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license: apache-2.0 |
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<img src="https://huggingface.co/cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser/resolve/main/fc-dolphin.jpg" width="600" /> |
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by David, Fernando and Eric |
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Sponsored by: [VAGO Solutions](https://vago-solutions.de) and [HyperSpace.Ai](https://hyperspace.computer/) |
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Join our Discord! https://discord.gg/cognitivecomputations |
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A function calling version of [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) |
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It follows the implementation of laserRMT @ https://github.com/cognitivecomputations/laserRMT and the novel training technique - we partially freeze the model according to a laser-like analysis (Official Paper soon) |
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which effectively prevents the significant problem of language models forgetting previously acquired knowledge. This aspect is particularly crucial when attempting to teach the model specific skills, such as function calling. |
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We intend to be the first of a family of experimentations being carried out @ Cognitive Computations. |
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# Usage |
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This model is trained on a variaton of [glaive function calling v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) and uses their format for declaring functions and invoking them. |
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Note that like other Dolphin models you should be using the ChatML syntax for representing chats. |
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Set your system prompt to something along these lines: |
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``` |
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You are a helpful assistant with access to the following functions. |
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Use them if required - |
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{ "name": "search_wikipedia", "description": "Search for a given keyword or phrase on Wikipedia", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "The keyword or phrase to search" } }, "required": [ "query" ] } } |
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``` |
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The model will then attempt to invoke the specified functions using the token `<functioncall>` and some JSON with a `name` and an `arguments` parameter. |
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e.g.: |
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``` |
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ASSISTANT: <functioncall> {"name": "search_wikipedia", "arguments": '{"query": "Eiffel Tower"}'} <|endoftext|> |
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``` |
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Your code should detect this and stop execution and invoke the function call and inject a response back into the context with the following syntax: |
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``` |
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FUNCTION RESPONSE: {"result": "The Eiffel Tower is a wrought-iron lattice... etc"} |
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``` |
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You should then prompt the assistant to generate another response. |
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Note that the model may not always attempt to invoke a function and may respond saying it doesn't have access to the right functionality. |
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# Quants |
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- [dagbs/-GGUF](https://huggingface.co/dagbs/fc-dolphin-2.6-mistral-7b-dpo-laser-GGUF) |
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- [solidrust/-AWQ](https://huggingface.co/solidrust/fc-dolphin-2.6-mistral-7b-dpo-laser-AWQ) |
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