Mahesh Sathiamoorthy @madiator
CEO @bespokelabsai. RL/Envs/Posttraining/Agents. Created Generative Retrieval at @GoogleDeepMind. smahesh.com Inside a RL Environment Joined February 2008-
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@aisearchio Thanks for covering the work!
Introducing Bespoke Nimble: an open data, open model, open recipe for an open Jev. Code and info: github.com/bespokelabsai/… Model: huggingface.co/bespokelabs/Be… Data: * A new data curation recipe called contrastive data curation. * Slightly change facts to generate negative data. This
@airesearch12 Hi! I had shorter context windows earlier which was causing errors for your benchmarking I believe. Fixed it in github.com/bespokelabsai/…. Is it possible to rerun?
Nimble is able to process images! This is a confusing floor plan where there are three bedrooms marked as Bedrm 1, Bedrm 3, Bedrm 4. Nimble is able to count the bedrooms and bathrooms without a hiccup, while the base Qwen struggles. Jev doesn't have multimodal support so i didn't check it. Oh damn, what have I done! :D
Introducing Bespoke Nimble: an open data, open model, open recipe for an open Jev. Code and info: github.com/bespokelabsai/… Model: huggingface.co/bespokelabs/Be… Data: * A new data curation recipe called contrastive data curation. * Slightly change facts to generate negative data. This
@EGafni Everyone loves you guys, but can you change this clause? That's making it sour all of a sudden.
congrats! but you violated terms of use already. (tough terms, but makes sense in this climate)
@QuixiAI Here
Introducing Bespoke Nimble: an open data, open model, open recipe for an open Jev. Code and info: github.com/bespokelabsai/… Model: huggingface.co/bespokelabs/Be… Data: * A new data curation recipe called contrastive data curation. * Slightly change facts to generate negative data. This
@marcelpociot Maybe this can be done locally without needing Internet!
Introducing Bespoke Nimble: an open data, open model, open recipe for an open Jev. Code and info: github.com/bespokelabsai/… Model: huggingface.co/bespokelabs/Be… Data: * A new data curation recipe called contrastive data curation. * Slightly change facts to generate negative data. This
@varun_mathur Maybe not a good idea for them yet.
Introducing Bespoke Nimble: an open data, open model, open recipe for an open Jev. Code and info: github.com/bespokelabsai/… Model: huggingface.co/bespokelabs/Be… Data: * A new data curation recipe called contrastive data curation. * Slightly change facts to generate negative data. This
@nvmsyy @AlexGDimakis Ah ok. I was talking about the 4B I was working on. How does Jev do on those tasks?
@nvmsyy @AlexGDimakis No the 4B was worse.. Also fair point. Jev also says it's 100% synthetic data 😅
@sghosh67 @suchenzang Typesafe can suspend my account I guess.. Uber driver is not the right example because the MSA is with Uber, not the driver.
You can just do things* *with Astra and Fable on your side, and the limits are reset.
@levilais I tried and it had same performance as 9B. Maybe it needs more data!
Introducing Bespoke Nimble: an open data, open model, open recipe for an open Jev. Code and info: github.com/bespokelabsai/… Model: huggingface.co/bespokelabs/Be… Data: * A new data curation recipe called contrastive data curation. * Slightly change facts to generate negative data. This pushes the model to discriminate better and become a better decision maker. The calibration is implicit. * Didn't do ablations but I think this is a critical piece! * This also means training data doesn't need probabilities. * Data covered 10 categories, and is fully synthetic. * This data is split into train and eval. Training * LoRA finetune of Qwen3.5-9B. * Distillation-free: we use Jev to only evaluate. * No RL yet! Serving * Parallel constrained decoding as suggested by @NielsRogge and @harshagundal. Results: * The post-trained Qwen (Nimble) became substantially better on our curated eval: 66% for Qwen to 90% for Nimble. Jev is at 93%. * 100ms on H100 and free to use on your macbook! Feel the AGI for free. * 2 days of building in public. :) Big caveat is that there is no standard benchmark to measure performance, and it's possible Nimble is much worse on other benchmarks compared to Jev. But it should be better than Qwen! We thank @typesafeai for making Jev and the inspiring discussions in the community. Hope this release lifts all the boats and encourages more research and activity in this space.
@cloudyaiorg We are in the together :)
@_Eddited_ I should have done that! Was sleep deprived I think!
@stingraynbow I have explained it in the GitHub link
@deepgandhi_07 Ah it's in the GitHub link!
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