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 commited on 28 days ago

Commit

5cb1604

1 Parent(s): 72958b4

Log README tool-calling rewrite + un-stale ollama_chat.py docstring

Browse files

CHANGELOG: log the README restructure (split Tool/function calling into
Ollama-path and embedded-jinja-path subsections; clarified that Ollama
is the only loader needing the Modelfile TEMPLATE override).

examples/ollama_chat.py: docstring step 2 'Edit ../Modelfile so the FROM
line points at the GGUF path' was stale since commit 3d2e907 pointed the
bundled Modelfile at ./Janus-27B.Q4_K_M.gguf. Replaced the four-step
list with two paths (bundled-GGUF or pull-from-HF) covering both flows.

Files changed (2) hide show

CHANGELOG.md +27 -0
examples/ollama_chat.py +14 -6

CHANGELOG.md CHANGED Viewed

@@ -7,6 +7,33 @@ and documentation**, not the underlying base model.
 ## [Unreleased]
 ### Added
 - `Modelfile` hardware notes: log a measured data point for the
   ASUS ROG Flow Z13 GZ302EA (Ryzen AI Max+ 395 / Radeon 8060S iGPU,

 ## [Unreleased]
+### Changed
+- README "Tool / function calling" section: split into explicit
+  Ollama-path and embedded-jinja-path subsections. The two loader
+  paths produce different on-the-wire formats and the previous text
+  conflated them. The Ollama path (this repo's `Modelfile` TEMPLATE)
+  prompts JSON-in-XML — 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 model to emit
+  the verbose `<function=name>` / `<parameter=arg>` form it was trained
+  on. Both are valid; the model adapts to whichever shape its system
+  prompt prescribes. README now shows both formats with a concrete
+  example each so users pick the parser that matches their loader.
+- README "Chat template" intro: clarified that Ollama is the lone
+  loader that needs the `Modelfile` `TEMPLATE` override; everything
+  else gets correct plain-conversation formatting from the embedded
+  jinja directly. Previous wording implied all loaders handled it
+  automatically.
+### Fixed
+- `examples/ollama_chat.py` docstring: step 2 ("Edit ../Modelfile so
+  the FROM line points at the GGUF path") was stale — commit 3d2e907
+  pointed the bundled `Modelfile` at `./Janus-27B.Q4_K_M.gguf`, which
+  the repo now ships, so the no-edit flow `ollama create janus-27b
+  -f ../Modelfile` works out of the box. Replaced the four-step list
+  with two paths (bundled-GGUF or pull-from-HF) covering both flows.
 ### Added
 - `Modelfile` hardware notes: log a measured data point for the
   ASUS ROG Flow Z13 GZ302EA (Ryzen AI Max+ 395 / Radeon 8060S iGPU,

examples/ollama_chat.py CHANGED Viewed

@@ -2,12 +2,20 @@
 """
 Janus-27B — Ollama chat examples.
-Prerequisites:
-    1. Pull a Qwen 3.6 27B GGUF (e.g. unsloth/Qwen3.6-27B-GGUF).
-    2. Edit ../Modelfile so the FROM line points at the GGUF path.
-    3. ollama create janus-27b -f ../Modelfile
-    4. ollama serve   (usually already running)
-    5. python ollama_chat.py
 The model emits <think>...</think> reasoning blocks before its answer.
 Ollama (as of 0.22) does not always split these into a separate field for

 """
 Janus-27B — Ollama chat examples.
+Prerequisites (pick one):
+    A. From the bundled GGUFs (default flow):
+        $ make build                     # uses Janus-27B.Q4_K_M.gguf
+        # or:
+        $ ollama create janus-27b -f ../Modelfile
+    B. Pull straight from HF:
+        $ ollama run hf.co/FoolDev/janus-27b
+        # then set MODEL=hf.co/FoolDev/janus-27b below
+Then:
+    $ ollama serve         # usually already running
+    $ python ollama_chat.py
 The model emits <think>...</think> reasoning blocks before its answer.
 Ollama (as of 0.22) does not always split these into a separate field for