Thank you! We currently don't support direct labeling, but if you can extract the text, our platform helps you organize it for fine-tuning. What use case are you looking to train the model for?
Yes, you can fine-tune using plain text completions. You don't need structured conversations unless you want conversational abilities. Plain text works great if you want the model to generate text in a specific style or domain. It all depends on what you're trying to achieve.
Hi, current pricing for Llama 3.1 8B for example is: Training Tokens: $2 / 1M, Input and Output Tokens: $0.30 / 1M. We'll update pricing on the website shortly to reflect this.
To get the outcome you want, RAG (retrieval augmented generation) would be the way to go, not fine-tuning. Fine-tuning doesn't make the model memorize specific content like a book. It teaches new behaviors or styles. RAG allows the model to access and reference the book during inference. Our platform focuses on fine-tuning with structured datasets, so data needs to be in a specific format.
The magic behind NotebookLM can't be replicated only with fine-tuning. It's all about the workflow, from the chunking strategy, to retrieval etc.
For a defined specific use-case it's certainly possible to beat their performance, but things get harder when you try to create a general solution.
To answer your question, the format of the data depends entirely on the use-case and how many examples you have. The more examples you have, the more flexible you can be.
Other co-founder here, so we offer more specific features around iterating on your datasets and include domain experts in this workflow. And I'd argue that you also want your datasets not necessarily with your foundation model provider like OpenAI, so you have the option to test with and potentially switch to open-source models.
If that's the case then I'll try the platform out :) I want to finetune Codestral or Qwen2.5-coder on a custom codebase. Thank you for the response! Are there some docs or infos about the compatibility of the downloaded models, meaning will they work right away with llama.cpp?
We don't support Codestral or Qwen2.5-coder right out of the box for now, but depending on your use-case we certainly could add it.
We utilize LoRA for smaller models, and qLoRA (quantized) for 70b+ models to improve training speeds, so when downloading model weights, what you get is the weights & adapter_config.json. Should work with llama.cpp!
Thanks! We have a free tier with limited features. Our pro plan starts at €50 per seat per month and includes all features. Teams often collaborate with domain experts to create datasets. And for custom integrations, we offer custom plans on request.
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