Depends on the LLM and its training data. Will have to think more about the second question.
RESEARCH
-
Fine-tune Mistral-7B with synthetic prompt-response datasets
By
–
Switch all prompts to mistral-large. Run the repo many times, collect the prompts / responses for each input / output. Format as JSONL, train a Mistral-7B or similar on all of them, and slot it in.
-
High-Quality Data Training for AI Models
By
–
Really good data with that style. You don’t need a ton of it (few hundred examples is totally fine), just make sure it’s really high-quality.
-
Axolotl and Mistral for Synthetic Data Generation
By
–
Axolotl. Fucking awesome. Shoutout to @winglian
. For synthetic data, Mistral models are great. Pair that with some real-world data for grounding/diversity/inspiration for good results. @csahil28 is building an even better solution here. Costs depend on the task at hand. Can be -
Pre-training vs Fine-tuning: Learning Strategies in AI Models
By
–
When you’re pre-training, you basically learn from everything in the dataset. When fine-tuning, you often only want the model to learn from the text in the output, not the prompt itself.
-
LLM Prompting and Fine-tuning Questions and Answers
By
–
Ask me your LLM prompting/fine-tuning questions. Have an hour, will answer as many as I can.
-
The Little Book of Deep Learning by Kirk D. Borne
By
–
The Little Book of #DeepLearning
by @KirkDBorne Read more: https://
buff.ly/42dUTQa #ArtificialIntelligence #ML #MachineLearning cc: @ravikikan @yvesmulkers @pbalakrishnarao -
Mistral 7B Improvements Enable Better Fine-Tuning Capabilities
By
–
This is huge news. Mistral 7B was already the best model in its size class, and these improvements are a huge step up. I’ll be re-training many of my current fine-tunes over this model ASAP.
-
Google GIVT: Generative Infinite Vocabulary Transformers Breakthrough
By
–
https://
drdebashisdutta.com/introducing-gi
vt-google-researchs-breakthrough-in-real-valued-vector-sequences-with-generative-infinite-vocabulary-transformers/
…
