← back to catalog · registered 2026-10-29 09:01

haihengh/Qwen3.8-Flash-Next-125B-finchmoe-4bit-ple4bit-abliterated-vision

haihengh 125B MoE multimodal second-order
Your rig guess connected
? Why do I need an app?
Reading your rig…

This is a rough estimate. Install the free app - we'll show exact numbers.

Reading real hardware from your app right now. Numbers below are exact.

Below is the per-quantization compatibility for this model.

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/haihengh%2FQwen3.8-Flash-Next-125B-finchmoe-4bit-ple4bit-abliterated-vision"
Response includes
  • classification m1
  • files 6
  • author_summary 10 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
Downloads · 30-day
0
Likes
0
Model age
today
created 2026-10-29

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
other
Tags
finchmoe finch qwen3.8 qwen moe quantized vision image-text-to-text 4-bit abliterated base_model:windowsxp811203/Qwen3.8-Flash-Next-Abliterated base_model:finetune:windowsxp811203/Qwen3.8-Flash-Next-Abliterated

Related

Total size
3.61 GB
Files
6
Quantizations
1
Registered
2026-10-29 09:01
Last updated on HF
2026-10-29 06:51

Files by quantization

Auxiliary files 6 files 3.61 GB
model_weights.bin 3.61 GB 6af82b55 download
manifest.json 31.2 KB 72b94bed download
verified-install.json 28.6 KB ad61931e download
README.md 4.70 KB f880ad98 download
LICENSE 3.16 KB 9557a896 download
.gitattributes 1.60 KB 5f70f778 download

README current version from Hugging Face


license: other
license_name: qwen-community-license-1.0
license_link: https://huggingface.co/haihengh/Qwen3.8-Flash-Next-125B-finchmoe-4bit-ple4bit-abliterated-vision/blob/main/LICENSE
base_model: windowsxp811203/Qwen3.8-Flash-Next-Abliterated
pipeline_tag: image-text-to-text
library_name: finchmoe
tags:

  • finch
  • finchmoe
  • qwen3.8
  • qwen
  • moe
  • quantized
  • vision
  • image-text-to-text
  • 4-bit
  • abliterated

Qwen3.8-Flash-Next-125B Abliterated — FinchMoE 4-bit routed experts + vision payload

This repository holds a .finch install for the out-of-core
FinchMoE engine and its Mac/iOS apps —
not a Transformers checkpoint. It is the Qwen3.8-Flash-Next-125B checkpoint repacked with
routed experts at 4-bit, with the source vision tower carried along.

  • Source: windowsxp811203/Qwen3.8-Flash-Next-Abliterated at the BF16 snapshot recorded below.
  • Text body: attention / shared experts at 4-bit, linear attention and router at 8-bit, PLE table at int4 group 32; routed experts 4-bit affine
    (group 64, BF16 scales/biases).
  • Vision: the source model.visual.* tower — 333 BF16 tensors,
    856.3 MiB
    — copied byte-for-byte into vision/vision.safetensors,
    with the source config and preprocessor sidecars. The FinchMoE vision runtime
    consumes it; text-only consumers can ignore it.
  • Text identity: the text bytes are the same recipe as the published text
    sibling haihengh/Qwen3.8-Flash-Next-125B-finchmoe-4bit-ple4bit-abliterated; this
    repository adds the vision payload only.

Artifact facts

Format Finch .finch (manifest + resident weights + packed experts + tokenizer + vision)
Total declared bytes 104,900,675,753 (97.70 GiB)
Resident weights 3,877,265,688 bytes
Routed experts 512 per layer × 48 layers, stride 2,768,896 bytes
Expert pool 68,048,388,096 bytes (63.38 GiB)
Tokenizer 22,916,963 bytes
Vision payload 897,904,664 bytes (856.3 MiB)
manifest.json SHA-256 cc9a0f111f43f124d279f5c683a0a47f57eed42b73f6f5c640211dd68890c7bb
Source snapshot sha256:99e815241ef03325536b0aaa4441deea45174c17fae31e10f0bb456410c590de

File layout

manifest.json                     file set, sizes, SHA-256, architecture, quantization
model_weights.bin                 resident weights
packed_experts/layout.json        expert tensor map (51.5 MiB)
packed_experts/layer_XX.bin       one routed-expert pool per layer
tokenizer/                        tokenizer.json, config, merges, vocab, chat template
vision/model_config.json                      4745 B  source vision tower and configs
vision/preprocessor_config.json                390 B  source vision tower and configs
vision/video_preprocessor_config.json          385 B  source vision tower and configs
vision/vision.safetensors                  856.3 MiB  source vision tower and configs

The repository carries the verified-install.json receipt generated while
verifying the published payload; the installer still re-verifies every file
against manifest.json.

Quality and performance

This variant is not separately measured. Its text weights are the same recipe
as haihengh/Qwen3.8-Flash-Next-125B-finchmoe-4bit-ple4bit-abliterated; use that card
for the measured quality and throughput of the text path. The vision tower adds
about 856.3 MiB of BF16 weights, mapped only while an image is being
encoded.

Usage

FinchMoECLI --model /path/to/Qwen3.8-Flash-Next-abliterated-ple4bit-vision.finch \
            --messages-file messages.json --max-new 128

The Mac/iOS app needs a vision-capable model row for image turns; a text-only
row will reject the vision/ directory rather than silently ignore it. Until
this repository is added to the app's picker, use the CLI or point the app at
the directory directly.

Provenance

Repacked with FinchMoERepack on 2026-10-28:

FinchMoERepack --input-snapshot <Qwen3.8-Flash-Next-125B-bf16> \
  --output Qwen3.8-Flash-Next-abliterated-ple4bit-vision.finch --routed-expert-bits 4 --include-vision

The source snapshot hash is recorded in the manifest above. The abliteration,
where applicable, was done upstream (windowsxp811203/Qwen3.8-Flash-Next-Abliterated); this repository changes
only the container, the quantization, and the added vision payload.

License

See LICENSE. Weights are derived from the upstream model named in
base_model; this repository is a derivative work.

Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration