calm3-22b-chat
Maintainer: cyberagent
59
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| Property | Value |
|---|---|
| Run this model | Run 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
calm3-22b-chat is a large language model developed by CyberAgent, Inc., a leading Japanese tech company. It is a fine-tuned version of their CyberAgentLM3 model, which was pre-trained on 2.0 trillion tokens from scratch. The calm3-22b-chat model is specialized for dialogue use cases, making it well-suited for chatbots and conversational AI applications.
Model inputs and outputs
The calm3-22b-chat model takes in messages formatted in the ChatML prompt format, which includes a system prompt and a user prompt. The model then generates a response, which can be up to 1024 tokens long. The output is streamed using the TextStreamer tool, allowing for real-time, interactive conversations.
Inputs
- System prompt: Sets the initial context for the conversation
- User prompt: The user's message or query
Outputs
- Generated response: The model's response to the user's input
Capabilities
calm3-22b-chat is a powerful language model capable of engaging in natural, coherent conversations. It can understand and respond to a wide range of topics, from general knowledge to complex, task-oriented dialogue. The model's large size and specialized fine-tuning allow it to generate human-like responses with a high degree of fluency and contextual awareness.
What can I use it for?
The calm3-22b-chat model is well-suited for a variety of conversational AI applications, such as virtual assistants, chatbots, and interactive educational or entertainment experiences. Its language understanding and generation capabilities can be leveraged to create engaging, intelligent dialogue systems that can assist users with a wide range of tasks and queries.
Things to try
Experiment with the calm3-22b-chat model by prompting it with different types of messages and conversational scenarios. Try asking it open-ended questions, giving it specific tasks to complete, or engaging in more freeform dialogue. Observe how the model responds and how its capabilities evolve as you interact with it further.
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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