Instructions to use Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL
# Run inference directly in the terminal:
llama cli -hf Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL
# Run inference directly in the terminal:
llama cli -hf Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

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 Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL
# Run inference directly in the terminal:
./llama-cli -hf Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

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 Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

Use Docker

docker model run hf.co/Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

LM Studio
Jan

vLLM

How to use Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Frosty40/Treebeard-Qwen3.6-35B-A3B-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": "Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

Ollama
How to use Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL
```
Unsloth Desktop

How to use Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL
```

Lemonade

How to use Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

Run and chat with the model

lemonade run user.Treebeard-Qwen3.6-35B-A3B-GGUF-UD-Q5_K_XL

List all available models

lemonade list

Hermes Agent

How to use Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

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 Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF:UD-Q5_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Treebeard-Qwen3.6-35B-A3B-GGUF

File size: 2,977 Bytes

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# Benchmarks

Model package: <https://huggingface.co/Frosty40/Treebeard-Qwen3.6-35B-A3B-GGUF>

GitHub: <https://github.com/newjordan/treebeard>

Site: <https://newjordan.github.io/treebeard/>

Public result index: <https://github.com/newjordan/treebeard/tree/main/results>

## Agent Bench freeze (named run)

Score **94/100** (130/138 points):

- 69/69 scenarios completed;
- 63 pass, 4 partial, 2 fail;
- zero request errors;
- one server slot and one benchmark worker;
- 262,144 total context tokens;
- temperature 0, thinking disabled, seed 42;
- tool-eval-bench 2.1.0 at `8b3259b`;
- llama.cpp build `b9624-0424f677f` (freeze pin).

Same score and outcome vector on Intel Arc Pro B70 and NVIDIA GB10.

Packaged evidence:

- `evidence/agent/single-slot-94/result.json`
- `evidence/agent/single-slot-94/index.html`
- `evidence/agent/single-slot-94/guard.log`
- `evidence/agent/single-slot-94/nvidia-result.json`
- `evidence/nvidia/agent-single-slot-94/result.json`

## Control A/B (2026-07-28, stock Q5, Arc Pro B70)

Same stock Q5 weights, np=1, c=262144, temp 0, seed 42, tool-eval-bench 2.1.0
@ `8b3259b`. Clean upstream SYCL versus Treebeard package binary + package env.

| Arm | Public 69 | Held-out ho-pack-v1.1 |
| --- | ---: | ---: |
| Original Qwen control | 91/100 (125/138) | 42/46 (91.3%) |
| Treebeard package | 91/100 (126/138) | 42/46 (91.3%) |

Sequential tg_p50 (np=1, c=32768, 5 prompts × 2): control 77.1 → Treebeard 88.9.
Multi-slot p50/agent (np=12, n_predict=96): control 6.88 → Treebeard 26.33
(concurrent capacity).

Evidence (GitHub, not duplicated as GGUF LFS here):

- https://github.com/newjordan/treebeard/tree/main/results/private-verification-20260728/agent-bench-ab-20260728T220230Z
- https://github.com/newjordan/treebeard/blob/main/docs/RELEASE-20260728.md

## Portable CPU package smoke

Independent install on AMD Ryzen 9 5950X:

- build identity: `b9624-6a6dc2def-cpu`;
- context: 4,096 tokens for the bounded smoke;
- chat output: exact `TREEBEARD READY`;
- chat generation: 9.302 tok/s;
- tool call: exact `multiply({"a":17,"b":23})`;
- tool-call generation: 7.400 tok/s;
- loader warnings, request errors, and assertion failures: zero.

Functional fallback only. Raw files under `evidence/cpu-linux-x86_64`.

## SYCL serving

Released 12-slot profile: 194.023 aggregate tok/s. 8-slot: 182.005 aggregate
tok/s. Single-session aggregate flat at -0.121% vs the prior RC2 reference in
the package notes. Supporting evidence under `evidence/sycl`.

## NVIDIA

GB10, compute capability 12.1, CUDA 13.3, Linux ARM64:

- CUDA correctness: 1,104/1,104 `MUL_MAT` and 796/796 `MUL_MAT_ID`;
- Q8_0 direct 12-column latency: 5.22 to 3.97 us median (31.49% lower);
- Q8_0 MoE down latency: 463.06 to 445.20 us median (4.01% lower);
- native pp4096: 2,422.325 tok/s over five samples;
- native tg128: 59.614 tok/s over five samples;
- single-slot agent freeze: 94/100, 130/138, zero request errors.

See `docs/NVIDIA.md` and `evidence/nvidia`.