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: 5,528 Bytes
64b629a e638186 64b629a 39c0d86 64b629a | 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 | #!/usr/bin/env python3
"""
Janus-35B — verify Modelfile and HF Ollama bridge files stay in sync.
The repo ships two parallel Ollama configurations:
- ``Modelfile`` is consumed by the local-build path
(``ollama create janus -f Modelfile``). It contains
``TEMPLATE`` / ``SYSTEM`` / ``PARAMETER`` directives.
- ``template`` / ``system`` / ``params`` at the repo root are consumed by HF's
Ollama bridge when users ``ollama run hf.co/FoolDev/Janus-35B-HERETIC`` directly. HF
does NOT read the Modelfile (per https://huggingface.co/docs/hub/en/ollama).
If the two configurations drift apart, ``hf.co/...`` users and local-build
users get different behaviour — exactly the bug fixed in commit 70ccef1
("Add HF Ollama bridge files (template/system/params)"). This script is
the regression guard: it parses the Modelfile, loads the three bridge
files, and fails on any mismatch.
Usage:
python3 scripts/check_bridge_sync.py
# exit 0 if in sync, 1 (with diff details) if not.
Run this manually before pushing a Modelfile / bridge-file edit. The 27B
sibling repo wires an equivalent script into scripts/check.sh and a
pre-commit hook; this repo intentionally stays leaner and runs it
on demand.
"""
from __future__ import annotations
import json
import re
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
# Ollama Modelfile reference: https://docs.ollama.com/modelfile
TEMPLATE_RE = re.compile(r'^TEMPLATE\s+"""(.*?)"""', re.DOTALL | re.MULTILINE)
SYSTEM_RE = re.compile(r'^SYSTEM\s+"""(.*?)"""', re.DOTALL | re.MULTILINE)
PARAMETER_RE = re.compile(r'^PARAMETER\s+(\S+)\s+(.*?)\s*$', re.MULTILINE)
def parse_modelfile(text: str) -> tuple[str, str, dict[str, object]]:
"""Extract TEMPLATE, SYSTEM, and PARAMETER blocks from a Modelfile."""
tpl_match = TEMPLATE_RE.search(text)
if not tpl_match:
die("Modelfile has no TEMPLATE block")
template = tpl_match.group(1)
sys_match = SYSTEM_RE.search(text)
if not sys_match:
die("Modelfile has no SYSTEM block")
system = sys_match.group(1)
params: dict[str, object] = {}
stops: list[str] = []
for key, raw in PARAMETER_RE.findall(text):
# Strip outer quotes if present.
value: object = raw.strip()
if isinstance(value, str) and len(value) >= 2 and value[0] == value[-1] == '"':
value = value[1:-1]
# Stop tokens accumulate; everything else is scalar.
if key == "stop":
stops.append(value) # type: ignore[arg-type]
continue
# Cast known numeric params.
if key in {"temperature", "top_p", "top_k", "repeat_penalty",
"num_ctx", "num_predict", "num_gpu", "num_batch", "seed"}:
try:
value = float(value) if "." in str(value) else int(value) # type: ignore[arg-type]
except (TypeError, ValueError):
pass
params[key] = value
if stops:
params["stop"] = stops
return template, system, params
def die(msg: str) -> None:
print(f"[FAIL] {msg}", file=sys.stderr)
sys.exit(1)
def diff_strings(label: str, expected: str, actual: str) -> bool:
if expected == actual:
return True
print(f"[FAIL] {label} drift detected", file=sys.stderr)
print(f" Modelfile len={len(expected)} bridge file len={len(actual)}", file=sys.stderr)
# Show the first diverging line for quick orientation.
e_lines = expected.splitlines()
a_lines = actual.splitlines()
for i, (e, a) in enumerate(zip(e_lines, a_lines)):
if e != a:
print(f" first diff at line {i + 1}:", file=sys.stderr)
print(f" modelfile : {e!r}", file=sys.stderr)
print(f" bridge : {a!r}", file=sys.stderr)
return False
if len(e_lines) != len(a_lines):
print(f" line count differs: modelfile={len(e_lines)} bridge={len(a_lines)}",
file=sys.stderr)
return False
def main() -> int:
modelfile = (ROOT / "Modelfile").read_text()
bridge_template = (ROOT / "template").read_text()
bridge_system = (ROOT / "system").read_text()
bridge_params = json.loads((ROOT / "params").read_text())
mf_template, mf_system, mf_params = parse_modelfile(modelfile)
ok = True
# 1. TEMPLATE: byte-for-byte.
ok &= diff_strings("TEMPLATE", mf_template, bridge_template)
# 2. SYSTEM: trim trailing whitespace on both ends. The bridge file
# typically has a trailing newline; the Modelfile block doesn't.
ok &= diff_strings("SYSTEM", mf_system.strip(), bridge_system.strip())
# 3. PARAMETER vs params JSON: compare normalized dicts.
if mf_params != bridge_params:
print("[FAIL] params drift detected", file=sys.stderr)
for k in sorted(set(mf_params) | set(bridge_params)):
mv = mf_params.get(k, "<missing>")
bv = bridge_params.get(k, "<missing>")
if mv != bv:
print(f" {k}: modelfile={mv!r} bridge={bv!r}", file=sys.stderr)
ok = False
if not ok:
print("\n[!] Modelfile and bridge files are out of sync.", file=sys.stderr)
print(" Edit them together: any change to TEMPLATE / SYSTEM /",
file=sys.stderr)
print(" PARAMETER must be reflected in template / system / params.",
file=sys.stderr)
return 1
print("[ ok ] Modelfile <-> bridge files in sync")
return 0
if __name__ == "__main__":
sys.exit(main())
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