Downloads · 30 days
62
19% of all-time downloads
junafinity/qwen38-mtp-head-fc-bf16-4bit
qwen38-mtp-head-fc-bf16-4bit is a text generation model from junafinity. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
Downloads · 30 days
62
19% of all-time downloads
All-time downloads
329
Public
Parameters
111M
314 MB on disk
Likes
1
Public
Click a slice to open those files.
.safetensors314 MB · 100%
How the weights are stored.
BF1664.1M · 58%
From the Hugging Face model README
<a href="https://verisafe.live"><img src="https://huggingface.co/junafinity/qwen38-mtp-head-fc-bf16-4bit/resolve/main/assets/verisafe-logo.png" alt="VeriSafe.Live" width="120"/></a>
<a href="https://verisafe.live"><img src="https://img.shields.io/badge/Built%20by-VeriSafe.Live-00bc8c?style=for-the-badge" alt="Built by VeriSafe.Live"/></a> <img src="https://img.shields.io/badge/License-apache--2.0-blue?style=for-the-badge" alt="License"/> <img src="https://img.shields.io/badge/Format-Safetensors-informational?style=for-the-badge" alt="Format"/> <a href="https://verisafe.live"><img src="https://img.shields.io/badge/Crypto-Accepted-f7931a?style=for-the-badge" alt="Crypto Accepted"/></a> <a href="mailto:[email protected]"><img src="https://img.shields.io/badge/Contact-info%40verisafe.live-0c1117?style=for-the-badge" alt="Contact"/></a>
</div>| Offering | What you get |
|---|---|
| 🎯 Custom fine-tuning | Any open-weights model adapted to your data, domain and tone. |
| 🧪 Training from scratch | Purpose-built models designed and trained for your use case. |
| ⚡ Quantization (MLX / GGUF) | Lean builds that run fast on Apple Silicon and llama.cpp. |
| 🌐 Hosted API access | Instant access to served models, with no wait time. |
| 🔓 Uncensored models | Refusal-removed builds for research, red teaming and evaluation. |
🚀 Now live at VeriSafe.Live: uncensored DeepSeek V4.1 Flash 1017, GLM 5.3 Flash and GLM 5.3. No wait time, instant access, crypto accepted.
fc in bf16 and quantizes the other seven linears to 4-bit/g64.fc, 4-bit/g64 elsewhere)A drafting head for Qwen 3.8 27B native-MTP speculative decode on MLX /
Apple Silicon. This is an optional experiment, not a speed product. Acceptance on hard, varied prose is unmeasured. Do not quote tok/s for this file. Derived from the organizer-pinned
EigenLabs/Qwen3.8-27B-MTP-bf16 @ 26a328e070875b0314d652a039b6b59902690f03.
Hub collection: https://huggingface.co/collections/junafinity/qwen-38-27b-uncensored-apple-silicon-6a896c726b52be3a0b63400e
This is a drafting head only, not a standalone model. Pair it with a Qwen3.8-27B MLX backbone (for example Qwen-3.8-27B-Uncensored-8-Bit-MLX) in a loader that accepts a scales-keyed mixed-precision head.
backbone: junafinity/Qwen-3.8-27B-Uncensored-8-Bit-MLX
draft head: junafinity/qwen38-mtp-head-fc-bf16-4bit
Exact loader flags: [PLACEHOLDER] (depends on your mlx / speculative-decode entrypoint). Acceptance-rate benefit on hard, varied prose is unmeasured; see the caveat below.
Existing 4-bit heads for this model quantize all eight head linears. This one
keeps fc in bf16 and quantizes only the other seven (q/k/v/o_proj,
gate/up/down_proj) to MLX affine 4-bit, group size 64 — the geometry that
matches the 4-bit backbone, so a stock scales-keyed loader converts exactly
the modules that carry a .scales sibling and leaves fc a dense Linear
with no code changes.
fcQwen's own official FP8 release, Qwen/Qwen3.8-27B-FP8, lists mtp.fc in
modules_to_not_convert — it declines to quantize that matrix while
quantizing the head's other large linears. fc is the projection that fuses
the embedding stream with the backbone hidden state
(concat[embed, hidden] at 10240 → 5120), so representation error there
propagates into every downstream head computation at every draft position.
This artifact tests whether respecting that exclusion improves draft
acceptance enough to pay for the extra bandwidth.
fc in bf16 is 104.9 MB versus 29.5 MB at 4-bit — about +75 MB of weight
traffic per draft step (total head read ≈ 314 MB vs ≈ 239 MB). Break-even
needs only a small acceptance gain at mid/deep draft positions, since an
accepted draft token amortizes a full ~15.6 GB target verify pass on this
dense 27B model.
29 tensors: fc.weight (bf16, [5120, 10240]), seven bf16 norm vectors, and
weight/scales/biases triples for the seven quantized linears. Single
model.safetensors (314,300,360 bytes) plus config.json and an index,
carried unchanged from the pinned head so the head loader accepts the tree.
Short local decode windows over a model's own greedy self-continuation saturate draft acceptance (measured: effective draft length 5.4, accept rate 1.000, identical for this head and a uniformly 4-bit one). Such a setup can measure this artifact's cost but not its benefit; evaluation needs harder, more varied prose where acceptance is genuinely below 1.
If your company or research institute would like us to fine-tune any open-weights model, or train one from scratch, we'd be happy to help.
<a href="mailto:[email protected]"><img src="https://img.shields.io/badge/Email-info%40verisafe.live-00bc8c?style=for-the-badge" alt="Email us"/></a> <a href="https://verisafe.live"><img src="https://img.shields.io/badge/Visit-verisafe.live-0c1117?style=for-the-badge" alt="Visit"/></a>
</div><sub>Built and published by VeriSafe.Live. Licensed under apache-2.0. Base model: EigenLabs/Qwen3.8-27B-MTP-bf16 (original authors retain their attribution and license).</sub>