31–40 of 63 posts
Neat. Maybe you guys should make a fine tuning platform for deepseek specifically, with a fine tune API similar to openAIs. You could expand out into hosting those models too.
Neat. Maybe you guys should make a fine tuning platform for deepseek specifically, with a fine tune API similar to openAIs. You could expand out into hosting those models too.
you mean fine tuning that feels like SFT but is different (since you can't use that with reasoning models) built around the DeepSeek class of models?
This is a good problem to solve for. But making this closed source makes it a bad choice for us to use. And the other aspect as someone already specified is it seems to only work with single agent workflows.
I have a few questions. 1. I'm assuming by the pricing it's "serverless" inference, what's the cold-start time like? 2. Any idea on inference costs?
Also just to reiterate what others say but the option of exporting weights would definitely make it more appealing (although it sounds like that's in the roadmap).
Earlier quoted context omitted.
Ha - here's the advice I give to YC startups about making demo videos for HN: "What works well for HN is raw and direct, with zero production values. Skip any introductions and jump straight into showing your product doing what it does best. Voiceover is good, but no marketing slickness—no fancy logos or background music!" I guess there's zero production values and zero production values...
Well... we took the rawness to heart, that's clear!
Where do you want to access #ext-customers?
The organization you select is where you’ll find this channel in Slack. Admins will get a chance to review everything before you start collaborating.
Tip: Add this Slack Connect channel to the organization that’s already connected with P2P Industries, or where you have similar channels.I tell you what I don't like, the game y'all are playing with billing of Slack users: Where do you want to access #ext-customers? The organization you select is where you’ll find this channel in Slack. Admins will get a chance to review everything before you start collaborating. Tip: Add this Slack Connect channel to the organization that’s already connected with P2P Industries, or where you have similar channels.
Also I think if I'd use your product, I'd like to be able to host the model elsewhere in case I don't like the platform anymore :)
That’s fair! It has been mentioned before so we‘ll likely build that into the platform. Would you like us to upload your model to your huggingface account, download the weights or choose an inference provider we then upload it to?
Only 20 training samples improved llm performance, that sounds unrealistic! My experience with RLHF for LLM perf differs. Can you be more specific about the case where you achieved this and share technical details about how do you do that?