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
| # Janus-35B — load this repo's bundle into Ollama as a local tag. | |
| # | |
| # The bundled GGUF (Janus-35B-A3B.Q4_K_M.gguf) is qwen35moe-stamped and | |
| # loads directly on stock llama.cpp / Ollama. This script is the | |
| # one-shot path from "I just cloned this repo" to "I have a working | |
| # local Ollama tag": | |
| # | |
| # 1. Resolve the bundle. If it's an LFS pointer (cloned without | |
| # `git lfs pull`), download the real ~19 GB blob via `hf download`. | |
| # 2. Sanity-check `general.architecture` is qwen35moe / qwen35. | |
| # 3. Run `ollama create <tag> -f <temp Modelfile pointing at the | |
| # resolved bundle>`. | |
| # | |
| # Useful if you want a bare local tag (`janus`) rather than the | |
| # `hf.co/FoolDev/Janus-35B-HERETIC` path. | |
| # | |
| # Usage: | |
| # ./scripts/load_bundle.sh # default tag: janus | |
| # TAG=janus-bundle ./scripts/load_bundle.sh | |
| # BUNDLE=/path/to/Janus-35B-A3B.Q4_K_M.gguf ./scripts/load_bundle.sh | |
| # | |
| # Requires: ollama, python3 with the `gguf` package, hf (if the bundle | |
| # needs to be downloaded). | |
| set -euo pipefail | |
| ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" | |
| BUNDLE="${BUNDLE:-${ROOT}/Janus-35B-A3B.Q4_K_M.gguf}" | |
| TAG="${TAG:-janus}" | |
| REPO_ID="${REPO_ID:-FoolDev/Janus-35B-HERETIC}" | |
| MODELFILE="${ROOT}/Modelfile" | |
| echo "[*] bundle: ${BUNDLE}" | |
| echo "[*] tag: ${TAG}" | |
| # ---- 1. Sanity --------------------------------------------------------------- | |
| if ! command -v ollama >/dev/null 2>&1; then | |
| echo "[!] ollama not found in PATH" >&2; exit 1 | |
| fi | |
| if [[ ! -f "${MODELFILE}" ]]; then | |
| echo "[!] missing ${MODELFILE}" >&2; exit 1 | |
| fi | |
| # ---- 2. Resolve bundle (smudge LFS pointer if needed) ------------------------ | |
| resolve_bundle() { | |
| local file="$1" | |
| if [[ ! -f "${file}" ]]; then | |
| return 1 | |
| fi | |
| # LFS pointer files are tiny (a couple hundred bytes) and start with | |
| # `version https://git-lfs.github.com/spec/v1`. | |
| local size | |
| size="$(stat -c '%s' "${file}")" | |
| if (( size < 1024 )) && head -n1 "${file}" | grep -q 'git-lfs'; then | |
| return 1 | |
| fi | |
| return 0 | |
| } | |
| if ! resolve_bundle "${BUNDLE}"; then | |
| # Download to a side path under .cache/ so we don't overwrite the | |
| # LFS pointer in the working tree. Without git-lfs installed, the | |
| # pointer never auto-smudges and the user expects the file in the | |
| # repo root to stay a few hundred bytes. Downstream steps read | |
| # whichever path BUNDLE points at, so just re-point it here. | |
| CACHE_DIR="${ROOT}/.cache" | |
| BUNDLE_NAME="$(basename "${BUNDLE}")" | |
| CACHED="${CACHE_DIR}/${BUNDLE_NAME}" | |
| if resolve_bundle "${CACHED}"; then | |
| echo "[=] using previously downloaded bundle at ${CACHED}" | |
| BUNDLE="${CACHED}" | |
| else | |
| echo "[*] bundle missing or LFS-pointer-only — downloading from ${REPO_ID} to ${CACHED} ..." | |
| HF="" | |
| if command -v hf >/dev/null 2>&1; then | |
| HF="hf" | |
| elif command -v huggingface-cli >/dev/null 2>&1; then | |
| HF="huggingface-cli" | |
| else | |
| echo "[!] neither 'hf' nor 'huggingface-cli' installed; can't fetch bundle" >&2 | |
| echo " pip install -U huggingface_hub" >&2 | |
| exit 1 | |
| fi | |
| mkdir -p "${CACHE_DIR}" | |
| case "${HF}" in | |
| hf) hf download "${REPO_ID}" "${BUNDLE_NAME}" --local-dir "${CACHE_DIR}" ;; | |
| huggingface-cli) huggingface-cli download "${REPO_ID}" "${BUNDLE_NAME}" --local-dir "${CACHE_DIR}" ;; | |
| esac | |
| BUNDLE="${CACHED}" | |
| fi | |
| if ! resolve_bundle "${BUNDLE}"; then | |
| echo "[!] still no usable bundle at ${BUNDLE} after download" >&2; exit 1 | |
| fi | |
| fi | |
| # ---- 3. Inspect arch --------------------------------------------------------- | |
| ARCH="$(python3 - "${BUNDLE}" <<'PY' | |
| import sys | |
| from gguf import GGUFReader, constants | |
| r = GGUFReader(sys.argv[1], "r") | |
| f = r.get_field(constants.Keys.General.ARCHITECTURE) | |
| print(bytes(f.parts[f.data[0]]).decode()) | |
| PY | |
| )" | |
| echo "[*] bundle arch: ${ARCH}" | |
| if [[ "${ARCH}" != "qwen35" && "${ARCH}" != "qwen35moe" ]]; then | |
| echo "[!] unexpected arch '${ARCH}' — refusing to load. Edit this script if intentional." >&2 | |
| exit 1 | |
| fi | |
| # ---- 4. Build a Modelfile copy with FROM pointing at the bundle -------------- | |
| TMP_MODELFILE="$(mktemp -t janus35b-loadbundle.XXXXXX)" | |
| trap 'rm -f "${TMP_MODELFILE}"' EXIT | |
| awk -v p="${BUNDLE}" ' | |
| /^FROM[[:space:]]/ && !done { print "FROM " p; done=1; next } | |
| { print } | |
| ' "${MODELFILE}" > "${TMP_MODELFILE}" | |
| # ---- 5. Create the Ollama model ---------------------------------------------- | |
| echo "[*] ollama create ${TAG} -f <patched modelfile pointing at ${BUNDLE}>" | |
| ollama create "${TAG}" -f "${TMP_MODELFILE}" | |
| echo | |
| echo "[+] Done. Try it:" | |
| echo " ollama run ${TAG}" | |