Image-Text-to-Text
Transformers
GGUF
qwen36
Mixture of Experts
conversational
multimodal
agent
heretic
uncensored
Instructions to use FoolDev/Janus-35B-HERETIC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- Pi
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
File size: 3,431 Bytes
7f4cc42 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | #!/usr/bin/env bash
# Janus-35B — fetch a Qwen 3.6 35B-A3B GGUF and build the Ollama model.
#
# Usage:
# ./scripts/build.sh # default: Q4_K_M
# ./scripts/build.sh Q5_K_M # different quant
# QUANT=Q6_K ./scripts/build.sh
#
# Skip the download by pointing at a GGUF you already have:
# GGUF_PATH=/path/to/Qwen3.6-35B-A3B-uncensored-heretic-Q4_K_M.gguf ./scripts/build.sh Q4_K_M
#
# Requires: huggingface-cli (or hf), ollama, awk.
set -euo pipefail
QUANT="${1:-${QUANT:-Q4_K_M}}"
REPO_ID="${REPO_ID:-llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GGUF}"
# Filenames at llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GGUF follow
# Qwen3.6-35B-A3B-uncensored-heretic-${QUANT}.gguf
# Check the repo's Files tab for the quants actually published; override
# GGUF_NAME or GGUF_PATH below if the naming differs for your chosen quant.
GGUF_NAME="Qwen3.6-35B-A3B-uncensored-heretic-${QUANT}.gguf"
ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
# GGUF_PATH defaults to ${ROOT}/${GGUF_NAME}, but can be overridden so users
# with cached weights elsewhere don't have to copy or symlink anything.
GGUF_PATH="${GGUF_PATH:-${ROOT}/${GGUF_NAME}}"
MODELFILE="${ROOT}/Modelfile"
TAG="${TAG:-janus}"
echo "[*] repo: ${REPO_ID}"
echo "[*] quant: ${QUANT}"
echo "[*] tag: ${TAG}"
echo "[*] modelfile:${MODELFILE}"
echo "[*] gguf: ${GGUF_PATH}"
# ---- 1. Sanity ---------------------------------------------------------------
if ! command -v ollama >/dev/null 2>&1; then
echo "[!] ollama not found in PATH" >&2; exit 1
fi
if [[ ! -f "${MODELFILE}" ]]; then
echo "[!] Missing ${MODELFILE}" >&2; exit 1
fi
# ---- 2. Download GGUF if missing --------------------------------------------
if [[ -f "${GGUF_PATH}" ]]; then
echo "[=] GGUF already present at ${GGUF_PATH}, skipping download."
else
# Need a HF CLI to fetch the file.
HF=""
if command -v hf >/dev/null 2>&1; then
HF="hf"
elif command -v huggingface-cli >/dev/null 2>&1; then
HF="huggingface-cli"
else
echo "[!] ${GGUF_PATH} not found, and neither 'hf' nor" >&2
echo " 'huggingface-cli' is installed to download it." >&2
echo " Either:" >&2
echo " pip install -U huggingface_hub" >&2
echo " or set GGUF_PATH to an existing GGUF and rerun." >&2
exit 1
fi
echo "[*] Downloading ${GGUF_NAME} from ${REPO_ID} ..."
DEST_DIR="$(dirname "${GGUF_PATH}")"
mkdir -p "${DEST_DIR}"
case "${HF}" in
hf) hf download "${REPO_ID}" "${GGUF_NAME}" --local-dir "${DEST_DIR}" ;;
huggingface-cli) huggingface-cli download "${REPO_ID}" "${GGUF_NAME}" --local-dir "${DEST_DIR}" ;;
esac
fi
if [[ ! -f "${GGUF_PATH}" ]]; then
echo "[!] GGUF still not present at ${GGUF_PATH} after download attempt." >&2
exit 1
fi
# ---- 3. Patch the Modelfile FROM line in a temp copy -------------------------
TMP_MODELFILE="$(mktemp -t janus35b-modelfile.XXXXXX)"
trap 'rm -f "${TMP_MODELFILE}"' EXIT
awk -v p="${GGUF_PATH}" '
/^FROM[[:space:]]/ && !done { print "FROM " p; done=1; next }
{ print }
' "${MODELFILE}" > "${TMP_MODELFILE}"
# ---- 4. Create the Ollama model ---------------------------------------------
echo "[*] ollama create ${TAG} -f <patched modelfile>"
ollama create "${TAG}" -f "${TMP_MODELFILE}"
echo
echo "[+] Done. Try it:"
echo " ollama run ${TAG}"
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