Instructions to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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 pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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 pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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 pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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 pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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": "pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M

SGLang

How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF with Ollama:
```
ollama run hf.co/pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M
```
Unsloth Desktop

How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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": "pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M
```

Lemonade

How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-35B-A3B-SignOfFour-Coder-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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 pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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 pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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 "pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder-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"

A four-way MoE merge of the Qwen 35B-A3B architecture, fusing task vectors from three specialized fine-tunes into a base anchor via DARE-TIES with sinusoidal depth modulation.

Designed specifically to consolidate software engineering, code synthesis, and agentic tool execution capabilities. Multimodal vision weights and Multi-Token Prediction (MTP) heads were stripped to reduce VRAM footprint and maximize throughput during coding tasks.

Architectural Specifications
Composition
Merge Methodology
Layer-Stratified Policies
Chat Template
Available quants
Generation Parameters
Lineage
References

Architectural Specifications

Spec	Value
Total parameters	35B
Active parameters / token	3B
Decoder layers	40
Routed experts	256
Shared experts	1
Attention	Gated DeltaNet hybrid linear attention
Merge algorithm	DARE-TIES + sine depth scaling

Composition

Jackrong/Qwopus3.6-35B-A3B-Coder serves as the base anchor (W₀); the remaining three models contribute task vectors at the listed weights.

Model	Role	Task Weight (α)
Jackrong/Qwopus3.6-35B-A3B-Coder	Base anchor (W₀)	1.00
ornith-ai/Ornith-1.5-35B-A3B	Donor (D₁)	0.30
Kwaipilot/KAT-Coder-V2.5-Dev	Donor (D₂)	0.25
Qwen/Qwen-AgentWorld-35B-A3B	Donor (D₃)	0.20

Merge Methodology

For each floating-point parameter, a task delta is computed per donor $k$ :

$\Delta_k = D_k - W_0$

DARE pruning. A Bernoulli mask at retention density $p$ zeroes out low-magnitude updates; surviving values are rescaled by $p^{- 1}$ :

$\tilde{\Delta}_k = \frac{1}{p} \left(\Delta_k \odot M_k\right), \quad M_k \sim \text{Bernoulli}(p)$

TIES sign election. A consensus sign $\Gamma$ is computed via weighted vote across donors, and any donor update conflicting with it is dropped before averaging:

$\Gamma = \operatorname{sgn}\left(\sum_{k=1}^K \alpha_k \tilde{\Delta}_k\right)$

$\Delta_{\text{TIES}} = \frac{\sum_{k=1}^K \alpha_k \tilde{\Delta}_k \odot \mathbb{I}\left(\operatorname{sgn}(\tilde{\Delta}_k) = \Gamma\right)}{\sum_{k=1}^K \alpha_k \cdot \mathbb{I}\left(\operatorname{sgn}(\tilde{\Delta}_k) = \Gamma\right) + \epsilon}$

Depth-scaled reconstruction. The merged weight is reconstructed as:

$W_{\text{final}} = W_0 + \lambda(l) \cdot \Delta_{\text{TIES}}$

where the layer scaling factor $\lambda(l)$ across decoder layer index $l \in [0, 39]$ is defined as:

$\lambda(l) = \beta \cdot \left(0.5 + 0.5 \sin\left(\pi \frac{l}{39}\right)\right)$

This keeps input/output projections closer to the base and applies the strongest task transfer to middle layers $(l \in [12, 28])$ .

Layer-Stratified Policies

Parameter Group	Match Substring	Policy	Density (p)	Base Scale (β)
Embeddings / LM head	`embed_tokens`, `lm_head`	Linear	—	1.00
Norms / biases	`norm`, `bias`, 1D tensors	Linear	—	1.00
DeltaNet recurrent state	`a_log`, `dt_bias`, `conv1d`	Linear	—	1.00
MoE router gate	`mlp.gate.weight`, `block_sparse_moe.gate`	Linear	—	1.00
MoE shared expert	`shared_expert`	DARE‑TIES	0.70	0.60
Attention projections	`attn`, `rotary`, `in_proj`, `out_proj`, `x_proj`	DARE‑TIES	0.75	0.60
Routed experts (×256)	`experts`, `mlp`	DARE‑TIES	0.65	0.55

Router protection: Gate weights use linear interpolation (~57% base, ~43% donors) rather than DARE to avoid destabilizing expert routing.
DeltaNet stability: Recurrent state kernels are excluded from DARE to prevent divergence in the linear-attention state space.
MTP removed: Multi-token-prediction heads beyond the 40 primary decoder blocks were stripped for standard CausalLM inference.

Chat Template

This model uses the Improved Chat Template for Qwen 3.x by Olivia Rossi to support multi-tier Chain-of-Thought (CoT) reasoning, dual-format agentic tool execution, automatic error-recovery heuristics, and strict token-waste elimination.

Available quants

Quantization	File	Size
Q4_K_M	`Qwen-35B-A3B-SignOfFour-Coder-Q4_K_M.gguf`	21.5 GB
APEX-High-Quality	`Qwen-35B-A3B-SignOfFour-Coder-APEX-High-Quality.gguf`	25.1 GB
Q5_K_M	`Qwen-35B-A3B-SignOfFour-Coder-Q5_K_M.gguf`	25.0 GB
Q6_K	`Qwen-35B-A3B-SignOfFour-Coder-Q6_K.gguf`	28.7 GB
Q8_0	`Qwen-35B-A3B-SignOfFour-Coder-Q8_0.gguf`	36.9 GB
BF16 (Split)	`Qwen-35B-A3B-SignOfFour-Coder-BF16-*.gguf`	69.4 GB

Need fast GPU inference? Running a 35B MoE with CPU offloading or on under-provisioned consumer RAM can result in low token throughput. If you lack a local 24 GB+ GPU and want faster inference, you can spin up an hourly instance on RunPod in one click to run llama-server with 100% layer offload. New accounts created through the link below receive bonus compute credits ($5–$500 on your first $10 top-up).

Affiliate Disclosure: If you register an account and load funds using this link, I receive a small commission and platform credits from RunPod at no extra cost to you. These earnings are directly reinvested into renting GPU compute for ongoing open-source model merging, testing, and evaluation. Thank you for your support!

Recommended Generation Parameters

For code generation and agentic task trajectories, avoid high temperatures to maintain routing stability and syntax validity.

Parameter	Coding / Terminal Agent	Creative Reasoning
Temperature	`0.6`	`1.0`
Top-P	`0.95`	`0.95`
Min-P	`0.0`	`0.01`
Repetition Penalty	`off`	`1.05`

Lineage

Qwen/Qwen3.6-35B-A3B
└── pragmaticcs/Qwen-35B-A3B-SignOfFour-Coder
    ├── base:  Jackrong/Qwopus3.6-35B-A3B-Coder
    ├── donor: ornith-ai/Ornith-1.5-35B-A3B
    ├── donor: Kwaipilot/KAT-Coder-V2.5-Dev
    └── donor: Qwen/Qwen-AgentWorld-35B-A3B

Citation & References

@inproceedings{yu2024dare,
  title={Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch},
  author={Yu, Le and Yu, Bowen and Yu, Haiyang and Huang, Fei and Li, Yongbin},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2024}
}

@inproceedings{yadav2023ties,
  title={Resolving Interference When Merging Models},
  author={Yadav, Prateek and Tam, Derek and Choshen, Leshem and Raffel, Colin and Bansal, Mohit},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2023}
}