Instructions to use Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-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 Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-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 Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-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 Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-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 Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-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 Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-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": "Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M

Ollama
How to use Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF with Ollama:
```
ollama run hf.co/Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M

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": "Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF with Docker Model Runner:
```
docker model run hf.co/Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M
```

Lemonade

How to use Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-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 Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-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 Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M

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 "Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF:Q4_K_M" \
  --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"

Qwen3.6 35B A3B Aggressive Q4_K_M + DSpark GGUF

A self-contained, tested pairing of the Q4_K_M target from HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive and the Qwen3.6 DSpark GGUF draft converted by williamliao/Qwen3.6-35B-A3B-DSPARK-GGUF.

The weights are unchanged from those upstream releases. This repository adds a reproducible llama.cpp launch profile and measured RTX 5090 results at a 200,704-token configured context window.

DSpark support is experimental. These results use llama.cpp PR #26275, commit f806441edb5006fdacb07df42445f337476dd169.

Files

File	Purpose	Size
`Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf`	verifier/target model	21.17 GB
`Qwen3.6-35B-A3B-DSPARK.gguf`	DSpark draft model; not standalone	1.04 GB
`launch-dspark.sh`	tested 200K launch profile	—
`prime-agent-models.json`	Prime Agent custom-provider template	—
`benchmark-results.json`	machine-readable measurements	—

RTX 5090 generation benchmark

Measured locally on 2026-08-07. Each result is an end-to-end wall-clock OpenAI Chat Completions request generating 512 tokens. Values are three independent runs after loading the full 200,704-token context configuration.

Configuration	Runs (tok/s)	Mean	Best	VRAM	Relative to baseline
No draft	270.79, 276.17, 279.65	275.54 tok/s	279.65	23,266 MiB	1.000x
DSpark, max draft 3	306.16, 312.70, 318.27	312.38 tok/s	318.27	27,944 MiB	1.134x
DSpark, max draft 5	233.74, 256.70, 260.33	250.26 tok/s	260.33	28,070 MiB	0.908x
DSpark, max draft 7	207.63, 224.25, 226.90	219.59 tok/s	226.90	28,196 MiB	0.797x

--spec-draft-n-max 3 is the clear choice on this target. In the 3 × 512-token coding run, 337 of 520 drafted tokens were accepted (64.81%). A separate short generation accepted 41 of 45 drafted tokens (91.11%). Acceptance depends on the prompt and output distribution.

Test machine

Component	Value
GPU	NVIDIA GeForce RTX 5090, 32,607 MiB
Driver / power limit	595.84 / 575 W
OS / kernel	Ubuntu 26.04 LTS / Linux 7.0.0-28-generic x86_64
Target quantization	Q4_K_M, 21.17 GB
Draft	BF16 GGUF, 1.04 GB
Configured context	200,704 tokens
KV cache	Q8_0 K and V for both target and draft
Batch / microbatch	2,048 / 512
Parallel slots	1
Flash attention	enabled

These are single-stream decode measurements, not prompt-processing throughput or multi-user aggregate throughput. The first request can include warm-up overhead, which is intentionally retained in the mean.

Quality benchmarks

DSpark is target-verifying speculative decoding: accepted draft tokens are verified by the target, so it accelerates inference without substituting the draft model's predictions for the target's decisions.

The upstream Aggressive model card does not publish LiveCodeBench v6 or SWE-bench Verified scores, and those suites were not rerun for this packaging work. No quality score is claimed here. Refer to the HauhauCS model card for the target's reported properties and safety behavior.

Run

Build the experimental branch with CUDA enabled:

git clone --branch dspark-speculators https://github.com/wjinxu/llama.cpp.git
cmake -S llama.cpp -B llama.cpp/build \
  -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_CUDA_ARCHITECTURES=120a
cmake --build llama.cpp/build --config Release -j --target llama-server

Then, from this repository directory:

LLAMA_SERVER=/path/to/llama.cpp/build/bin/llama-server ./launch-dspark.sh

OpenAI-compatible API:

curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "qwen3.6-35b-a3b-aggressive-dspark",
    "messages": [{"role": "user", "content": "Write a Python LRU cache."}],
    "temperature": 0,
    "max_tokens": 512
  }'

For Prime Agent, copy prime-agent-models.json to ~/.prime/agent/models.json, change YOUR_SERVER_IP, then select provider qwen36-dspark and model qwen3.6-35b-a3b-aggressive-dspark.

Attribution and limitations

Target and quantization: HauhauCS
Base model: Qwen/Qwen3.6-35B-A3B
Draft conversion: williamliao/Qwen3.6-35B-A3B-DSPARK-GGUF
Draft lineage: Koopah/Qwen3.6-35B-A3B-NVFP4-DSPARK
DSpark research/runtime: DeepSpec and llama.cpp PR #26275

This target is explicitly uncensored. Review the upstream model card and use it responsibly. This bundle is text-only because it does not include the optional vision projector. See THIRD_PARTY_NOTICES.md and LICENSE.

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GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

View +1 variant

Model tree for Danny-Dasilva/Qwen3.6-35B-A3B-Aggressive-Q4_K_M-DSPARK-GGUF

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