Instructions to use FoolDev/Janus-35B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FoolDev/Janus-35B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="FoolDev/Janus-35B") 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", dtype="auto") - llama-cpp-python
How to use FoolDev/Janus-35B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="FoolDev/Janus-35B", 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
- llama.cpp
How to use FoolDev/Janus-35B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf FoolDev/Janus-35B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf FoolDev/Janus-35B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf FoolDev/Janus-35B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf FoolDev/Janus-35B: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:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FoolDev/Janus-35B: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:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FoolDev/Janus-35B:Q4_K_M
Use Docker
docker model run hf.co/FoolDev/Janus-35B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use FoolDev/Janus-35B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FoolDev/Janus-35B" # 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", "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:Q4_K_M
- SGLang
How to use FoolDev/Janus-35B 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" \ --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", "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" \ --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", "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 with Ollama:
ollama run hf.co/FoolDev/Janus-35B:Q4_K_M
- Unsloth Studio new
How to use FoolDev/Janus-35B 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 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 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 to start chatting
- Pi new
How to use FoolDev/Janus-35B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf FoolDev/Janus-35B: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:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use FoolDev/Janus-35B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf FoolDev/Janus-35B: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:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use FoolDev/Janus-35B with Docker Model Runner:
docker model run hf.co/FoolDev/Janus-35B:Q4_K_M
- Lemonade
How to use FoolDev/Janus-35B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FoolDev/Janus-35B:Q4_K_M
Run and chat with the model
lemonade run user.Janus-35B-Q4_K_M
List all available models
lemonade list
File size: 6,632 Bytes
64b629a 52b76d1 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 | # Changelog
All notable changes to this repository. Format loosely follows
[Keep a Changelog](https://keepachangelog.com/en/1.1.0/). This repo holds
a model card, an Ollama Modelfile, the HF Ollama-bridge `template` /
`system` / `params` files, and the bundled Q4_K_M GGUF, so versions
track the **tooling and documentation**, not the underlying base model.
## [Unreleased]
### Changed (sibling rename β Thanatos-27B β Thanatos-27B-Heretic)
- **README "Related models" row** updated to point at
`FoolDev/Thanatos-27B-Heretic` (the dense sibling renamed from
`FoolDev/Thanatos-27B` on 2026-05-23 along with a base swap to
`llmfan46/Qwen3.6-27B-uncensored-heretic-v2`, an uncensored
Heretic-style abliteration of Qwen 3.6 27B). Description now
notes the Heretic base, the uncensored property, and the
old-path 307 redirect HF serves.
### Added
- Root-level `template`, `system`, and `params` files for HF's Ollama
bridge. The bridge generates Ollama manifests at request time from
these three files (NOT from `Modelfile` β confirmed against
https://huggingface.co/docs/hub/en/ollama). Without them, `ollama
run hf.co/FoolDev/janus` got an auto-generated manifest with the
broken `{{ if .Prompt }} .Prompt }}<|im_end|>` template (Ollama's
faulty Go-template conversion of the GGUF's embedded jinja),
corrupted stop tokens (`".Prompt }}<|im_end|>"` bleed), and no
`.Tools` / `.ToolCalls` blocks β so the published Ollama tag
advertised `completion` only and rejected any request with a
`tools` array. The three files mirror the `Modelfile`'s `TEMPLATE`
/ `SYSTEM` / `PARAMETER` directives; both routes wire tool calling
correctly. Edit them together when changing one. Verified by
re-pulling the fresh tag: `ollama show hf.co/FoolDev/janus` now
reports `completion`, `tools`, `thinking` and tool calls round-trip
end-to-end through `/api/chat`.
### Changed
- README "Tool / function calling" section: split into explicit
Ollama-path and embedded-jinja-path subsections. Earlier wording
conflated the two on-the-wire formats. The Ollama path (Modelfile
`TEMPLATE` and the new `template` bridge file, both kept in sync)
prompts JSON-in-XML β the form Ollama's tool-call extractor parses
into a structured `tool_calls` array. The embedded-jinja path
(llama.cpp, llama-cpp-python, LM Studio) reads the Qwen 3.6 native
chat template baked into the GGUF, which prompts the verbose
`<function=name>` / `<parameter=arg>` form the model was trained
on. Both are valid; the model adapts to whichever shape the system
prompt prescribes. README now shows both formats side by side.
- README "Quick start / Ollama" section: documents both pull paths
(`hf.co/...` via bridge files vs `make ... -f Modelfile` locally)
and explicitly notes that HF's bridge does not read `Modelfile`.
- README "Hardware requirements" intro: re-framed the "~38 GB
minimum" claim as "~38 GB at default `num_ctx 16384`" and
documented that 32 GB hosts can fit the model by trimming context
and batch size.
- README "Quick start / Ollama" snippet: show both
`ollama run hf.co/FoolDev/janus` and the explicit-tag form
`ollama run hf.co/FoolDev/janus:Q4_K_M`. Same blob (the default
tag maps to Q4_K_M), but parity with the 27B sibling β which lists
both `:latest` and `:Q3_K_S` β and removes ambiguity for users
scripting against an explicit quant tag. Verified the explicit tag
resolves to the same manifest (model SHA `a076aa0d3a1a`, bridge
blobs `22c7ade72045` / `84a1a6ac580b` / `f7b1992cf9c1`).
### Added (cont'd)
- README `## TL;DR` section near the top of the model card, mirroring
the 27B sibling. Two paths (HF Ollama bridge / local Modelfile
build) with explicit tags and a one-line capability check. Notes
the bridge ingests `template` / `system` / `params`, not
`Modelfile`, so users skimming the top of the page won't form the
wrong mental model of which file gets used when.
- `CITATION.cff` for citation metadata (Apache-2.0, references the
upstream Qwen3.6-35B-A3B base and the dense Janus-27B sibling).
The 27B sibling has had this file since 0.5.0; adding here for
parity so academic-style citations work across both repos.
- `LICENSE` file containing the full Apache 2.0 text. The model card
front-matter has always declared `license: apache-2.0` and the
upstream Qwen 3.6 license inherits Apache-2.0, but until now the
repo lacked the actual license text file. Same Apache 2.0 text
shipped in the 27B sibling.
- `scripts/check_bridge_sync.py` β regression guard for the
`Modelfile` <-> `template` / `system` / `params` sync invariant.
The two configurations are consumed by different code paths
(`ollama create -f Modelfile` for local builds vs HF's Ollama
bridge for `hf.co/...` pulls β HF does not read `Modelfile`), so
drift between them re-introduces the bug fixed in commit 70ccef1
where `hf.co/FoolDev/janus` shipped a broken auto-generated
template while local builds had the correct one. Script parses
the Modelfile's `TEMPLATE` / `SYSTEM` / `PARAMETER` directives,
loads the three bridge files, and fails on any mismatch with a
per-key diff. Run on demand before pushing edits to either side
of the configuration. The 27B sibling wires an equivalent script
into a pre-commit hook (commit 5c67b08); this repo stays leaner
and runs it manually.
### Fixed
- README "Chat template" intro previously claimed all loaders handle
the embedded jinja automatically. True for llama.cpp / LM Studio /
llama-cpp-python; not true for Ollama, which needs an explicit
override (the `Modelfile` TEMPLATE block locally, the root-level
`template` file when serving via `hf.co/...`).
- README "Tool / function calling" earlier said the XML form
`<function=name><parameter=arg>` is "not what this model produces".
That was wrong: the embedded GGUF jinja prompts exactly that form,
and llama.cpp / LM Studio / llama-cpp-python users will see it.
The "JSON-in-XML" claim only applies on the Ollama path because
that's what the Modelfile TEMPLATE prompt instructs.
## [0.1.0] β initial public release
### Added
- Model card with architecture overview, sampling defaults, hardware
table, and `Modelfile` for `ollama create janus -f Modelfile`.
- Bundled `Janus-35B-A3B.Q4_K_M.gguf` (~19 GB) via Git LFS so the HF
"Use this model" widget surfaces a working `ollama run` snippet.
- Tokyo Night themed banner (PNG sourced from the SVG).
- Status badges for license, base model, architecture, quant.
- Linked sibling `FoolDev/janus-27b` (dense Qwen 3.6 27B base) under
Related models.
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