# Ethan Todd: Open-Weight Models and Datasets
Source: https://savrn.com/model-publishers/ethantodd4l
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Models

Model · Image and text to text

### [Swift-1.5-Qwen3.8-27b-FP8](https://savrn.com/models/swift-1-5-qwen3-8-27b-fp8)

[Ethan Todd](https://savrn.com/model-publishers/ethantodd4l)

quantized to FP8 with the same recipe and checkpoint format as the official Qwen/Qwen3.8-27B-FP8: weights in FP8 E4M3 with 128x128 block scales, activations dynamically quantized (activationscheme: dynamic), serialized in the compressed-tensors/ quantmethod: fp8 layout that vLLM loads directly. modulestonotconvert is a superset of the reference checkpoint's list (1111 vs 882 entries): every module that is BF16 in this checkpoint is listed, and the quantized tensor set is exactly the reference's. Tensor inventory matched against Qwen/Qwen3.8-27B-FP8: identical 1606 keys (407 F8E4M3 + 1199 BF16), identical quantized module set, no name or shape differences. Served with vLLM Radiance (vLLM…

Open weights other 27.8B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/swift-1-5-qwen3-8-27b-fp8)

Model · Image and text to text

### [Swift-1.5-Qwen3.8-27b-Quark-RTN-MXFP4](https://savrn.com/models/swift-1-5-qwen3-8-27b-quark-rtn-mxfp4)

[Ethan Todd](https://savrn.com/model-publishers/ethantodd4l)

quantized to OCP MXFP4 4-bit weights in the same Quark checkpoint container as The weights are plain RTN, not AWQ: see the note below. The 15 MTP tensors are kept in BF16; the radiance runtime loads them with RADIANCEQUARKBF16MTP=1. Why RTN instead of AWQ. The first build of this checkpoint used AWQ with the same smoothing recipe as AMD's release. The AWQ fold itself was mathematically consistent, but on this model a few layers converged to weights to 32-element MXFP4 blocks then destroyed those layers on real, outlier-carrying inputs (layer 7 output relative error ~324), and repairing the worst layers individually was not enough; the remaining smoothed layers still accumulated too much…

Open weights other 15.6B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/swift-1-5-qwen3-8-27b-quark-rtn-mxfp4)

## Explore More

- [All model publishers](https://savrn.com/model-publishers)
- [The model directory](https://savrn.com/models)
- [The dataset directory](https://savrn.com/datasets)

## Source

- Listed from their public repositories, read 2026-10-04.
- [Hugging Face profile](https://huggingface.co/ethantodd4l)
- [How the hub is built](https://savrn.com/model-hub/methodology)