This repository provides GGUF-quantized tensors for the Qwen3.5-4B model (official repo: https://huggingface.co/Qwen/Qwen3.5-4B). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With this GGUF Tool Suite, you can produce your own Dynamic 3.0 Quants recipes and achieve optimum accuracy & SOTA quantization performance.
tl;dr: Expand the details section below
cd ~
# Make sure to install all ik_llama.cpp compilation dependencies...
apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx
# Obtain ik_llama's Thireus version - Windows/macOS/Linux builds available at https://github.com/Thireus/ik_llama.cpp/releases
git clone https://github.com/Thireus/ik_llama.cpp
cd ik_llama.cpp
git pull
# Build ik_llama.cpp
cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048
cmake --build build --config Release -j16
cd ..
# Obtain Thireus' GGUF-Tool-Suite
GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/Thireus/GGUF-Tool-Suite
# Download model quant mix from recipe file - you can also try the web version: https://gguf.thireus.com/quant_downloader.html
cd GGUF-Tool-Suite
rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py
cp -f models/Qwen3.5-4B/download.conf . # Use the download.conf of the chosen model
mkdir -p kitchen && cd kitchen
# Obtain a recipe example for the chosen model from ../recipe_examples/
../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/Qwen3.5-4B.ROOT-3.5993bpw-11.3565ppl.1GB-GGUF_0GB-GPU_0GB-CPU.9888e4b_831ff04.recipe
# Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples
# Launch ik_llama's llama-cli:
ulimit -n 9999 # Lifts "too many open files" limitation on Linux
~/ik_llama.cpp/build/bin/llama-server \
-m Qwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00001-of-00399.gguf \
-fa auto -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \
-b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \
--main-gpu 0
ik_llama.cpp. Here’s how Qwen3.5-4B quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw):
Note: The
recipe_examplesfiles illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, andquant_assign.pyfinds the best mix.
More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs
All PPL benchmarks are computed with the parameters -ctk f16 -c 512 -b 4096 -ub 4096. Changing any of these parameters will alter the PPL. In particular, reducing -b 4096 -ub 4096 increases the PPL, while increasing them decreases the PPL.
Check out the GGUF Tool Suite README — focus on these sections:
ik_llama.cpp (or llama.cpp) version to use and how to compile. quant_downloader.sh or quant_downloader.html to fetch GGUF shards from any recipe. llama-cli. Supported models are listed under models/ in the Tool Suite Github repo. Presence of ppl_results.csv indicates official support and compatibility with quant_assign.py.
No, because I believe in tailored quantization for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via llama-gguf-split --merge, or request someone to publish them, or rely on generic GGUF dynamic quants such as unsloth's.
Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The quant_downloader.sh script or quant_downloader.html (web port of this script) handles automatic fetching and verification of each shard. Note that recipes provided by Ubergarm on his model cards are also compatible with quant_downloader.sh and quant_downloader.html, providing a "SPECIAL_SPLIT" version of these models exists (see https://gguf.thireus.com/).
Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to llama-quantize --custom-q (see example). Run llama-quantize --help to list compatible quants for quant_assign.py. This approach is especially useful if you prefer llama.cpp over ik_llama.cpp.
tensors.map for names, quant types, sizes, SHA-256 hash, shard IDs, etc. tensors.map and header shard are signed with the key in trusted-keys.asc for tamper detection. You can easily download the BF16 model version to quantize your own shards:
mkdir kitchen
echo '.*=bf16' > kitchen/bf16.recipe
cd kitchen
../quant_downloader.sh bf16.recipe --qtype BF16
You can also quantize individual BF16 tensors without the need to download every BF16 .gguf shard:
BF16 model shards can also be individually quantized using a special version of ik_llama.cpp's llama-quantize utility which comes with the --individual-tensors option.
Usage example:
./llama-quantize --keep-split --imatrix imatrix_ubergarm.dat --individual-tensors 2,3,1094 Kimi-K2-Thinking-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01097.gguf my_new_shards.gguf iq3_s 12
For more information about how to use it: https://github.com/Thireus/GGUF-Tool-Suite/issues/45
You can produce your own quantized shards from Thireus' special BF16 model using quantize_model.sh found on https://github.com/Thireus/GGUF-Tool-Suite, for example:
./quantize_model.sh --model "Qwen3.5-122B-A10B" --qtype iq2_xxs
You can disable reasoning (thinking) when using jinja templates for supported models:
llama-server ... --jinja --chat-template-kwargs '{"enable_thinking": false}'
Enjoy optimized quantization! 🎉
Qwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00002-of-00427.gguf 1.3 GBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00003-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00004-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00005-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00006-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00007-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00008-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00009-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00010-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00011-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00012-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00013-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00014-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00015-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00016-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00017-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00018-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00019-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00020-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00021-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00022-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00023-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00024-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00025-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00026-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00027-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00028-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00029-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00030-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00031-of-00427.gguf 47 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MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00077-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00078-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00079-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00080-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00081-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00082-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00083-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00084-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00085-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00096-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00097-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00098-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00296-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00297-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00300-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00301-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00302-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00320-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00321-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00322-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00334-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00335-of-00427.gguf 47 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00092-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00106-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00117-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00128-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00136-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00147-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00158-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00169-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00177-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00188-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00199-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00210-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00218-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00229-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00240-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00251-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00262-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00270-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00281-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00292-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00308-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00316-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00330-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00343-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00351-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00362-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00370-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00381-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00392-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00403-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00411-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00422-of-00427.gguf 42 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00093-of-00427.gguf 21 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00095-of-00427.gguf 21 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00107-of-00427.gguf 21 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00109-of-00427.gguf 21 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00118-of-00427.gguf 21 MBQwen3.5-4B-THIREUS-BF16-SPECIAL_TENSOR-00120-of-00427.gguf 21 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