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"
File size: 3,661 Bytes
ac021c9 f48fa07 ac021c9 ee4c9b0 ac021c9 f48fa07 e023532 ac021c9 f48fa07 c0d1a26 ac021c9 edc32fb ac021c9 f48fa07 ac021c9 edc32fb ac021c9 edc32fb ac021c9 e023532 ac021c9 f48fa07 ac021c9 | 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 | #!/usr/bin/env bash
# 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.'"
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