Instructions to use Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT",
	filename="mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_TENSOR-00001-of-00024.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R
# Run inference directly in the terminal:
llama-cli -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R
# Run inference directly in the terminal:
llama-cli -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R
# Run inference directly in the terminal:
./llama-cli -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R

Use Docker

docker model run hf.co/Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R

LM Studio
Jan
Ollama
How to use Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT with Ollama:
```
ollama run hf.co/Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R
```

Unsloth Studio

How to use Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT to start chatting

How to use Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT with Docker Model Runner:
```
docker model run hf.co/Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R
```

Lemonade

How to use Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT:IQ3_S_R

Run and chat with the model

lemonade run user.mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT-IQ3_S_R

List all available models

lemonade list

mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT / README.md

Thireus

mtp-Qwen3.6-35B-A3B-THIREUS-IQ3_S_R4-SPECIAL_SPLIT

a293d07 25 days ago

preview code

Raw

History Blame Contribute Delete

9.73 kB

	---
	license: mit
	---
	# mtp-Qwen3.6-35B-A3B

	## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/mtp-Qwen3.6-35B-A3B-THIREUS-BF16-SPECIAL_SPLIT/) about?

	This repository provides GGUF-quantized tensors for the [mtp](https://github.com/ggml-org/llama.cpp/blob/master/docs/speculative.md) layer(s) of the Qwen3.6-35B-A3B model (official repo: https://huggingface.co/Qwen/Qwen3.6-35B-A3B). These GGUF shards are designed to be used with Thireus’ GGUF Tool Suite (https://github.com/Thireus/GGUF-Tool-Suite), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given a model size target. With this GGUF Tool Suite, you can produce your own Dynamic 3.0 Quants recipes and achieve optimum accuracy & SOTA quantization performance. Give it a try here: https://gguf.thireus.com/quant_assign.html

	- 📖 Documentation: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/docs
	- 🔍 Example of GGUF recipes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples
	- 🍳 Cook your own recipe files: https://gguf.thireus.com/quant_assign.html
	- ☁️ Download GGUF models from recipe files: https://gguf.thireus.com/quant_downloader.html
	- 📂 Browse available models: https://huggingface.co/Thireus/collections and https://gguf.thireus.com

	tl;dr: Expand the details section below
	<details>

	```
	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/Qwen3.6-35B-A3B/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/Qwen3.6-35B-A3B.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 Qwen3.6-35B-A3B-THIREUS-BF16-SPECIAL_TENSOR-00001-of-*.gguf \
	-md mtp-Qwen3.6-35B-A3B-THIREUS-BF16-SPECIAL_TENSOR-00001-of-*.gguf --spec-type draft-mtp \
	-fa auto -amb 1024 -ctk q8_0 -c 32768 -ngl 99 \
	-b 4096 -ub 4096 --warmup-batch --no-mmap --threads 1 \
	--main-gpu 0
	```

	</details>

	---

	## ❓ Why does this Tool Suite exist?

	1. Compatibility & Speed – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`.
	2. Custom Rig Fit – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity.
	3. Automated PPL-Optimal Quantization – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results!

	---

	## 📊 How does it compare to other GGUFs?

	Here’s how Qwen3.6-35B-A3B quantized with Thireus’ GGUF Tool Suite stacks up against other quantizers (lower perplexity = better at equal or lower bpw):

	![PPLs Compared With Others](https://github.com/Thireus/GGUF-Tool-Suite/raw/main/ppl_graphs/Qwen3.6-35B-A3B.svg)

	> _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._

	More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs

	Qwen3.6 Thireus' PPL benchmarks are computed with the parameters `-ctk f16 -c 512 -b 512 -ub 512`. Changing any of these parameters will alter the PPL. In particular, reducing `-b 512 -ub 512` increases the PPL, while increasing them decreases the PPL.

	---

	## 🚀 How do I get started?

	Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections:

	1. ⚠️ Requirements – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile.
	- Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases
	2. 📥 Download Model Shards – Use `quant_downloader.sh` or [quant_downloader.html](https://gguf.thireus.com/quant_downloader.html) to fetch GGUF shards from any recipe.
	- Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples
	3. 🧠 Run a Downloaded Model – Sample usage with `llama-cli`.
	4. 🛠️ Generate a Custom Recipe – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity.

	---

	## ✅ Supported Models

	Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`.

	---

	## 🤷‍♂️ Will I release baked dynamic quant GGUFs?

	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](https://huggingface.co/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](https://gguf.thireus.com/quant_downloader.html) (web port of this script) handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh` and [quant_downloader.html](https://gguf.thireus.com/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](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). 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`.

	---

	## 📦 What’s in this repository?

	- 00001 GGUF header shard – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard.
	- Tensor shards – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc.
	- GPG-signed files – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection.
	- Security note – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits.

	---

	## 💡 Pro Tips

	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.

	- Source code: https://github.com/Thireus/ik_llama.cpp/tree/th/quantize_individual_tensors
	- Builds (macOS, Windows and Linux): https://github.com/Thireus/ik_llama.cpp/releases/tag/th-quantize_individual_tensors-b4210-7a44805

	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 "Qwen3.6-122B-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! 🎉