Skip to page content
SAVRN
Search Plan a project

Independent publisher

Lotto

Lottolabs

Models in Library2
Datasets in Library0
Models on Hugging Face5
Followers3

Compare the published evidence

This profile connects the 2 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.

Published tasks: Text generation.

Reported licenses: apache-2.0, other. Open a record to review its specific license and conditions.

Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.

Models

A hardware-specific, text-only checkpoint and custom runtime derived from Qwen/Qwen3.6-35B-A3B, revision 995ad96eacd98c81ed38be0c5b274b04031597b0, for one Tenstorrent Blackhole P150. This is a community experimental release by Lottolabs, not an official Qwen or Tenstorrent release. It is not GGUF, AWQ, an AutoGPTQ/GPTQModel int4 checkpoint, bitsandbytes, NVFP4, or an ordinary Transformers checkpoint. transformers.frompretrained() and hosted Inference Providers are not supported launch paths. The vision encoder of the upstream model is not included: the server accepts text only. - Checkpoint (checkpoint/, 21.7 GB, manifest sha256 439096088c8b…): - routed experts (256 per layer × 40 layers)…

Open weights apache-2.0
View model

Model · Text generation

Qwen3.8-Flash-Next-Coder-P150

Lotto

Block-float weights that let Strata's tt backend run Qwen3.8-Flash-Next Coder on one Tenstorrent Blackhole P150 card: - the routed experts of ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF (IQ1M), 48 layers x 256 experts x gate/up/down, re-quantized with GPTQ to Tenstorrent's BFP4 (bfp4b) format, in the exact byte layout the P150 kernels read; - the precision/placement plan bfp4-tiered-2.4e+10: which experts live in the card's DRAM and which in pinned host memory; - the MTP draft layer's 512 routed experts in BFP4 and their routing profile (speculative decoding). This is not a standalone model. It holds no tokenizer, attention, embedding or LM-head weights: those come from the GGUF that…

Open weights other
View model