Built with qwowl35 to optimize inference on Apple Silicon. The qwowl35 cooking tool bakes a base Qwen3.5 GGUF (Q4_K_M / Q5_K / Q6_K FFN) into a single self-contained .gguf whose FFN gate/up/down tensors are stored as GF4 (eight 3-bit quants plus an fp8 scale per 32-bit word, GGUF type-id 100) and whose post-attention norms carry an AWQ per-channel scale fold. Every other tensor is copied verbatim. The result is what the server loads by default — Qwowl3.5-9B.gguf, cooked from unsloth/Qwen3.5-9B-Q4_K_M.gguf.

Qwowl3.5-9B is the dense, text path of Qwen3.5-9B re-packed for local inference with qw35, a Metal-backed engine built for agentic loops on a MacBook. It is the same model as the base GGUF — same weights, same tokenizer, same chat template — nothing is retrained. The only change is how the FFN weights are stored (GF4, a uniform 4-bit grouped floating-point format) and the folding of a one-time AWQ calibration scale into the post-attention norms, so the engine serves both prefill and decode from one mmap. It is a thinking-capable, tool-calling model: a turn may open with a hidden <think> … </think> reasoning block, and tool calls are emitted as compact Qwen3 XML inside <tool_call>. It keeps Qwen3.5's 262K native context (the server defaults to a 128K window for the throughput sweet spot).

[!Note] This repository contains the model weights as a custom GGUF file.

Qwowl3.5-9B.gguf is a qw35-specific artifact: its FFN tensors use GF4 (GGUF type-id 100), a custom quantization that standard GGUF loaders (e.g. stock llama.cpp) do not understand. It is built for qw35's Metal runtime and therefore runs only on Apple Silicon (M1 or newer, macOS 14+). There is no CUDA, CPU, or cross-platform path.

Cooking details

The cook touches only two families of tensors and copies everything else through untouched:

Tensors	Base `Qwen3.5-9B-Q4_K_M.gguf`	Unified `Qwowl3.5-9B.gguf`
FFN `ffn_gate` / `ffn_up` / `ffn_down` (96 tensors)	Q4_K / Q6_K	GF4 (type-id 100): eight 3-bit quants + one fp8 (e5m2) scale per 32-bit word, 4 bpw
`post_attention_norm` (32 tensors)	as shipped	AWQ-folded: the inverse of the per-input-channel AWQ scale is baked into the norm weights
Everything else (attention, Gated DeltaNet, embeddings, other norms, KV metadata, chat template)	—	copied verbatim

GF4 drives both the single-token decode matvec and the tiled multi-token prefill matmul, so there is no separate sidecar and no duplicate Q4_K FFN. AWQ scales the salient input channels up before GF4 quantization (giving them finer resolution) and folds the inverse into the preceding norm, so the layer's full-precision output is unchanged while the quantized weights spend their bits where the activations are largest.

Quality is measured by teacher-forcing the calibration corpus through both the base GGUF and the cooked model and comparing logits (real_model_unified_quality_report, M2 16 GB). AWQ-GF4 wins on every axis at identical decode speed, which is why it is the canonical build:

Cooked variant	top-1 agreement vs base	mean KL	argmax flips
AWQ-GF4 (`Qwowl3.5-9B.gguf`)	89.8%	0.038	5/49
plain GF4 (no AWQ)	85.7%	0.062	7/49

Property	Value
Predominant precision	4-bit (GF4 FFN, uniform 4 bpw)
AWQ scale	`s_c = act_c^0.6`, geomean-normalised, folded into post-attention norms
KV cache	`q8_0` (default) or `f16`
Decode throughput	~17 tok/s (M2, 128K ctx)
Greedy parity	byte-identical to llama.cpp on the same base GGUF
Base size	5,680,522,464 B (≈ 5.29 GiB)
Unified size	5,170,914,624 B (≈ 4.82 GiB), ~0.47 GiB smaller

The unified file is smaller because GF4 is a flat 4 bpw versus Q4_K's ~4.5 bpw, and the AWQ fold adds no bytes (it reuses the existing norm tensors).

Usage

Grab the cooked weights from this Hugging Face repo (or cook them yourself) and drop the file in .gguf/ so the server's default path resolves:

huggingface-cli download EmilioSchi/Qwowl3.5-9B Qwowl3.5-9B.gguf --local-dir .gguf

Then load it with qw35 and use it as usual. Build and run the server:

make run # or: cargo run --release -p qw35-server --bin qw35

or compile once and run the binary directly:

bash

cargo build --release
./target/release/qw35

The server seeds its sampling and think/no-think defaults from an official Qwen3.5 profile, selectable with --mode (default: thinking-coding):

qw35 --mode thinking-coding

Profile (`--mode`)	thinking	temperature	top_p	top_k	presence	repeat
`thinking-general`	on	1.0	0.95	20	1.5	1.1
`thinking-coding`	on	0.6	0.95	20	0.0	1.1
`instruct-general`	off	0.7	0.80	20	1.5	1.0
`instruct-reasoning`	off	1.0	0.95	20	1.5	1.0

Pick the profile by mode and task type (per-request parameters override them):

Thinking mode, general tasks → thinking-general
Thinking mode, precise coding (e.g. WebDev) → thinking-coding
Instruct (non-thinking) mode, general tasks → instruct-general
Instruct (non-thinking) mode, reasoning tasks → instruct-reasoning

In another terminal, launch the qwowl35 TUI agent (it talks to the server on 127.0.0.1:8080):

bash

pip install -r qw35-agent/requirements.txt    # textual, rich, httpx, xxhash
cd qw35-agent && python -m qwowl35

Cook by yourself (optional)

The cooked Qwowl3.5-9B.gguf is published on Hugging Face, so most users can just download it (see Usage). Cooking it yourself is optional — useful if you want to re-quantize from a different base GGUF or reproduce the build. The downloader script wraps the whole flow:

bash

./download_model.sh         # base GGUF -> .gguf/Qwen3.5-9B-Q4_K_M.gguf (~5.3 GB)
./download_model.sh cook    # cook the unified Qwowl3.5-9B.gguf (CPU-heavy)
./download_model.sh all     # both

cook runs the AWQ-GF4 cooker, which needs the AWQ activation statistics (act-stats.bin, per-channel mean-abs FFN activations captured once from the base model over a calibration corpus):

bash

# capture activation stats first (if act-stats.bin is missing)
cargo test -p qw35-server --lib real_model_capture_activations -- --ignored

# then cook (this is what `download_model.sh cook` invokes)
python3 tools/cook_qw35_awq_gf4.py \
    .gguf/Qwen3.5-9B-Q4_K_M.gguf .gguf/Qwowl3.5-9B.gguf --awq .gguf/act-stats.bin

Per FFN tensor, the cooker dequantizes the Q4_K/Q5_K/Q6_K source → multiplies each input-channel column by its AWQ scale → re-packs as GF4 → folds 1/s_c into that layer's post_attention_norm. Every non-FFN tensor is streamed through untouched, one tensor at a time, so peak RAM is a single tensor. It is CPU-heavy and takes a few minutes. Requires python3 + numpy + gguf.

Licensing

The model weights are Qwowl3.5-9B by Emilio Schininà, cooked from the Qwen Team's model and released under the Apache-2.0 license. qw35 is an independent inference engine for the model.

Citation

bibtex

@misc{qwen3.5,
    title  = {{Qwen3.5}: Towards Native Multimodal Agents},
    author = {{Qwen Team}},
    month  = {February},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.5}
}

EmilioSchi/Qwowl3.5-9B-GGUF

Cooking details

Usage

Cook by yourself (optional)

Licensing

Citation

EmilioSchi/
Qwowl3.5-9B-GGUF