GLM-4-9B-Chat
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GLM 4 9B Chat
Overview :
GLM-4-9B-Chat is the open-source version of the latest pre-trained model GLM-4 series released by Zhi谱 AI. It boasts advanced features such as multi-turn dialogue, web browsing, code execution, custom tool invocation, and long-text reasoning. It supports 26 languages including Japanese, Korean, and German, and has launched a model with support for a 1M context length.
Target Users :
This product is designed for developers and researchers in natural language processing, machine learning, and artificial intelligence. It provides strong language understanding and generation capabilities, as well as support for multiple languages and code execution, which helps in conducting complex data analysis and model training.
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Top Region: US(17.94%)
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Use Cases
Used to develop multilingual chatbots, providing cross-language communication services.
Integrated into programming assistant tools to help developers understand and generate code.
Serves as a data scientist's assistant for conducting complex data analysis and reasoning.
Features
Multi-turn dialogue capability, allowing for continuous interaction with users.
Web browsing functionality, enabling the model to access and understand webpage content.
Code execution capability, permitting the execution of simple code snippets.
Custom tool invocation, allowing integration with external tools to expand functionality.
Long-text reasoning, supporting a maximum context length of 128K.
Multilingual support, covering 26 different languages.
Excellent performance on the Berkeley Function Calling Leaderboard, demonstrating strong tool invocation abilities.
How to Use
Step 1: Visit the GLM-4-9B-Chat model page and download the model.
Step 2: Load the model and tokenizer using the transformers library.
Step 3: Prepare the input data and tokenize it using the tokenizer.
Step 4: Convert the tokenized input data into a format acceptable by the model.
Step 5: Set generation parameters, such as maximum length and sampling strategy.
Step 6: Use the model to generate output and obtain the desired results.
Step 7: Decode and post-process the generated text to meet specific application requirements.
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