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: 4,705 Bytes
2e81416 | 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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | #!/usr/bin/env bash
# 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}"
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