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")

# pip install -U transformers accelerate
# 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"

pragmaticcs commited on Sep 3

Commit

7815aa8

1 Parent(s): fb602dd

Update README.md

Browse files

Files changed (1) hide show

README.md +14 -3

README.md CHANGED Viewed

@@ -12,9 +12,12 @@ tags:
   - dare
   - moe
   - qwen
   - causal-lm
   - deltanet
   - agentic
   - code
 license: apache-2.0
 language:
@@ -36,10 +39,10 @@ model_type: qwen3_5_moe
 </div>
-A four-way merge of the Qwen 35B-A3B Mixture-of-Experts architecture, integrating task deltas from three fine-tunes into a base anchor via DARE-TIES with sinusoidal depth modulation.
 > [!IMPORTANT]
-> Merged 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.
 ---
@@ -49,6 +52,7 @@ A four-way merge of the Qwen 35B-A3B Mixture-of-Experts architecture, integratin
 - [Composition](#composition)
 - [Merge Methodology](#merge-methodology)
 - [Layer-Stratified Policies](#layer-stratified-policies)
 - [Generation Parameters](#recommended-generation-parameters)
 - [How to Use](#how-to-use)
 - [Lineage](#lineage)
@@ -127,6 +131,12 @@ This keeps input/output projections closer to the base and applies the strongest
 ---
 ## Recommended Generation Parameters
 For code generation and agentic task trajectories, avoid high temperatures to maintain routing stability and syntax validity.
@@ -199,6 +209,7 @@ Qwen/Qwen3.6-35B-A3B
 - [ornith-ai/Ornith-1.5-35B-A3B](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B)
 - [Kwaipilot/KAT-Coder-V2.5-Dev](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev)
 - [Qwen/Qwen-AgentWorld-35B-A3B](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B)
 ```bibtex
 @inproceedings{yu2024dare,
@@ -214,4 +225,4 @@ Qwen/Qwen3.6-35B-A3B
   booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
   year={2023}
 }
-```

   - dare
   - moe
   - qwen
+  - qwen3.5
+  - qwen3.6
   - causal-lm
   - deltanet
   - agentic
+  - reasoning
   - code
 license: apache-2.0
 language:
 </div>
+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.
 > [!IMPORTANT]
+> 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.
 ---
 - [Composition](#composition)
 - [Merge Methodology](#merge-methodology)
 - [Layer-Stratified Policies](#layer-stratified-policies)
+- [Chat Template](#chat-template)
 - [Generation Parameters](#recommended-generation-parameters)
 - [How to Use](#how-to-use)
 - [Lineage](#lineage)
 ---
+## Chat Template
+This model uses the [Improved Chat Template for Qwen 3.x by Olivia Rossi](https://huggingface.co/OliviaRossi/Improved-Chat-Template-for-Qwen-3.x) to support multi-tier Chain-of-Thought (CoT) reasoning, dual-format agentic tool execution, automatic error-recovery heuristics, and strict token-waste elimination.
+---
 ## Recommended Generation Parameters
 For code generation and agentic task trajectories, avoid high temperatures to maintain routing stability and syntax validity.
 - [ornith-ai/Ornith-1.5-35B-A3B](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B)
 - [Kwaipilot/KAT-Coder-V2.5-Dev](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev)
 - [Qwen/Qwen-AgentWorld-35B-A3B](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B)
+- [Improved Chat Template for Qwen 3.x](https://huggingface.co/OliviaRossi/Improved-Chat-Template-for-Qwen-3.x)
 ```bibtex
 @inproceedings{yu2024dare,
   booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
   year={2023}
 }
+```