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: 6,929 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 | #!/usr/bin/env bash
# Janus-35B — smoke test against a running Ollama daemon.
#
# Verifies:
# 1. The Ollama server is reachable.
# 2. The target model is loaded / loadable.
# 3. The model exposes the `tools` capability (Modelfile TEMPLATE wired).
# 4. A single chat round-trip succeeds and produces non-empty output.
# 5. No chat-template control tokens leak into the response.
# 6. (TOOLS_TEST=1) An end-to-end tool-call round-trip emits a structured
# tool_calls array with the expected name and arguments. Off by default
# because it costs ~5-10 sec of inference; on for comprehensive runs.
#
# Usage:
# ./scripts/smoke_test.sh # fast checks only
# TOOLS_TEST=1 ./scripts/smoke_test.sh # add tool-call round-trip
# MODEL=hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M ./scripts/smoke_test.sh
# HOST=http://localhost:11434 ./scripts/smoke_test.sh
#
# Requires: curl, jq.
set -euo pipefail
MODEL="${MODEL:-janus}"
HOST="${HOST:-http://localhost:11434}"
PROMPT="${PROMPT:-Reply with the single word: OK}"
require() {
if ! command -v "$1" >/dev/null 2>&1; then
echo "[!] missing dependency: $1" >&2; exit 1
fi
}
require curl
require jq
echo "[*] host: ${HOST}"
echo "[*] model: ${MODEL}"
# ---- 1. Server up? ----------------------------------------------------------
if ! curl -fsS "${HOST}/api/tags" >/dev/null; then
echo "[!] Ollama not reachable at ${HOST}. Is 'ollama serve' running?" >&2
exit 1
fi
echo "[+] server reachable"
# ---- 2. Model present? ------------------------------------------------------
# Match case-insensitively: Ollama 0.24 normalizes model names at lookup but
# preserves whatever case was first registered on disk (e.g. an earlier
# session may leave a `Janus-35B:latest` manifest dir behind even when a
# build was invoked with TAG=janus). The exact tag the user typed is still
# valid for `ollama run` — the comparison just needs to be case-folded to
# match.
if ! curl -fsS "${HOST}/api/tags" | jq -e --arg m "${MODEL}" '.models[] | select((.name | ascii_downcase) | startswith($m | ascii_downcase))' >/dev/null; then
echo "[!] Model '${MODEL}' not found. Build it first:" >&2
echo " ./scripts/build.sh # Q4_K_M" >&2
echo " ./scripts/build.sh Q3_K_M # smaller quant" >&2
echo " ./scripts/load_bundle.sh # load this repo's qwen35moe bundle" >&2
exit 1
fi
echo "[+] model present"
# ---- 3. Capability guard ----------------------------------------------------
# The Modelfile TEMPLATE must expose .Tools / .ToolCalls so Ollama lists
# `tools` under capabilities. Without it, /api/chat with a tools array returns
# 400 "does not support tools" even though plain chat works. Catches Modelfile
# regressions that strip or break the TEMPLATE.
CAPS="$(curl -fsS "${HOST}/api/show" -H 'Content-Type: application/json' \
-d "$(jq -n --arg m "${MODEL}" '{name: $m}')" | jq -r '.capabilities[]?')"
if ! grep -qx -- 'tools' <<<"${CAPS}"; then
echo "[!] model missing capability: tools" >&2
echo " Modelfile likely missing TEMPLATE that references .Tools / .ToolCalls." >&2
echo "----- present capabilities -----" >&2
echo "${CAPS:-<none>}" >&2
echo "--------------------------------" >&2
exit 1
fi
echo "[+] capabilities include: tools"
# ---- 4. Round-trip ----------------------------------------------------------
echo "[*] sending test prompt..."
RESP="$(curl -fsS "${HOST}/api/chat" \
-H 'Content-Type: application/json' \
-d "$(jq -n --arg m "${MODEL}" --arg p "${PROMPT}" '{
model: $m,
messages: [{role:"user", content:$p}],
stream: false
}')" | jq -r '.message.content // empty')"
if [[ -z "${RESP}" ]]; then
echo "[!] empty response from model" >&2
exit 1
fi
# Token-leakage guard: if any of the chat-template control tokens show up
# verbatim in the response, the Modelfile stop-token list is broken and the
# model is bleeding past EOS.
LEAKED=()
for tok in '<|im_start|>' '<|im_end|>' '<|endoftext|>'; do
if grep -qF -- "${tok}" <<<"${RESP}"; then
LEAKED+=("${tok}")
fi
done
if (( ${#LEAKED[@]} )); then
echo "[!] response contains raw control tokens: ${LEAKED[*]}" >&2
echo " Modelfile likely missing PARAMETER stop directives." >&2
echo "----- model said -----" >&2
echo "${RESP}" >&2
echo "----------------------" >&2
exit 1
fi
echo "[+] round-trip OK"
echo "----- model said -----"
echo "${RESP}"
echo "----------------------"
# ---- 5. Tool-call round-trip (opt-in via TOOLS_TEST=1) ----------------------
#
# Capability advertisement (step 3) only checks the TEMPLATE references
# .Tools / .ToolCalls. It does NOT check the model actually emits a parseable
# tool call. A regression in the prompt scaffolding (e.g. the system-prompt
# instructions inside the TEMPLATE going stale) can leave capabilities
# reported correctly but tool calls failing — the assistant prose-describes
# the call instead of emitting <tool_call>{...}</tool_call>. This block sends
# a tools-array request, parses .message.tool_calls, and asserts the shape
# matches.
if [[ "${TOOLS_TEST:-0}" == "1" ]]; then
echo "[*] tool-call round-trip..."
TOOL_RESP="$(curl -fsS "${HOST}/api/chat" \
-H 'Content-Type: application/json' \
-d "$(jq -n --arg m "${MODEL}" '{
model: $m,
messages: [{role:"user", content:"Call get_weather for Tokyo. Respond ONLY with the tool call."}],
tools: [{
type: "function",
function: {
name: "get_weather",
description: "Get the weather for a city",
parameters: {
type: "object",
properties: {city: {type: "string"}},
required: ["city"]
}
}
}],
stream: false,
options: {num_predict: 1024, temperature: 0.3}
}')")"
TC_COUNT="$(jq -r '.message.tool_calls // [] | length' <<<"${TOOL_RESP}")"
if [[ "${TC_COUNT}" -lt 1 ]]; then
echo "[!] model did not emit a tool call" >&2
echo "----- response -----" >&2
echo "${TOOL_RESP}" | jq . >&2
echo "--------------------" >&2
exit 1
fi
TC_NAME="$(jq -r '.message.tool_calls[0].function.name // empty' <<<"${TOOL_RESP}")"
TC_CITY="$(jq -r '.message.tool_calls[0].function.arguments.city // empty' <<<"${TOOL_RESP}")"
if [[ "${TC_NAME}" != "get_weather" ]]; then
echo "[!] unexpected tool name: '${TC_NAME}' (wanted 'get_weather')" >&2
exit 1
fi
if [[ "${TC_CITY,,}" != "tokyo" ]]; then
echo "[!] unexpected city argument: '${TC_CITY}' (wanted 'Tokyo' case-insensitive)" >&2
exit 1
fi
echo "[+] tool-call round-trip OK (name=${TC_NAME} city=${TC_CITY})"
fi
|