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 10 days ago

Commit

385ed94

1 Parent(s): aaef90f

fix(heal-hf): give an actionable error when the tag isn't pulled

Browse files

Reproduced this session: `make heal-hf` against an empty ollama
store (after a clean wipe) printed only

[*] tag: hf.co/FoolDev/Thanatos-27B:Q4_K_M
[*] store: /home/fool/.ollama/models
make: *** [Makefile:47: heal-hf] Error 1

and exited 1. The intended "could not resolve model blob for tag
'...'. Is the tag pulled? Try: ollama pull <tag>" hint never
appeared.

Root cause is a set-e + pipefail interaction. The blob-locator
line was

MODEL_BLOB="$(ollama show --modelfile "${TAG}" 2>/dev/null | awk ...)"

When the tag isn't pulled, `ollama show` exits 1, `pipefail`
propagates the non-zero exit code through the pipeline (awk's
own 0 doesn't mask it), and `set -e` terminates the script on
the assignment before the explicit `[[ -z "${MODEL_BLOB}" ]]`
check below it ever runs.

Fixed by splitting the assignment so set -e doesn't see a failed
pipeline:

MODEL_BLOB=""
if MODELFILE_OUT="$(ollama show --modelfile "${TAG}" 2>/dev/null)"; then
MODEL_BLOB="$(awk '/^FROM[[:space:]]/ {print $2; exit}' <<<"${MODELFILE_OUT}")"
fi
if [[ -z "${MODEL_BLOB}" || ! -f "${MODEL_BLOB}" ]]; then
red "[!] could not resolve model blob for tag '${TAG}'."
red " Is the tag pulled? Try: ollama pull ${TAG}"
exit 1
fi

The `if cmd; then` form takes the false branch on failure without
tripping set -e — the long-standing shell idiom for "test this
command without aborting on failure." Behavior on the happy path
(tag is pulled, ollama show succeeds) is unchanged.

Added a load-bearing comment block explaining the gotcha so the
next reader doesn't "simplify" it back to the broken direct
substitution form.

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

Files changed (2) hide show

CHANGELOG.md +13 -0
scripts/heal_hf_pull.sh +14 -1

CHANGELOG.md CHANGED Viewed

@@ -7,6 +7,19 @@ and documentation**, not the underlying base model.
 ## [Unreleased]
 ### Added
 - `scripts/heal_hf_pull.sh` now validates the rewritten manifest by
   running `ollama show <tag>` immediately after the atomic mv into

 ## [Unreleased]
+### Fixed
+- `scripts/heal_hf_pull.sh` silent-exit when the target tag isn't
+  pulled. The blob-locator line was
+  `MODEL_BLOB="$(ollama show --modelfile "${TAG}" 2>/dev/null | awk ...)"`;
+  under `set -e + pipefail`, an unpulled tag makes `ollama show`
+  exit non-zero, pipefail propagates the non-zero through the
+  pipeline, and set -e terminates the script before the explicit
+  `[[ -z "${MODEL_BLOB}" ]]` check ever runs. The user saw only
+  `make: *** [Makefile:47: heal-hf] Error 1` with no clue what
+  was wrong. Refactored to use an `if MODELFILE_OUT=...; then`
+  form, which takes the false branch on failure without tripping
+  set -e. The "tag not pulled?" hint now fires as intended.
 ### Added
 - `scripts/heal_hf_pull.sh` now validates the rewritten manifest by
   running `ollama show <tag>` immediately after the atomic mv into

scripts/heal_hf_pull.sh CHANGED Viewed

@@ -56,7 +56,20 @@ done
 # (hf.co's 307 lets `Thanatos-27B` and `thanatos-27b` both resolve to the
 # canonical repo, and ollama stores the manifest under whichever case
 # was first registered).
-MODEL_BLOB="$(ollama show --modelfile "${TAG}" 2>/dev/null | awk '/^FROM[[:space:]]/ {print $2; exit}')"
 if [[ -z "${MODEL_BLOB}" || ! -f "${MODEL_BLOB}" ]]; then
     red "[!] could not resolve model blob for tag '${TAG}'."
     red "    Is the tag pulled? Try: ollama pull ${TAG}"

 # (hf.co's 307 lets `Thanatos-27B` and `thanatos-27b` both resolve to the
 # canonical repo, and ollama stores the manifest under whichever case
 # was first registered).
+#
+# The `if MODELFILE_OUT=...; then` form rather than a direct command
+# substitution is load-bearing: under `set -e + pipefail`, a bare
+# `MODEL_BLOB="$(ollama show ... | awk ...)"` silently terminates
+# the script when the tag isn't pulled (ollama show exits non-zero
+# -> pipefail propagates -> set -e exits the script before the
+# explicit `[[ -z "${MODEL_BLOB}" ]]` check below ever runs). The
+# `if` form takes the false branch on failure without tripping
+# set -e, so the user gets the actionable error instead of a silent
+# `make: *** [Makefile:N: heal-hf] Error 1`.
+MODEL_BLOB=""
+if MODELFILE_OUT="$(ollama show --modelfile "${TAG}" 2>/dev/null)"; then
+    MODEL_BLOB="$(awk '/^FROM[[:space:]]/ {print $2; exit}' <<<"${MODELFILE_OUT}")"
+fi
 if [[ -z "${MODEL_BLOB}" || ! -f "${MODEL_BLOB}" ]]; then
     red "[!] could not resolve model blob for tag '${TAG}'."
     red "    Is the tag pulled? Try: ollama pull ${TAG}"