Instructions to use jstarks/Qwen3.5-4B-Weight-Space-Programming with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jstarks/Qwen3.5-4B-Weight-Space-Programming with Transformers:

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

pipe = pipeline("text-generation", model="jstarks/Qwen3.5-4B-Weight-Space-Programming")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("jstarks/Qwen3.5-4B-Weight-Space-Programming")
model = AutoModelForMultimodalLM.from_pretrained("jstarks/Qwen3.5-4B-Weight-Space-Programming", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jstarks/Qwen3.5-4B-Weight-Space-Programming 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 jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M

Use Docker

docker model run hf.co/jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use jstarks/Qwen3.5-4B-Weight-Space-Programming with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jstarks/Qwen3.5-4B-Weight-Space-Programming"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jstarks/Qwen3.5-4B-Weight-Space-Programming",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M

SGLang

How to use jstarks/Qwen3.5-4B-Weight-Space-Programming 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 "jstarks/Qwen3.5-4B-Weight-Space-Programming" \
    --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": "jstarks/Qwen3.5-4B-Weight-Space-Programming",
		"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 "jstarks/Qwen3.5-4B-Weight-Space-Programming" \
        --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": "jstarks/Qwen3.5-4B-Weight-Space-Programming",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use jstarks/Qwen3.5-4B-Weight-Space-Programming with Ollama:
```
ollama run hf.co/jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M
```
Unsloth Desktop

How to use jstarks/Qwen3.5-4B-Weight-Space-Programming with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jstarks/Qwen3.5-4B-Weight-Space-Programming: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": "jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jstarks/Qwen3.5-4B-Weight-Space-Programming with Docker Model Runner:
```
docker model run hf.co/jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M
```

Lemonade

How to use jstarks/Qwen3.5-4B-Weight-Space-Programming with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-Weight-Space-Programming-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use jstarks/Qwen3.5-4B-Weight-Space-Programming with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jstarks/Qwen3.5-4B-Weight-Space-Programming: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 jstarks/Qwen3.5-4B-Weight-Space-Programming:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jstarks/Qwen3.5-4B-Weight-Space-Programming with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jstarks/Qwen3.5-4B-Weight-Space-Programming: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 "jstarks/Qwen3.5-4B-Weight-Space-Programming: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"

Qwen3.5-4B-Weight-Space-Programming / scripts /dynamic_router.py

Commit History

Nov 13 2 days ago

16:38
Upload Q4_K_M GGUF, full BF16 HF checkpoint, safetensors patches (r16 + dense delta), and apply_patch.py

jstarks

Verified signature
605f888
16:28
Upload Qwen3.5-4B WSP v2.5 6-subspace GGUF LoRAs, scripts, and 4-variant verification (Variant 3 most improved: 82.9%)

jstarks

Verified signature
711a19d

Commit History

Nov 13 2 days ago

Upload Q4_K_M GGUF, full BF16 HF checkpoint, safetensors patches (r16 + dense delta), and apply_patch.py

Upload Qwen3.5-4B WSP v2.5 6-subspace GGUF LoRAs, scripts, and 4-variant verification (Variant 3 most improved: 82.9%)