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 27 days ago

Commit

4208793

1 Parent(s): bec5589

README/examples: tighten Ollama-vision failure mode description

Browse files

Previously implied that `ollama create` itself returns the
`unknown model architecture` error. Empirically that's wrong on
Ollama 0.22 against the dense qwen35 27B + mmproj-F16: `ollama create`
succeeds, `ollama show` reports the `vision` capability with a CLIP
projector attached, and the architecture error only fires from the
runner on the first inference request — at which point it blocks text
inference too (matches the upstream issue's "blocks ALL inference"
phrasing).

Reworded both the loader-compat table row and the examples/README
"Why not Ollama?" note to describe what actually happens. Also flipped
the now-stale "Text only" qualifier on examples/ollama_chat.py to
"Text + tool calling" to match the Modelfile.

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

Files changed (2) hide show

README.md +1 -1
examples/README.md +7 -4

README.md CHANGED Viewed

@@ -223,7 +223,7 @@ This repo intentionally does not redistribute either.
 |---|---|---|---|
 | **llama.cpp** (`llama-mtmd-cli`, `llama-server --mmproj`) | ✅ | ✅ | Reference path. Upstream has the `qwen35`/`qwen35moe` arch entries. |
 | **llama-cpp-python** | ✅ | ✅ | See `examples/llama_cpp_vision.py`. |
-| **Ollama 0.22** | ✅ | ❌ | Vendored llama.cpp fork is missing the architecture entries. Attaching `mmproj` via `FROM` *or* `ADAPTER` returns `unknown model architecture: 'qwen35moe'` (and the same for the dense `qwen35`). See [ollama/ollama#15898](https://github.com/ollama/ollama/issues/15898). Will work once that PR lands. |
 | **LM Studio** | ✅ | ✅ (last tested) | Uses upstream llama.cpp directly. |
 ### Vision via llama.cpp

 |---|---|---|---|
 | **llama.cpp** (`llama-mtmd-cli`, `llama-server --mmproj`) | ✅ | ✅ | Reference path. Upstream has the `qwen35`/`qwen35moe` arch entries. |
 | **llama-cpp-python** | ✅ | ✅ | See `examples/llama_cpp_vision.py`. |
+| **Ollama 0.22** | ✅ | ❌ | Vendored llama.cpp fork is missing the `qwen35` / `qwen35moe` architecture entries. `ollama create` accepts a dual-`FROM` (text + mmproj) and `ollama show` even reports `vision` capability — but the **first inference request** fails with `error loading model architecture: unknown model architecture: 'qwen35'` (or `'qwen35moe'`), and once mmproj is attached this blocks text inference too. See [ollama/ollama#15898](https://github.com/ollama/ollama/issues/15898). Will work once that PR lands. |
 | **LM Studio** | ✅ | ✅ (last tested) | Uses upstream llama.cpp directly. |
 ### Vision via llama.cpp

examples/README.md CHANGED Viewed

@@ -4,7 +4,7 @@ Three minimal entry points. Pick the one that matches how you run models.
 | File | Backend | When to use |
 |---|---|---|
-| `ollama_chat.py` | Ollama HTTP API | You already have `ollama serve` running and the `janus-27b` model created from the project `Modelfile`. **Text only** — vision via Ollama is broken upstream for this arch. |
 | `transformers_quickstart.py` | Hugging Face Transformers | You want to run the upstream safetensors (`Qwen/Qwen3.6-27B`) on GPU, optionally in 4-bit via bitsandbytes. |
 | `llama_cpp_quickstart.py` | llama-cpp-python | You want to invoke a local GGUF directly without a daemon (CI, batch jobs, scripts). Text only. |
 | `llama_cpp_vision.py` | llama-cpp-python + mmproj | **Image input.** Loads a text GGUF + `mmproj-F16.gguf` and answers questions about an image. The only working vision path right now. |
@@ -63,8 +63,11 @@ python llama_cpp_vision.py \
   --prompt "Describe this image."
 ```
-Why not Ollama? Ollama 0.22's vendored llama.cpp is missing the `qwen35`
-architecture entries needed to attach an mmproj — `FROM` and `ADAPTER`
-both fail with `unknown model architecture: 'qwen35moe'`. Tracked in
 [ollama/ollama#15898](https://github.com/ollama/ollama/issues/15898).
 Until that's fixed, llama.cpp / llama-cpp-python is the working path.

 | File | Backend | When to use |
 |---|---|---|
+| `ollama_chat.py` | Ollama HTTP API | You already have `ollama serve` running and the `janus-27b` model created from the project `Modelfile`. **Text + tool calling** — vision via Ollama is broken upstream for this arch. |
 | `transformers_quickstart.py` | Hugging Face Transformers | You want to run the upstream safetensors (`Qwen/Qwen3.6-27B`) on GPU, optionally in 4-bit via bitsandbytes. |
 | `llama_cpp_quickstart.py` | llama-cpp-python | You want to invoke a local GGUF directly without a daemon (CI, batch jobs, scripts). Text only. |
 | `llama_cpp_vision.py` | llama-cpp-python + mmproj | **Image input.** Loads a text GGUF + `mmproj-F16.gguf` and answers questions about an image. The only working vision path right now. |
   --prompt "Describe this image."
 ```
+Why not Ollama? Ollama 0.22's vendored llama.cpp is missing the
+`qwen35` / `qwen35moe` architecture entries. `ollama create` accepts
+the dual-`FROM` and `ollama show` reports `vision` capability, but the
+first inference call fails with `error loading model architecture:
+unknown model architecture: 'qwen35'` (verified empirically against
+the dense 27B + `mmproj-F16.gguf`). Tracked in
 [ollama/ollama#15898](https://github.com/ollama/ollama/issues/15898).
 Until that's fixed, llama.cpp / llama-cpp-python is the working path.