Instructions to use 0xSero/Qwen3.6-35B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/Qwen3.6-35B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="0xSero/Qwen3.6-35B-GGUF",
	filename="Qwen3.6-35B-A3B-DYNAMIC.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use 0xSero/Qwen3.6-35B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 0xSero/Qwen3.6-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 0xSero/Qwen3.6-35B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 0xSero/Qwen3.6-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 0xSero/Qwen3.6-35B-GGUF:Q4_K_M

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 0xSero/Qwen3.6-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 0xSero/Qwen3.6-35B-GGUF:Q4_K_M

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 0xSero/Qwen3.6-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 0xSero/Qwen3.6-35B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/0xSero/Qwen3.6-35B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use 0xSero/Qwen3.6-35B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/Qwen3.6-35B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xSero/Qwen3.6-35B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/Qwen3.6-35B-GGUF:Q4_K_M

Ollama
How to use 0xSero/Qwen3.6-35B-GGUF with Ollama:
```
ollama run hf.co/0xSero/Qwen3.6-35B-GGUF:Q4_K_M
```

Unsloth Studio

How to use 0xSero/Qwen3.6-35B-GGUF 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 0xSero/Qwen3.6-35B-GGUF 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 0xSero/Qwen3.6-35B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 0xSero/Qwen3.6-35B-GGUF to start chatting

How to use 0xSero/Qwen3.6-35B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf 0xSero/Qwen3.6-35B-GGUF:Q4_K_M

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": "0xSero/Qwen3.6-35B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use 0xSero/Qwen3.6-35B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf 0xSero/Qwen3.6-35B-GGUF:Q4_K_M

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 0xSero/Qwen3.6-35B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use 0xSero/Qwen3.6-35B-GGUF with Docker Model Runner:
```
docker model run hf.co/0xSero/Qwen3.6-35B-GGUF:Q4_K_M
```

Lemonade

How to use 0xSero/Qwen3.6-35B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 0xSero/Qwen3.6-35B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-GGUF-Q4_K_M

List all available models

lemonade list

Support this work → · X · GitHub · REAP paper · Cerebras REAP

Qwen3.6-35B-GGUF

GGUF quantization of Qwen/Qwen3.6-35B-A3B.

At a glance


Base model	Qwen/Qwen3.6-35B-A3B
Format	GGUF
Total params	35B
Active / token	3B
Experts / layer	—
Layers	—
Hidden size	—
Context	—
On-disk size	171 GB

Which variant should I pick?

Variant	Format	Link
`Qwen3.6-28B`	BF16	link
`Qwen3.6-28B-GGUF`	GGUF	link
`Qwen3.6-35B-GGUF` (this)	GGUF	link

Dynamic mixed-precision GGUF quantizations of Qwen/Qwen3.6-35B-A3B, produced and benchmarked on a Framework Desktop with AMD Ryzen AI MAX+ 395 (Radeon 8060S, gfx1151, 128 GB UMA) running Vulkan via llama.cpp.

Variants

File	Size	prefill (t/s)	decode (t/s)	Notes
`Qwen3.6-35B-A3B-Q8_0.gguf`	35 GB	975	52.7	near-lossless reference
`Qwen3.6-35B-A3B-Q6_K.gguf`	27 GB	830	62.2
`Qwen3.6-35B-A3B-Q5_K_M.gguf`	24 GB	943	64.1
`Qwen3.6-35B-A3B-Q4_K_M.gguf`	20 GB	1021	70.2	production sweet spot
`Qwen3.6-35B-A3B-Q4_0.gguf`	19 GB	1061	76.5	fastest decode
`Qwen3.6-35B-A3B-IQ4_NL.gguf`	19 GB	891	73.1
`Qwen3.6-35B-A3B-DYNAMIC.gguf`	19 GB	1100	64.0	fastest prefill; mixed per-tensor quant

All numbers: pp=4096 tokens, tg=128 tokens; -fa 1 -ctk q8_0 -ctv q8_0 -ub 2048 -b 2048 on a single Vulkan gfx1151 device.

Dynamic mix recipe

DYNAMIC.gguf uses a per-tensor quantization map chosen for the hybrid Gated DeltaNet + Gated Attention architecture:

attn_k / attn_q / attn_v → Q8_0 (retrieval-critical)
attn_output → Q5_K
ffn_gate_inp (router) → Q8_0 (routing-critical)
ffn_gate_exps / ffn_up_exps / ffn_down_exps (256 routed experts) → IQ4_NL
ffn_gate_shexp / ffn_up_shexp / ffn_down_shexp (shared expert) → Q6_K
token_embd / output → Q8_0
everything else → Q4_K_M (fallback)

Usage

llama-bench -m Qwen3.6-35B-A3B-DYNAMIC.gguf -ngl 99 -fa 1 -ctk q8_0 -ctv q8_0 \
  -ub 2048 -b 2048 -p 4096 -n 128

Benchmark context

Research series on pushing Qwen3.5/3.6 on AMD Strix Halo. Methodology, scripts, and live results: see the benchmark site referenced from the GitHub repo.

License

Apache 2.0 (inherited from base model).

License & citation

License inherited from the base model.

@misc{lasby2025reap,
  title  = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
  author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
  year   = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}

Model tree for 0xSero/Qwen3.6-35B-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(465)

this model

Collection including 0xSero/Qwen3.6-35B-GGUF

Qwen — REAP

Collection

REAP-pruned & quantized Qwen3.5 / 3.6 / Coder variants. • 15 items • Updated 15 days ago

Paper for 0xSero/Qwen3.6-35B-GGUF

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 20

0xSero
/

Qwen3.6-35B-GGUF