Now

What I'm working on this month. It changes, so it carries a date.

Updated

tiyuvta

Most of my time

I help companies choose, adapt and deploy open models on their own hardware or cloud account. We compare options on the customer's workload and budget, then agree the deliverables, handover and support before the work starts.

  • Model and hardware assessmentKnow what to run before buying hardware: candidate comparison, workload baseline, cost recommendation.
  • Deployment and optimizationFit the model and serving engine to hardware within the budget: installed configuration, measurements, operating instructions.
  • Fine-tuning and evaluationMeasure the quality gap, then train where the evidence supports it: evaluation set, candidate comparison, deployment artifacts.

The lab's models are on Hugging Face: NVFP4 builds of GLM, DeepSeek, MiMo, Qwen and DictaLM, MTP draft heads in GGUF, and Phonon-2 in ONNX.

Discuss a project

Research

One study running, one released

Negative-eigenvalue gates for hybrid models. The retrofit works at 4B and 9B: +48 and +43 points on state tracking at 4x the training length, over LoRA alone. I'm scaling it to Qwen3.8-27B, where the current run trains with thinking on and measures accuracy and reasoning tokens per task.

Layby, released. KV cache placement for idle LLM sessions by when they come back. On a slow disk with Qwen3-8B in vLLM, p95 time to first token at 0.59x of an LRU CPU tier with the median unchanged, and 0.49x of the better of two LMCache settings. Code, model and benchmark are open; the paper goes to arXiv next.

Maintaining

Every week

Valkey GLIDE and valkey-skills: review, releases and support. The agent-sh tools, mainly agnix and parlar. My day job is in-memory data systems at AWS ElastiCache.

Earlier

Kept for the record

Research snapshot from 17 September 2026

The main study then was StudentSV: a half-width MTP block with its own 32,768-token output head, trained for the real speculative-decoding path instead of inheriting the target model's full 248,320-row projection.

It concluded against the student for a functional reason, not a size one. Fidelity cuts (precision) cost at most 1.9 acceptance-retention points even under severe shift, while function cuts (pruning, low-rank and the distilled student) keep 97% on calibration-style text and lose 10 to 19 points where the text actually shifts. The recipe that survived needs no training: trim the vocabulary to the model's own 32k most used rows, quantize to NVFP4, verify against the full vocabulary. The working paper has the final numbers.

Other threads at the time: memra (v0.69 then), recipe-lab, mem-retrofit, the REAP and quantization work, revuto and Valkey GLIDE.