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") - 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
- 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"
docs: fix model-tag seam for HF-pull users + vision caveat + CITATION version
Browse filesFrom the round-2 review:
- The `ollama show janus` note claimed "either path," but the short `janus` tag
only exists after the local build (path B); every inference example hard-codes
model "janus", so a TL;DR/path-A puller hit `model 'janus' not found` on their
first snippet. Corrected the claim and added a note to substitute the full
hf.co/FoolDev/Janus-35B-HERETIC tag (or `ollama cp`).
- llama-server vision block now documents the reasoning_content/content split
and the >=500 max_tokens budget (parity with the Thanatos sibling).
- CITATION.cff gains version 0.1.0 + date-released 2026-05-20 so the HF "Cite
this model" widget generates a dated, versioned citation.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- CHANGELOG.md +12 -0
- CITATION.cff +2 -0
- README.md +6 -1
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- **Frontmatter tags `reasoning` + `distillation`.** The card positions Janus
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as reasoning-first and distilled (and `CITATION.cff` already lists both as
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keywords), but neither was a searchable Hub tag — added both.
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### Changed (default sampling matched to the Fable teacher)
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- **Default sampling raised to a Fable-matched profile** in `params` and the
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override.
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### Fixed
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- **Hardware-table eval disclaimer no longer self-contradicts.** "Known
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limitations" said the table numbers are "estimates, not measured," yet the
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Z13 row records a measured 28.71 tok/s — reworded to carve out that row.
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- **Frontmatter tags `reasoning` + `distillation`.** The card positions Janus
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as reasoning-first and distilled (and `CITATION.cff` already lists both as
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keywords), but neither was a searchable Hub tag — added both.
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- **`CITATION.cff` now carries `version` + `date-released`.** Added `0.1.0` /
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`2026-05-20` (mirroring the CHANGELOG release anchor) so the HF "Cite this
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model" widget generates a dated, versioned citation.
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### Changed (default sampling matched to the Fable teacher)
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- **Default sampling raised to a Fable-matched profile** in `params` and the
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override.
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### Fixed
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- **Inference examples no longer 404 for HF-pull users.** The `ollama show janus`
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claim said "either path," but the short `janus` tag only exists after the local
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build (path B); every inference example hard-codes `model: "janus"`, so a
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TL;DR/path-A puller hit `model 'janus' not found`. Corrected the claim and added
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a note to substitute the full `hf.co/FoolDev/Janus-35B-HERETIC` tag (or `ollama cp`).
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- **Vision block documents the `reasoning_content` split.** The llama-server
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vision recipe now notes the thinking trace lands in `message.reasoning_content`
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and the answer in `message.content`, and to budget ≥500 `max_tokens` so
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reasoning doesn't crowd out the answer (parity with the Thanatos sibling).
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- **Hardware-table eval disclaimer no longer self-contradicts.** "Known
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limitations" said the table numbers are "estimates, not measured," yet the
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Z13 row records a measured 28.71 tok/s — reworded to carve out that row.
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cff-version: 1.2.0
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title: "Janus-35B: A Mixture-of-Experts Distillation Wrapper for llmfan46's Qwen 3.6 35B-A3B Uncensored Heretic"
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message: "If you use this model card or its accompanying files, please cite as below."
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type: software
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cff-version: 1.2.0
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version: 0.1.0
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date-released: "2026-05-20"
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title: "Janus-35B: A Mixture-of-Experts Distillation Wrapper for llmfan46's Qwen 3.6 35B-A3B Uncensored Heretic"
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message: "If you use this model card or its accompanying files, please cite as below."
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type: software
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ollama create janus -f Modelfile && ollama run janus
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```
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After
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### Inference examples
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Once the model is loaded (via `ollama run janus`, `lms server`, or `llama-server`), all the standard OpenAI-compatible clients work. Examples assume the loader is listening on `http://localhost:11434` (Ollama default) — adjust the port for LM Studio (`:1234`) or llama.cpp (`:8080`). Runnable versions of everything below live in [`examples/`](examples/README.md).
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#### curl
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```bash
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--host 127.0.0.1 --port 8765 -c 8192 -ngl 99
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# then POST OpenAI-style chat completions with an image_url content block —
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# e.g. {"type":"image_url","image_url":{"url":"data:image/jpeg;base64,..."}}
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# B. CLI via llama-mtmd-cli (one-shot). It's a separate cmake target, so a
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# selective build can skip it; a plain `cmake --build build` produces it.
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ollama create janus -f Modelfile && ollama run janus
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```
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After the local build (path B), `ollama show janus` lists `completion`, `tools`, and `thinking` under Capabilities. (The HF-pull paths register the model under the full tag `hf.co/FoolDev/Janus-35B-HERETIC`, not `janus`.)
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### Inference examples
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Once the model is loaded (via `ollama run janus`, `lms server`, or `llama-server`), all the standard OpenAI-compatible clients work. Examples assume the loader is listening on `http://localhost:11434` (Ollama default) — adjust the port for LM Studio (`:1234`) or llama.cpp (`:8080`). Runnable versions of everything below live in [`examples/`](examples/README.md).
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> The examples use `model: "janus"`, the tag from the local build (path B). If you pulled via the TL;DR one-liner instead, use the full tag `hf.co/FoolDev/Janus-35B-HERETIC`, or run `ollama cp hf.co/FoolDev/Janus-35B-HERETIC janus` once to create the short tag.
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#### curl
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```bash
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--host 127.0.0.1 --port 8765 -c 8192 -ngl 99
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# then POST OpenAI-style chat completions with an image_url content block —
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# e.g. {"type":"image_url","image_url":{"url":"data:image/jpeg;base64,..."}}
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# The thinking trace arrives in message.reasoning_content; the visible
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# answer is in message.content. Budget ≥500 max_tokens so the reasoning
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# block doesn't crowd out the final answer.
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# B. CLI via llama-mtmd-cli (one-shot). It's a separate cmake target, so a
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# selective build can skip it; a plain `cmake --build build` produces it.
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