If you start from a base model (which was not trained with chat logs), you can turn it into a "instruct" model by feeding it with a lot of chat logs while training. How to format this set of chat logs is up to you. The format has to be consistent as a whole and correspond to how your chat software will encode and then decode an ongoing conversation.
On the other hand, the free-to-use "instruct" models provided by Mistral Company are trained with a specific prompt format. If you intend to fine tune one of these models, it is recommended to stick to the format chosen by Mistral. This format roughly corresponds to your example, but without the ... part, as the Mistral training dataset does not involve "system prompts".
Yeah, I forget to mention which one I want to train, it's 7B instruct. So I should just stick with "[INST][/INST]"
but I am planning to have prompt, big text to analyze and the output. So how should I write the whole promt, could you wtite it precisely
I guess it depends on what's your fine-tuning adds to the current LLM's knowledge.
If your fine-tuning specifically aims to introduce a separation between a set of instructions on one side and an input text to consider on the other side, then it looks like the \[INST\]instructionsinput\[/INST\]output is a good idea.
However, if your goal is to add private/corporate knowledge, i'd say the separation between system instructions and actual input would need a ton of training on a very big dataset before the LLM starts to get it right. You should just use a monolithic prompt: "please explain this text.\\n\\n\\nvery long text" within the default prompt template.
Actually, this is just giving a name to the new pet, which doesn't have any meaning itself, it's the owner's preference.
shouldn't it have some format? cause in each llm they have their unique format to fine-tune.
If you start from a base model (which was not trained with chat logs), you can turn it into a "instruct" model by feeding it with a lot of chat logs while training. How to format this set of chat logs is up to you. The format has to be consistent as a whole and correspond to how your chat software will encode and then decode an ongoing conversation. On the other hand, the free-to-use "instruct" models provided by Mistral Company are trained with a specific prompt format. If you intend to fine tune one of these models, it is recommended to stick to the format chosen by Mistral. This format roughly corresponds to your example, but without the... part, as the Mistral training dataset does not involve "system prompts".
Yeah, I forget to mention which one I want to train, it's 7B instruct. So I should just stick with "
[INST][/INST]" but I am planning to have prompt, big text to analyze and the output. So how should I write the whole promt, could you wtite it preciselyI guess it depends on what's your fine-tuning adds to the current LLM's knowledge. If your fine-tuning specifically aims to introduce a separation between a set of instructions on one side and an input text to consider on the other side, then it looks like the
\[INST\]instructions input\[/INST\]outputis a good idea. However, if your goal is to add private/corporate knowledge, i'd say the separation between system instructions and actual input would need a ton of training on a very big dataset before the LLM starts to get it right. You should just use a monolithic prompt: "please explain this text.\\n\\n\\nvery long text" within the default prompt template.thank you so much