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 11 days ago

Commit

5302d10

1 Parent(s): ac94e67

Drop config.json to remove HF's auto-detected qwen3_5 tag

Browse files

HF auto-derives a `qwen3_5` tag from `config.json`'s `"model_type":
"qwen3_5"` / `"architectures": ["Qwen3_5ForConditionalGeneration"]`
declarations. The other safetensors files (15 shards + tokenizer +
index + generation/preprocessor/video_preprocessor configs +
chat_template.jinja) stay on HF, but `AutoModelForCausalLM.from_pretrained(
"FoolDev/Thanatos-27B")` stops working without a config — users
who want the transformers entry point now need to point at
`Qwen/Qwen3.6-27B` for the config, or carry one alongside.

Re-add config.json (`hf upload Qwen/Qwen3.6-27B config.json ...`)
to restore both the auto-tag and the from_pretrained path.

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

Files changed (1) hide show

config.json +0 -140

config.json DELETED Viewed

@@ -1,140 +0,0 @@
-{
-    "architectures": [
-        "Qwen3_5ForConditionalGeneration"
-    ],
-    "image_token_id": 248056,
-    "language_model_only": false,
-    "model_type": "qwen3_5",
-    "text_config": {
-        "attention_bias": false,
-        "attention_dropout": 0.0,
-        "attn_output_gate": true,
-        "bos_token_id": 248044,
-        "dtype": "bfloat16",
-        "eos_token_id": 248044,
-        "full_attention_interval": 4,
-        "head_dim": 256,
-        "hidden_act": "silu",
-        "hidden_size": 5120,
-        "initializer_range": 0.02,
-        "intermediate_size": 17408,
-        "layer_types": [
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention",
-            "linear_attention",
-            "linear_attention",
-            "linear_attention",
-            "full_attention"
-        ],
-        "linear_conv_kernel_dim": 4,
-        "linear_key_head_dim": 128,
-        "linear_num_key_heads": 16,
-        "linear_num_value_heads": 48,
-        "linear_value_head_dim": 128,
-        "mamba_ssm_dtype": "float32",
-        "max_position_embeddings": 262144,
-        "model_type": "qwen3_5_text",
-        "mtp_num_hidden_layers": 1,
-        "mtp_use_dedicated_embeddings": false,
-        "num_attention_heads": 24,
-        "num_hidden_layers": 64,
-        "num_key_value_heads": 4,
-        "output_gate_type": "swish",
-        "pad_token_id": null,
-        "partial_rotary_factor": 0.25,
-        "rms_norm_eps": 1e-06,
-        "rope_parameters": {
-            "mrope_interleaved": true,
-            "mrope_section": [
-                11,
-                11,
-                10
-            ],
-            "partial_rotary_factor": 0.25,
-            "rope_theta": 10000000,
-            "rope_type": "default"
-        },
-        "tie_word_embeddings": false,
-        "use_cache": true,
-        "vocab_size": 248320
-    },
-    "tie_word_embeddings": false,
-    "transformers_version": "4.57.1",
-    "video_token_id": 248057,
-    "vision_config": {
-        "deepstack_visual_indexes": [],
-        "depth": 27,
-        "hidden_act": "gelu_pytorch_tanh",
-        "hidden_size": 1152,
-        "in_channels": 3,
-        "initializer_range": 0.02,
-        "intermediate_size": 4304,
-        "model_type": "qwen3_5",
-        "num_heads": 16,
-        "num_position_embeddings": 2304,
-        "out_hidden_size": 5120,
-        "patch_size": 16,
-        "spatial_merge_size": 2,
-        "temporal_patch_size": 2
-    },
-    "vision_end_token_id": 248054,
-    "vision_start_token_id": 248053
-}