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TokenAI-zer/Swift-Qwen3.8-27b-oQ8-mtp
Swift-Qwen3.8-27b-oQ8-mtp is a image-text-to-text model from TokenAI-zer. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as other.
Unofficial 8-bit MLX quantization of ukisai/Swift-Qwen3.8-27b for Apple Silicon. The MTP head is retained. This is an MLX safetensors repository, not GGUF or PyTorch.
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From the Hugging Face model README
Unofficial 8-bit MLX quantization of ukisai/Swift-Qwen3.8-27b for Apple Silicon. The MTP head is retained. This is an MLX safetensors repository, not GGUF or PyTorch.
The source model and its license belong to UkisAI. I am not affiliated with UkisAI. See License and attribution before using or redistributing these weights.
| Property | Value |
|---|---|
| Quantization | 8-bit affine, group size 64 |
| Weight size | 27.94 GiB (30.00 GB) across 6 safetensors shards |
| Whole model directory | 27.96 GiB (30.02 GB) |
| Architecture | Qwen3_5ForConditionalGeneration, with vision tower |
| Context limit in config | 262,144 tokens; usable context depends on available memory |
| MTP | 1 hidden layer and 29 tensors in the weight index |
These values come from the shipped config.json, model.safetensors.index.json and local files. The quantization_config has a uniform 8-bit setting; unlike the oQ4 and oQ6 variants, it contains no module-specific bit overrides. Quantization is lossy, and this variant has no published post-quantization benchmark results.
| oQ4-mtp | oQ6-mtp | oQ8-mtp | |
|---|---|---|---|
| Weights on disk | 15.81 GiB (16.97 GB), 4 shards | 22.09 GiB (23.72 GB), 5 shards | 27.94 GiB (30.00 GB), 6 shards |
| Weight precision | mixed 4/5-bit | mixed 6/8-bit | uniform 8-bit |
| Quantization | affine, group size 64 | affine, group size 64 | affine, group size 64 |
| Choose when | prioritize memory headroom | balance size and weight precision | prioritize weight precision and have more memory |
All three retain the MTP head. Task-level accuracy and speed after quantization have not been measured here. Runtime memory also depends on context length and KV cache.
Download the repository into the oMLX model directory:
hf download TokenAI-zer/Swift-Qwen3.8-27b-oQ8-mtp --local-dir ~/.omlx/models/Swift-Qwen3.8-27b-oQ8-mtp
omlx serve --model-dir ~/.omlx/models --port 8000
The model ID is Swift-Qwen3.8-27b-oQ8-mtp. In oMLX, enable the MTP speculative head to use the included MTP weights. Without that setting, the model can still run but the extra head does not accelerate decoding.
The architecture is multimodal. Use an MLX runtime with Qwen3.5 vision support. The 262k context value is an architecture limit, not a memory guarantee.
The shipped generation_config.json comes from the upstream checkpoint. Start with its settings for thinking mode: temperature 1.0, top_p 0.95, top_k 20 and repetition_penalty 1.0. Adjust for your task after checking output quality.
The upstream model identifies its license as Swift Open License v1.0. Its published terms apply to this derivative quantization; this repository does not grant a separate license to the upstream weights. See LICENSE-NOTICE.md for attribution and upstream links.
@misc{swift-qwen3.8-27b-oq8-mtp,
title = {Swift-Qwen3.8-27b-oQ8-mtp: 8-bit MLX quantization with MTP head},
author = {TokenAI-zer},
year = {2026},
url = {https://huggingface.co/TokenAI-zer/Swift-Qwen3.8-27b-oQ8-mtp},
note = {Unofficial quantization of ukisai/Swift-Qwen3.8-27b}
}