chatglm3-6b-base

Maintainer: THUDM

Total Score

82

Last updated 5/17/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

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.



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

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Total Score

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chatglm3-6b-32k

THUDM

Total Score

241

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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chatglm2-6b-int4

THUDM

Total Score

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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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