R to @svpino: That's awesome news! Fine-tuning models without writing code is a game-changer. @monsterapis seems to have thought of everything, with even more updates! Can't wait to explore Mistral 7B, QLora, Flash Attention 2, and the parallelism feature. Thanks for sharing! #leadingthefuture

R to @monsterapis: Great!

R to @agiutopia: We have already added Mistral and Zephyr. You can select them for finetuning while creating the finetuning job :)

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R to @Suhail: That is literally @monsterapis

R to @monsterapis: Thanks for providing this.

R to @Klaudioz: Hi @Klaudioz can you please confirm again? We can confirm that your account received 10k credits using the code. Let us know if we can help any way :)

R to @kiranadimatyam: We would love to get your feedback!

R to @slow_developer: Hi @slow_developer you may want to refer to our blog on how we finetuned Llama 70B which is a very large model using our no code LLM finetuner: https://blog.monsterapi.ai/finetuning-llama-70b-with-no-code-results-methods-and-implications/

R to @itamar_mar: Absolutely, we provide pre filled recipes of hyperparameters based on our internal research. And these values can be modified as per your requirement. Are you looking for tweaking any other hyperparameter than the default ones? Would love to know! Let's connect in DM

R to @Suhail: @monsterapis @saurabhvij137

R to @svpino: It seems that @monsterapis are solving the infra challenge As part of the fine-tuning process, two additional pains are: 1. Selecting hyperparameters 2. Understanding the training data (in order to clean it or add additional useful data) Will monsterapi solution help with that?

R to @svpino: Thanks for the detailed breakdown Santiago. How has @monsterapis no-code platform transformed the ease of fine-tuning large language models?

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