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Community to discuss about Llama, the family of large language models created by Meta AI.

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Why is no one releasing 70b models? (alien.top)

submitted 11 months ago by Longjumping-Bake-557@alien.top to c/localllama@poweruser.forum

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There has been a lot of movement around and below the 13b parameter bracket in the last few months but it's wild to think the best 70b models are still llama2 based. Why is that?

We have 13b models like 8bit bartowski/Orca-2-13b-exl2 approaching or even surpassing the best 70b models now

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[–] thereisonlythedance@alien.top 1 points 11 months ago (3 children)

I've been training a lot lately, mostly on RunPod, a mix of fine-tuning Mistral 7B and training LoRA and QLoRAs on 34B and 70Bs. My main takeaway is that the LoRA outcomes are just... not so great. Whereas I'm very happy with the Mistral fine-tunes.

I mean, it's fantastic we can tinker with a 70B at all, but it doesn't matter how good your dataset is, you just can't have the same impact as you can with a full finetune. I think this is why model merging/frankensteining has become popular, it's an expression of the limitations of LoRA training.

Personally, I have high hopes for a larger Mistral model (in the 13-20B range) that we can still do a full fine-tune on. Right now, between my own specific tunes of Mistral and some of the recent external tunes like Starling I feel like I'm close to having the tools I want/need. But Mistral is still 7B, it doesn't matter how well it's tuned, it will still get a little muddled at times, particular with longer term dependencies.

[–] Vilzuh@alien.top 1 points 11 months ago

I have been trying to learn about fine-tuning and lora training for the past couple weeks but I'm having trouble finding easy enough resources to learn from. Could you give me some pointers to what I can read to get started with finetuning llama2 or mistral?

I have tried training quantized models locally with oobabooga and llama.cpp and I also have access to runpod. Really appreciate any info!

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