Text Generation
GGUF
qwen36
Mixture of Experts
conversational
multimodal
agent
ollama
heretic
uncensored
reasoning
distillation
Instructions to use FoolDev/Janus-35B-HERETIC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
- Ollama
How to use FoolDev/Janus-35B-HERETIC with Ollama:
ollama run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
- Unsloth Desktop
- 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 @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": "FoolDev/Janus-35B-HERETIC:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- 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
- Hermes Agent
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
- OpenClaw
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"
Download scripts/fetch_vision.sh from FoolDev/Janus-35B-HERETIC: direct link, hf CLI and curl.
- Browser
- Download file 3.66 kB
-
https://huggingface.co/FoolDev/Janus-35B-HERETIC/resolve/main/scripts/fetch_vision.sh
- Command line
-
hf download hf://FoolDev/Janus-35B-HERETIC/scripts/fetch_vision.sh
-
curl -L -o fetch_vision.sh https://huggingface.co/FoolDev/Janus-35B-HERETIC/resolve/main/scripts/fetch_vision.sh
3.66 kB
| # Janus-35B — fetch the vision projector (mmproj) for image input. | |
| # | |
| # Why this is separate from build.sh: | |
| # build.sh is for the Ollama text path. The mmproj is only known-good on | |
| # llama.cpp / llama-cpp-python: attaching it to Ollama was observed failing | |
| # with `unknown model architecture` and has not been re-tested since | |
| # ollama/ollama#14575 closed (see README Vision section). Note there is no | |
| # "vendored llama.cpp fork" -- Ollama serves every GGML model through the | |
| # bundled llama-server (ollama/ollama#16031); the old wording here was wrong. | |
| # | |
| # Usage: | |
| # ./scripts/fetch_vision.sh # default: BF16 (902 MB) | |
| # MMPROJ_PATH=/path/to/proj.gguf ./scripts/fetch_vision.sh # destination override | |
| # | |
| # The Heretic GGUF repo publishes the projector as BF16 only (its Files | |
| # listing, HF API, 2026-09-18); for another precision, point REPO_ID / | |
| # FILE_NAME at a projector from any other Qwen3.6-35B-A3B GGUF repo: | |
| # REPO_ID=<other-qwen3.6-35b-a3b-gguf-repo> FILE_NAME=mmproj-F16.gguf ./scripts/fetch_vision.sh | |
| # (vision tokens are projected the same way across Qwen 3.6 35B-A3B | |
| # finetunes, so a family projector is functionally interchangeable.) | |
| # | |
| # Requires: huggingface-cli (or hf). | |
| set -euo pipefail | |
| # Resolve a Hugging Face CLI. The bare name `hf` is ambiguous: Arch's `hf` | |
| # package ships an unrelated hidden-file utility under that name, so probing | |
| # with `command -v hf` alone can select the wrong binary and turn a download | |
| # into a baffling argument error. Prefer the unambiguous `huggingface-cli`, | |
| # and accept `hf` only when its help text identifies it as Hugging Face's. | |
| detect_hf() { | |
| local candidate | |
| for candidate in huggingface-cli hf; do | |
| command -v "${candidate}" >/dev/null 2>&1 || continue | |
| if "${candidate}" --help 2>&1 | grep -qiE 'huggingface|hf\.co'; then | |
| printf '%s\n' "${candidate}" | |
| return 0 | |
| fi | |
| done | |
| return 1 | |
| } | |
| PRECISION="${1:-${PRECISION:-BF16}}" | |
| REPO_ID="${REPO_ID:-llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GGUF}" | |
| FILE_NAME="${FILE_NAME:-Qwen3.6-35B-A3B-uncensored-heretic-mmproj-${PRECISION}.gguf}" | |
| ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" | |
| DEST="${MMPROJ_PATH:-${ROOT}/${FILE_NAME}}" | |
| echo "[*] repo: ${REPO_ID}" | |
| echo "[*] precision: ${PRECISION}" | |
| echo "[*] file: ${FILE_NAME}" | |
| echo "[*] dest: ${DEST}" | |
| if [[ -f "${DEST}" ]]; then | |
| echo "[=] already present at ${DEST}, skipping." | |
| exit 0 | |
| fi | |
| if ! HF="$(detect_hf)"; then | |
| echo "[!] No Hugging Face CLI found (an unrelated 'hf' on PATH does not count)." >&2 | |
| echo " pip install -U huggingface_hub" >&2 | |
| exit 1 | |
| fi | |
| DEST_DIR="$(dirname "${DEST}")" | |
| mkdir -p "${DEST_DIR}" | |
| "${HF}" download "${REPO_ID}" "${FILE_NAME}" --local-dir "${DEST_DIR}" | |
| # `hf download REPO FILE --local-dir DIR` writes to DIR/FILE: only the directory | |
| # part of MMPROJ_PATH is honoured and the basename is discarded. Without this | |
| # move, MMPROJ_PATH=~/models/mmproj.gguf downloaded ~900 MB successfully and then | |
| # reported "download failed" and exited 1, and the re-run guard above probed a | |
| # path that was never created. | |
| FETCHED="${DEST_DIR}/${FILE_NAME}" | |
| if [[ "${FETCHED}" != "${DEST}" && -f "${FETCHED}" ]]; then | |
| mv -- "${FETCHED}" "${DEST}" | |
| fi | |
| if [[ ! -f "${DEST}" ]]; then | |
| echo "[!] download failed: ${DEST} not present." >&2 | |
| exit 1 | |
| fi | |
| echo | |
| echo "[+] Done. Use it via:" | |
| echo " python ${ROOT}/examples/llama_cpp_vision.py \\" | |
| echo " --gguf ${ROOT}/Janus-35B-A3B.Q4_K_M.gguf \\" | |
| echo " --mmproj ${DEST} \\" | |
| echo " --image /path/to/photo.jpg \\" | |
| echo " --prompt 'Describe this image.'" | |