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

Libraries

How to use GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF",
	filename="Qwen3.6-35B-A3B-NSC-ACE-SABER-F16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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 GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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 GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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 GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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": "GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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/GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M

Ollama
How to use GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF with Ollama:
```
ollama run hf.co/GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M
```

Unsloth Studio

How to use GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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 GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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 GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF to start chatting

How to use GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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": "GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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 GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-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 GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF with Docker Model Runner:
```
docker model run hf.co/GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M
```

Lemonade

How to use GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF-Q4_K_M

List all available models

lemonade list

Support this work on Ko-fi

Qwen3.6-35B-A3B-NSC-ACE-SABER GGUF

This repository hosts llama.cpp/GGUF builds for GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER. The source checkpoint is the full safetensors model in GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER.

The quantization suite is being built down to Q2_K. Higher quants preserve more of the SABER-selected distribution; lower quants are provided for smaller VRAM/RAM targets and should be evaluated against your own prompts.

Image/video sidecars: This repository now includes the restored Qwen3.6 multimodal config, processor/preprocessor files, tokenizer/chat template, safetensors index, and model-vision-from-qwen3.6-base.safetensors visual tower sidecar. The existing .gguf binaries were not rewritten in this metadata-copy pass.

Release Snapshot

Item	Value
Source checkpoint	`GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER`
Base model	`Qwen/Qwen3.6-35B-A3B`
Format	GGUF for llama.cpp-compatible runtimes
Quantization range	F16, Q8_0, Q6_K, Q5_K_M, Q5_K_S, Q4_K_M, Q4_K_S, Q3_K_L, Q3_K_M, Q3_K_S, Q2_K
Final source compliance	98.33% on HarmBench-300
Final source KLD	0.025383937664711

Benchmark Plots

35B SABER release gate

35B BFCL function calling

BFCL Tool-Calling Check

The source safetensors checkpoint was compared against Qwen/Qwen3.6-35B-A3B on a 40-case BFCL subset: 20 simple and 20 multiple-function prompts. GGUF files inherit from that checkpoint, but individual quants should be rechecked if exact tool-call behavior matters.

Metric	Base	NSC-ACE SABER source
Tool-call rate	92.50%	95.00%
Function name accuracy	92.50%	95.00%
Required argument name accuracy	90.00%	93.12%
Required argument value accuracy	79.79%	83.54%
Exact required-call accuracy	75.00%	77.50%

Available Files

File	Status	Notes
`Qwen3.6-35B-A3B-NSC-ACE-SABER-F16.gguf`	uploaded	Full GGUF conversion source / highest local fidelity
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q8_0.gguf`	uploaded	Near-full quality, large local file
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q6_K.gguf`	uploaded	High-quality local default if memory allows
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q5_K_M.gguf`	uploaded	Strong quality/size balance
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q5_K_S.gguf`	uploaded	Smaller Q5 option
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q4_K_M.gguf`	uploaded	Common balanced local target
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q4_K_S.gguf`	uploaded	Smaller Q4 option
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q3_K_L.gguf`	uploaded	Lower-memory Q3 option
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q3_K_M.gguf`	uploaded	Smaller Q3 balance
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q3_K_S.gguf`	uploaded	Small Q3 option
`Qwen3.6-35B-A3B-NSC-ACE-SABER-Q2_K.gguf`	uploaded	Minimum-size target; quality loss expected

The uploader refreshes this card as each artifact finishes. Uploaded non-F16 files are deleted from the build pod after upload to stay under the pod volume quota.

Which Quant Should I Use?

Quant	Best fit
F16	Maximum fidelity when disk/RAM are not a concern
Q8_0	Very high fidelity local inference
Q6_K	Recommended high-quality local starting point
Q5_K_M	Strong balance for quality and size
Q4_K_M	Practical default for constrained machines
Q3_K_M / Q3_K_S	Low-memory experiments
Q2_K	Smallest target; use only when memory is the hard constraint

For agentic/tool-calling workloads, prefer Q6_K, Q5_K_M, or Q4_K_M when possible. Very low quants can affect formatting, argument fidelity, and refusal calibration.

What NSC-ACE Is

NSC-ACE means Neural Steering Committee for Agentic Co-Evolution. The source checkpoint was trained by generating multiple steered rollouts from the same model and rewarding convergence in tool-call structure across those latent modes. The goal is stronger agentic/tool-use behavior: stable function selection, argument filling, useful reasoning wrappers, and fewer repeated tool loops.

SABER was applied after NSC-ACE as a calibration stage. The release objective was to raise HarmBench-300 compliance while keeping KLD and PPL movement low.

Running With llama.cpp

llama-cli \
  -m Qwen3.6-35B-A3B-NSC-ACE-SABER-Q5_K_M.gguf \
  -c 32768 \
  -ngl 999 \
  -p "Write a compact tool plan for indexing a Python repo."

For OpenAI-compatible local serving:

llama-server \
  -m Qwen3.6-35B-A3B-NSC-ACE-SABER-Q5_K_M.gguf \
  -c 32768 \
  -ngl 999 \
  --jinja

Use a current llama.cpp build. Qwen3.6 support, chat-template handling, and tool-call behavior depend on runtime freshness.

Quantization Notes

GGUFs are produced from the accepted full safetensors checkpoint.
The source model's final release metrics are measured before quantization.
Quantized files should be re-evaluated if exact compliance/KLD behavior matters.
Lower bitrate files can degrade structured output before they obviously degrade prose quality.

Related Repositories

Full safetensors checkpoint: GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER
Base model: Qwen/Qwen3.6-35B-A3B

Downloads last month: 910

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Collection including GestaltLabs/Qwen3.6-35B-A3B-NSC-ACE-SABER-GGUF

NSC-ACE-SABER Pipeline

Collection

Full pipeline: SFT -> NSC-ACE -> SABER -> GGUF. No naive abliteration. • 4 items • Updated May 22