation**: chatglm3-6b-32k can be used to generate high-quality text content, such as articles, reports, or creative writing, by providing appropriate prompts. Question answering and knowledge exploration**: Leveraging the model's strong knowledge base, it can be used to answer questions, provide explanations, and assist with research and information discovery tasks. Code generation and programming assistance**: The model's support for code-related inputs allows it to generate, explain, and debug code, making it a valuable tool for software development workflows. Things to Try Some interesting things to try with chatglm3-6b-32k include: Engage the model in long-form, multi-turn conversations to test its ability to maintain context and coherence over extended interactions. Provide prompts that combine text with other modalities, such as functions or code snippets, to see how the model handles these more complex inputs. Explore the model's reasoning and problem-solving capabilities by giving it tasks that require analytical thinking, such as math problems or logical reasoning exercises. Fine-tune the model on domain-specific datasets to see how it can be adapted for specialized applications, like medical diagnosis, legal analysis, or scientific research. By experimenting with the diverse capabilities of chatglm3-6b-32k, you can uncover new and innovative ways to leverage this powerful language model in your own projects and applications.
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chatglm-6b
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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.
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