Instructions to use FoolDev/Janus-35B-HERETIC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FoolDev/Janus-35B-HERETIC with Transformers:

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

pipe = pipeline("image-text-to-text", model="FoolDev/Janus-35B-HERETIC")
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)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FoolDev/Janus-35B-HERETIC", device_map="auto")

llama-cpp-python

How to use FoolDev/Janus-35B-HERETIC with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FoolDev/Janus-35B-HERETIC",
	filename="Janus-35B-A3B.Q4_K_M.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 FoolDev/Janus-35B-HERETIC 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 FoolDev/Janus-35B-HERETIC:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FoolDev/Janus-35B-HERETIC:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FoolDev/Janus-35B-HERETIC:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FoolDev/Janus-35B-HERETIC: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 FoolDev/Janus-35B-HERETIC:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FoolDev/Janus-35B-HERETIC: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 FoolDev/Janus-35B-HERETIC:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FoolDev/Janus-35B-HERETIC:Q4_K_M

Use Docker

docker model run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M

LM Studio
Jan

vLLM

How to use FoolDev/Janus-35B-HERETIC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FoolDev/Janus-35B-HERETIC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FoolDev/Janus-35B-HERETIC",
		"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/FoolDev/Janus-35B-HERETIC:Q4_K_M

SGLang

How to use FoolDev/Janus-35B-HERETIC 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 "FoolDev/Janus-35B-HERETIC" \
    --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": "FoolDev/Janus-35B-HERETIC",
		"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 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 "FoolDev/Janus-35B-HERETIC" \
        --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": "FoolDev/Janus-35B-HERETIC",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use FoolDev/Janus-35B-HERETIC with Ollama:
```
ollama run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
```

Unsloth Studio

How to use FoolDev/Janus-35B-HERETIC 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 FoolDev/Janus-35B-HERETIC 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 FoolDev/Janus-35B-HERETIC to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FoolDev/Janus-35B-HERETIC to start chatting

How to use FoolDev/Janus-35B-HERETIC with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FoolDev/Janus-35B-HERETIC: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": "FoolDev/Janus-35B-HERETIC:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FoolDev/Janus-35B-HERETIC with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FoolDev/Janus-35B-HERETIC: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 FoolDev/Janus-35B-HERETIC:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use FoolDev/Janus-35B-HERETIC with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FoolDev/Janus-35B-HERETIC: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 "FoolDev/Janus-35B-HERETIC: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"

Docker Model Runner
How to use FoolDev/Janus-35B-HERETIC with Docker Model Runner:
```
docker model run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
```

Lemonade

How to use FoolDev/Janus-35B-HERETIC with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FoolDev/Janus-35B-HERETIC:Q4_K_M

Run and chat with the model

lemonade run user.Janus-35B-HERETIC-Q4_K_M

List all available models

lemonade list

Janus-35B-HERETIC / examples /README.md

FoolDev

Complete examples/ suite parity with Thanatos-27B (adapted to Janus)

5471e29 about 7 hours ago

preview code

Raw

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3.74 kB

Janus-35B examples

Four minimal entry points. Pick the one that matches how you run models.

File	Backend	When to use
`ollama_chat.py`	Ollama HTTP API	You already have `ollama serve` running and the `janus` model created from the project `Modelfile`. Text + tool calling — vision via Ollama is broken upstream for this arch.
`transformers_quickstart.py`	Hugging Face Transformers	You want to run the Heretic safetensors (`llmfan46/Qwen3.6-35B-A3B-uncensored-heretic`) on GPU, optionally in 4-bit via bitsandbytes.
`llama_cpp_quickstart.py`	llama-cpp-python	You want to invoke a local GGUF directly without a daemon (CI, batch jobs, scripts). Text only.
`llama_cpp_vision.py`	llama-cpp-python + mmproj	Image input. Loads a text GGUF + `Qwen3.6-35B-A3B-uncensored-heretic-mmproj-BF16.gguf` and answers questions about an image. The only working vision path right now.

All four apply the same Janus system prompt and sampling defaults (temp=0.6, top_p=0.95, top_k=20, repeat_penalty=1.05) so behavior should be consistent across backends modulo quantization noise. The three non-Ollama scripts set them explicitly; ollama_chat.py inherits them from the Modelfile / bridge files.

Setup

Ollama

Pull straight from HF (gets the bundled Q4_K_M GGUF + this repo's root-level template / system / params files via HF's Ollama bridge):

ollama pull hf.co/FoolDev/Janus-35B-HERETIC              # ~19 GB Q4_K_M (only bundled quant)
pip install requests
MODEL=hf.co/FoolDev/Janus-35B-HERETIC python ollama_chat.py

Or build a local janus tag from this repo's bundled GGUF without going through the HF pull:

cd ..  &&  ./scripts/load_bundle.sh  &&  cd examples
python ollama_chat.py                                   # MODEL defaults to `janus`

For a non-bundled quant (e.g. Q3_K_M ~14 GB, Q5_K_M ~24 GB), ./scripts/build.sh downloads it from llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GGUF, patches the Modelfile FROM line into a temp copy, and creates the local janus tag:

cd ..  &&  QUANT=Q5_K_M ./scripts/build.sh  &&  cd examples
python ollama_chat.py

Transformers (safetensors)

pip install --upgrade "transformers>=4.45" accelerate sentencepiece bitsandbytes
python transformers_quickstart.py            # 4-bit, ~20 GB VRAM
python transformers_quickstart.py --no-4bit  # bf16, ~70 GB VRAM

llama-cpp-python (GGUF, no daemon)

pip install llama-cpp-python  # CPU-only build
python llama_cpp_quickstart.py /path/to/Janus-35B-A3B.Q4_K_M.gguf --gpu-layers 99

For GPU offload, rebuild llama-cpp-python with the matching backend — see the script header for CMAKE_ARGS recipes (CUDA, Metal, ROCm/HIP).

Vision (image input)

# Pull the projector once (~903 MB) into the repo root:
cd ..  &&  ./scripts/fetch_vision.sh  &&  cd examples

pip install llama-cpp-python pillow
python llama_cpp_vision.py \
  --gguf ../Janus-35B-A3B.Q4_K_M.gguf \
  --mmproj ../Qwen3.6-35B-A3B-uncensored-heretic-mmproj-BF16.gguf \
  --image /path/to/photo.jpg \
  --prompt "Describe this image."

Why not Ollama? Ollama's Go engine has the qwen35 / qwen35moe arch entries (text inference works in 0.24+), but the C++ llama.cpp fallback that Ollama switches to when an mmproj is attached still lacks them. ollama create accepts the dual-FROM and ollama show reports vision capability, but the first inference call fails with error loading model architecture: unknown model architecture: 'qwen35moe'. Tracked in ollama/ollama#15898. Until that's fixed, llama.cpp / llama-cpp-python is the working path for vision.