Instructions to use FoolDev/Thanatos-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FoolDev/Thanatos-27B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="FoolDev/Thanatos-27B")
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/Thanatos-27B", dtype="auto")

llama-cpp-python

How to use FoolDev/Thanatos-27B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FoolDev/Thanatos-27B",
	filename="Thanatos-27B.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/Thanatos-27B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FoolDev/Thanatos-27B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FoolDev/Thanatos-27B: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/Thanatos-27B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FoolDev/Thanatos-27B: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/Thanatos-27B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FoolDev/Thanatos-27B: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/Thanatos-27B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FoolDev/Thanatos-27B:Q4_K_M

Use Docker

docker model run hf.co/FoolDev/Thanatos-27B:Q4_K_M

LM Studio
Jan

vLLM

How to use FoolDev/Thanatos-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FoolDev/Thanatos-27B"
# 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/Thanatos-27B",
		"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/Thanatos-27B:Q4_K_M

SGLang

How to use FoolDev/Thanatos-27B 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/Thanatos-27B" \
    --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/Thanatos-27B",
		"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/Thanatos-27B" \
        --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/Thanatos-27B",
		"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/Thanatos-27B with Ollama:
```
ollama run hf.co/FoolDev/Thanatos-27B:Q4_K_M
```

Unsloth Studio new

How to use FoolDev/Thanatos-27B 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/Thanatos-27B 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/Thanatos-27B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FoolDev/Thanatos-27B to start chatting

Pi new

How to use FoolDev/Thanatos-27B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FoolDev/Thanatos-27B: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/Thanatos-27B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FoolDev/Thanatos-27B 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/Thanatos-27B: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/Thanatos-27B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use FoolDev/Thanatos-27B with Docker Model Runner:
```
docker model run hf.co/FoolDev/Thanatos-27B:Q4_K_M
```

Lemonade

How to use FoolDev/Thanatos-27B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FoolDev/Thanatos-27B:Q4_K_M

Run and chat with the model

lemonade run user.Thanatos-27B-Q4_K_M

List all available models

lemonade list

FoolDev Claude Opus 4.7 commited on 26 days ago

Commit

4c20ab5

1 Parent(s): b0d8482

Add Q3_K_S as a second HF/Ollama quant

Browse files

Ship Janus-27B.Q3_K_S.gguf (~12 GB) alongside the existing Q4_K_M
so users on tighter memory budgets (16 GB GPUs, 32 GB unified-memory
laptops) get a one-liner too:

ollama run hf.co/FoolDev/janus-27b # default Q4_K_M
ollama run hf.co/FoolDev/janus-27b:Q3_K_S # 12 GB option

Same Modelfile applies in both cases, so tool calling / thinking /
stop-token plumbing carries over. README TL;DR + Quick start +
"What's here" updated to surface the quant tag form.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Files changed (3) hide show

CHANGELOG.md +12 -11
Janus-27B.Q3_K_S.gguf +3 -0
README.md +21 -11

CHANGELOG.md CHANGED Viewed

@@ -8,17 +8,18 @@ and documentation**, not the underlying base model.
 ## [Unreleased]
 ### Added
-- `Janus-27B.Q4_K_M.gguf` (~17 GB) shipped in the repo so HF's
-  "Use this model" widget surfaces the Ollama snippet (`ollama run
-  hf.co/FoolDev/janus-27b`) and Ollama can pull weights + Modelfile in
-  one step. Q4_K_M for parity with the 35B sibling and the README's
-  default. Adds `gguf` to the model-card tags so HF lists the repo
-  among GGUF models. Loosens `.gitignore` from `*.gguf` to allow
-  `Janus-27B.*.gguf` while still excluding the upstream
-  `Qwen3.6-27B-*.gguf` that `make build` fetches locally. README's
-  "does not redistribute weights" wording reworked: the wrapper
-  philosophy still holds for the upstream Qwen GGUFs, but we ship a
-  single tagged distribution to back the HF/Ollama UX.
 ### Fixed
 - `Modelfile`: ship a `TEMPLATE` directive mirroring Qwen 3.6 ChatML in

 ## [Unreleased]
 ### Added
+- `Janus-27B.Q4_K_M.gguf` (~17 GB) and `Janus-27B.Q3_K_S.gguf`
+  (~12 GB) shipped in the repo so HF's "Use this model" widget
+  surfaces the Ollama snippet (`ollama run hf.co/FoolDev/janus-27b`)
+  and Ollama can pull weights + Modelfile in one step. Q4_K_M is the
+  default tag; `:Q3_K_S` is the smaller-footprint option for 16 GB
+  GPUs / unified-memory laptops. Adds `gguf` to the model-card tags
+  so HF lists the repo among GGUF models. Loosens `.gitignore` from
+  `*.gguf` to allow `Janus-27B.*.gguf` while still excluding the
+  upstream `Qwen3.6-27B-*.gguf` that `make build` fetches locally.
+  README's "does not redistribute weights" wording reworked: the
+  wrapper philosophy still holds for the upstream Qwen GGUFs, but we
+  ship two tagged distributions to back the HF/Ollama UX.
 ### Fixed
 - `Modelfile`: ship a `TEMPLATE` directive mirroring Qwen 3.6 ChatML in

Janus-27B.Q3_K_S.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:4afb4abcf0207a484b0d7e92c0421b74e8ce1c7a7250bb9d824b79288da68f20
+size 12358727904

README.md CHANGED Viewed

@@ -61,19 +61,20 @@ A personal sibling to [`FoolDev/janus`](https://huggingface.co/FoolDev/janus). S
 ## TL;DR
-One-liner via Hugging Face (pulls the 17 GB Q4_K_M GGUF + this repo's
-Modelfile, including the tool-calling TEMPLATE):
 ```bash
-ollama run hf.co/FoolDev/janus-27b
 ```
-Or build locally if you want a different quant:
 ```bash
 git clone https://huggingface.co/FoolDev/janus-27b && cd janus-27b
-make build                            # downloads ~17 GB Q4_K_M GGUF
-make build QUANT=Q3_K_S               # smaller ~12 GB quant
 ollama run janus-27b
 ```
@@ -119,9 +120,17 @@ The 27B is **dense**: every parameter participates in every forward pass. It's s
 | `CHANGELOG.md` | Versioned tooling/docs changes |
 | `README.md` | This file |
-This repo ships **one** GGUF — `Janus-27B.Q4_K_M.gguf` (~17 GB) — to
-back the HF/Ollama "Use this model" widget (`ollama run
-hf.co/FoolDev/janus-27b`). For other quants or local builds, pull from
 [`unsloth/Qwen3.6-27B-GGUF`](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF)
 and `make build QUANT=...` — the Modelfile here is the same one Ollama
 applies in either path.
@@ -150,8 +159,9 @@ If you want the safetensors for `transformers`, fetch them from [`Qwen/Qwen3.6-2
 Two paths:
 ```bash
-# A. Pull straight from HF (uses the bundled Q4_K_M + this Modelfile):
-ollama run hf.co/FoolDev/janus-27b
 # B. Build locally (lets you pick the quant):
 make build                                              # Q4_K_M  -> janus-27b

 ## TL;DR
+One-liner via Hugging Face (pulls a GGUF + this repo's Modelfile,
+including the tool-calling TEMPLATE):
 ```bash
+ollama run hf.co/FoolDev/janus-27b           # default ~17 GB Q4_K_M
+ollama run hf.co/FoolDev/janus-27b:Q3_K_S    # tighter ~12 GB quant
 ```
+Or build locally for any other quant:
 ```bash
 git clone https://huggingface.co/FoolDev/janus-27b && cd janus-27b
+make build                            # downloads Q4_K_M (default)
+make build QUANT=Q5_K_M               # any unsloth/Qwen3.6-27B-GGUF quant
 ollama run janus-27b
 ```
 | `CHANGELOG.md` | Versioned tooling/docs changes |
 | `README.md` | This file |
+This repo ships two GGUFs to back the HF/Ollama "Use this model"
+widget — `Janus-27B.Q4_K_M.gguf` (~17 GB, default) and
+`Janus-27B.Q3_K_S.gguf` (~12 GB, smaller-footprint option for 16 GB
+GPUs / unified-memory laptops):
+```bash
+ollama run hf.co/FoolDev/janus-27b           # default Q4_K_M
+ollama run hf.co/FoolDev/janus-27b:Q3_K_S    # tighter quant
+```
+For other quants or local builds, pull from
 [`unsloth/Qwen3.6-27B-GGUF`](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF)
 and `make build QUANT=...` — the Modelfile here is the same one Ollama
 applies in either path.
 Two paths:
 ```bash
+# A. Pull straight from HF (uses the bundled Q4_K_M / Q3_K_S + Modelfile):
+ollama run hf.co/FoolDev/janus-27b           # default Q4_K_M
+ollama run hf.co/FoolDev/janus-27b:Q3_K_S    # tighter quant
 # B. Build locally (lets you pick the quant):
 make build                                              # Q4_K_M  -> janus-27b