Alvaro Bartolome

Also to mention that there's a v2 of the dataset ahead and anyone can contribute via https://huggingface.co/spaces/DIBT/prompt-collective signing in with their HuggingFace Hub account! 🤗

P.S. Thanks everyone for the great community effort already done!

indeed https://huggingface.co/CohereForAI/aya-101 and https://huggingface.co/datasets/CohereForAI/aya_dataset

Fair, maybe you can go ahead and merge the PEFT adapter into the model, as otherwise you may be getting those OOM, anyway, feel free to report that in the community tab.

Also w.r.t. prompting, the original model was fine-tuned for instruction following, but concatenating the messages can be used for chat applications, so yes, after the system and user prompt the assistants response goes there and then the upcoming turns are appended to that string using the same [INST] notation.

Indeed we’re serving this via TGI, so that doesn’t seem to be the issue, with Inference Endpoint do you mean HuggingFace’s service right? Could you let me know which GPUs are you using?

Hey sorry to hear that, is it due to the resources required to run it right? Maybe you can ask HuggingFace for a quota increase so that you can allocate the required resources 🤗 Let me know if there's anything I can help you with!

@Sylvestre 🐐

Could that be related to https://huggingface.co/spaces/social-post-explorers/README/discussions/15? Thanks for the quick update BTW

Hi @abhishek , I think the link you pasted in the post is broken! Did you mean https://huggingface.co/blog/stefan-it/autotrain-flair-mobie? (it's the only blog I saw posted within the 🤗 Hub from @stefan-it )

In fact, to add more context, the authors mentioned that they will release some more content in the upcoming revision of the paper which is nice, because that would imply that anyone could run a faithful reproduction of their synthetic data generation process. See the reply from the authors at https://huggingface.co/papers/2401.00368#65978d195f689f3f0b2caeb9.

Also worth mentioning that @andersonbcdefg ran both stages:

• Task definition generation at https://huggingface.co/datasets/andersonbcdefg/synthetic_retrieval_tasks
• query-pos-neg triplets generation at https://huggingface.co/datasets/andersonbcdefg/synthetic_tuples_gpt35_turbo

(Unsure if the reproduction of the second stage is faithful to the original, but asked them at https://twitter.com/alvarobartt/status/1742839431881490717, anyway I think we may need to wait for the authors to share the full details on the prompting strategies for the generation).

MT-Bench is on par with mistralai/Mixtral-8x7B-Instruct-v0.1 which means ~8.3, and we didn't run AlpacaEval yet

We may need to discuss that internally, but could be something we may consider for opening 2024 🤗

Yes, indeed for anyone wondering, a bit more into detail, Mixtral-8x7B-Instruct-v0.1 was fine-tuned using SFT + DPO (read more about it at https://mistral.ai/news/mixtral-of-experts/) and we ran the DPO fine-tuning on top of it, but using data from UltraFeedback, in particular argilla/ultrafeedback-binarized-preferences-cleaned, but using a different binarization approach and applying some data cleaning, but essentially following the same approach as @HuggingFaceH4 did with zephyr-7b-beta.

So DPO ^ 2 is fair! 😄

Also thanks to @osanseviero for granting me access for the posts private beta! 🦙