AI Architect & Builder - Inference, LLMs, SLMs GSoC Org Admin - Jenkins | Docker Captain | CNCF Ambassador
New Delhi
Joined December 2018
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What an amazing day! Presented a poster at @PyTorch Conference: how we optimised the RLM paper with pre-fix caching & batched Sub calls with @vllm_project
And a full house for our Pytorch Conference talk on running LLMs using Executorch on Android!
Also great to catch-up with some of the best folks in the business after such a long time:
@kentcdodds @ossia @fharper @SwiftAlphaOne
This is only possible at ATO 🫡
Had a wonderful time presenting 2 talks at @AllThingsOpen:
1. How do you secure coding agents using @Docker Sandboxes
2. LLM inference on edge using @PyTorch Executorch and LiteRT for @Qualcomm_Dev QNN
Always have wonderful discussions and cross sharing of feedback at ATO!
Shivay Lamba retweeted
New research: We developed JOLT, a post-training method where one model acts as both teacher and student, with the teacher trained to give feedback the student can use.
On MATH, it matched GRPO's accuracy with 6.5x fewer training tokens.
perplexity.ai/hub/blog/a-goo…
Shivay Lamba retweeted
Today we release mammoth-1 and elephant-1!
We post-trained mammoth-1, the #1 ranked best decision model in the world, and elephant-1, which is the best model under 15B (and beats Jev).
We're open-sourcing all model weights and provide a technical report detailing all our training and data methods.
I built Kubetrace - using Beam with @nebiustf
xitter.cf/HowDevelop/status/2111…
An AI coding agent becomes useful when it can investigate a problem, change the code, test the fix, and follow through to a working deployment.
That’s what I wanted to explore with the newly launched @reflection_ai Beam 🚀
Beam is built for coding, reasoning and agentic workloads. KubeTrace AI puts those capabilities to work on @kubernetesio incidents, with inference through @nebiustf.
The agent can:
→ Investigate logs, events and application code
→ Trace a failure to its cause
→ Propose code or Kubernetes manifest changes
→ Run regression tests in a sandbox
→ Build and deploy the repaired application
→ Check whether it recovered
The execution layer checks every proposed change against policy, and hidden checks decide whether the incident is resolved. You can inspect the evidence, patches, test output and failed attempts.
Shivay Lamba retweeted
We teamed up with @huggingface to give away $20,000 to anyone who's building with Beam 🤗
Use Beam for any project - make a demo or a dataset, benchmark the model, create an RL environment - and comment your link in the model's repo page by Monday, November 2.
We choose the winners. Winners choose cash or credits. Claim $25 in Hugging Face credits to build with Beam and enter the competition 👇
huggingface.co/reflection/Be…
Shivay Lamba retweeted
Inspired by @BhosalePratim's Macbrow, I built Winbrow: a Windows voice assistant.
@GradiumAI speech + @typesafeai Jev routing + browser automation adapted from @browser_use’s Jev Ultrafast project.
@Alibaba_Qwen 4B LLM handles replies. Phonon (TTS) speaks them both running locally on @Qualcomm_Dev X Elite’s NPU.
Built for Phonon’s OSS launch!
I've always wanted a way to talk to my Mac and do things on the go.
Until now, it was super slow.
So I built Macbrow.
Voice-controlled browser and computer-use agent.
Built using @GradiumAI @browser_use and @typesafeai
Still early and a work in progress.
github.com/timpratim/macbrow
Shivay Lamba retweeted
Model customization recipes are live on our website and in the cookbook repo.
Beam is now supported across RL and SFT stacks including @axolotl_ai, NeMo RL, NeMo AutoModel, Megatron Bridge (@NVIDIAAI), Miles (@radixark), SkyRL (@NovaSkyAI), and TRL (@huggingface).
Get cooking 🧑🍳
developers.reflection.ai/cus…
