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: 3,466 Bytes
26f1c69 f48fa07 26f1c69 be7f527 f48fa07 26f1c69 f48fa07 26f1c69 f042cb3 26f1c69 f48fa07 26f1c69 e023532 26f1c69 e023532 26f1c69 e023532 26f1c69 e023532 26f1c69 | 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 | #!/usr/bin/env bash
# Janus-35B — create a small-context local tag for OpenAI /v1 clients.
#
# Ollama's /v1/chat/completions (OpenAI-compatible) silently ignores the
# `options` field, so it loads at the baked default `num_ctx 262144` — 19.77 GiB
# of weights plus a 262K f16 KV cache, ~25.4 GiB in total (~23.0 GiB with
# OLLAMA_KV_CACHE_TYPE=q8_0), which is too much for a tight host. Figures
# measured 2026-09-18; see the README's Hardware requirements. This bakes a
# local tag with num_ctx overridden to a loadable value (and num_batch 256),
# which any OpenAI /v1 client can point at.
# Reuses this repo's Modelfile verbatim (TEMPLATE / SYSTEM / stop params), only
# swapping the FROM line to the bundled blob and the num_ctx parameter.
#
# Usage:
# ./scripts/cap_ctx.sh # tag `janus`, num_ctx 4096
# CTX=8192 TAG=janus-cap ./scripts/cap_ctx.sh
# GGUF=/path/to/other.gguf ./scripts/cap_ctx.sh
#
# Requires: ollama, awk.
set -euo pipefail
CTX="${CTX:-4096}"
NBATCH="${NBATCH:-256}"
ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
MODELFILE="${ROOT}/Modelfile"
GGUF="${GGUF:-${ROOT}/Janus-35B-A3B.Q4_K_M.gguf}"
TAG="${TAG:-janus}"
if ! command -v ollama >/dev/null 2>&1; then echo "[!] ollama not found in PATH" >&2; exit 1; fi
if [[ ! -f "${MODELFILE}" ]]; then echo "[!] missing ${MODELFILE}" >&2; exit 1; fi
# A fresh clone leaves ${GGUF} on disk as a ~136-byte git-LFS POINTER, not the
# weights. `[[ -f ]]` is true for that pointer, so the old existence check never
# fired and `ollama create` was handed a text file. Check the GGUF magic instead.
if [[ ! -f "${GGUF}" ]] || [[ "$(head -c 4 "${GGUF}" 2>/dev/null)" != "GGUF" ]]; then
echo "[!] ${GGUF} is not a usable GGUF." >&2
if [[ -f "${GGUF}" ]]; then
echo " It looks like an un-smudged git-LFS pointer." >&2
fi
echo " Fetch the weights with 'git lfs pull', or run './scripts/load_bundle.sh' - which" >&2
echo " downloads into .cache/ and prints the path - then rerun with GGUF=<that path>." >&2
exit 1
fi
echo "[*] tag: ${TAG}"
echo "[*] gguf: ${GGUF}"
echo "[*] num_ctx: ${CTX} num_batch: ${NBATCH}"
TMP="$(mktemp -t janus35b-capctx.XXXXXX)"
trap 'rm -f "${TMP}"' EXIT
# The END rule is load-bearing: the num_ctx rule only REWRITES an existing
# PARAMETER num_ctx line, so if the Modelfile ever loses it this script used to
# emit no override at all and still print "[+] Done. baked at num_ctx=..." - the
# exact OOM it exists to prevent, reported as success. live_check.sh has carried
# the same END fallback since 0.6.4.
awk -v g="${GGUF}" -v c="${CTX}" -v b="${NBATCH}" '
/^FROM[[:space:]]/ && !done { print "FROM " g; done=1; next }
/^PARAMETER[[:space:]]+num_ctx[[:space:]]/ { print "PARAMETER num_ctx " c; print "PARAMETER num_batch " b; n=1; next }
{ print }
END { if (!n) { print "PARAMETER num_ctx " c; print "PARAMETER num_batch " b } }
' "${MODELFILE}" > "${TMP}"
if ! grep -q '^PARAMETER[[:space:]]\+num_ctx[[:space:]]' "${TMP}"; then
echo "[!] internal error: patched Modelfile carries no num_ctx override" >&2
exit 1
fi
echo "[*] ollama create ${TAG} -f <patched modelfile>"
ollama create "${TAG}" -f "${TMP}"
echo
echo "[+] Done. ${TAG} is baked at num_ctx=${CTX} — usable from any OpenAI /v1 client:"
echo " curl http://localhost:11434/v1/chat/completions \\"
echo " -d '{\"model\":\"${TAG}\",\"messages\":[{\"role\":\"user\",\"content\":\"hi\"}]}'"
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