Instructions to use FoolDev/Thanatos-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 6,869 Bytes
6f2884f 7197abd 6f2884f f4e7fa4 84d3da6 6f2884f 84d3da6 7197abd 6f2884f 7197abd 6f2884f ef3c5d9 7197abd ef3c5d9 6f2884f 0d08cb9 ef3c5d9 6f2884f f4e7fa4 6f2884f c61756c 6f2884f 84d3da6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | #!/usr/bin/env bash
# Thanatos-27B — smoke test against a running Ollama daemon.
#
# Verifies:
# 1. The Ollama server is reachable.
# 2. The target model is loaded / loadable.
# 3. The model exposes the `tools` capability (Modelfile TEMPLATE wired).
# 4. A single chat round-trip succeeds and produces non-empty output.
# 5. No chat-template control tokens leak into the response.
# 6. (TOOLS_TEST=1) An end-to-end tool-call round-trip emits a structured
# tool_calls array with the expected name and arguments. Off by default
# because it costs ~5-10 sec of inference; on for comprehensive runs.
#
# Usage:
# ./scripts/smoke_test.sh # fast checks only
# TOOLS_TEST=1 ./scripts/smoke_test.sh # add tool-call round-trip
# MODEL=hf.co/FoolDev/Thanatos-27B:Q4_K_M ./scripts/smoke_test.sh
# HOST=http://localhost:11434 ./scripts/smoke_test.sh
set -euo pipefail
MODEL="${MODEL:-thanatos-27b}"
HOST="${HOST:-http://localhost:11434}"
PROMPT="${PROMPT:-Reply with the single word: OK}"
red() { printf "\033[31m%s\033[0m\n" "$*"; }
green() { printf "\033[32m%s\033[0m\n" "$*"; }
blue() { printf "\033[34m%s\033[0m\n" "$*"; }
require() {
if ! command -v "$1" >/dev/null 2>&1; then
red "[!] missing dependency: $1"; exit 1
fi
}
require curl
require jq
blue "[*] host: ${HOST}"
blue "[*] model: ${MODEL}"
# 1. Server up?
if ! curl -fsS "${HOST}/api/tags" >/dev/null; then
red "[!] Ollama not reachable at ${HOST}. Is 'ollama serve' running?"
exit 1
fi
green "[+] server reachable"
# 2. Model present? Match case-insensitively: Ollama 0.24 normalizes
# model names at lookup but preserves whatever case was first registered
# on disk (e.g. `make load-bundle` may produce `Thanatos-27B:latest`
# even when invoked with TAG=thanatos-27b, if an earlier session left a
# Thanatos-27B manifest dir behind). The exact tag the user typed is
# still valid for `ollama run` — the comparison just needs to be
# case-folded to match.
if ! curl -fsS "${HOST}/api/tags" | jq -e --arg m "${MODEL}" '.models[] | select((.name | ascii_downcase) | startswith($m | ascii_downcase))' >/dev/null; then
red "[!] Model '${MODEL}' not found. Build it first:"
red " ./scripts/build.sh # Q4_K_M"
red " ./scripts/build.sh Q3_K_S # smaller quant"
red " ./scripts/load_bundle.sh # load this repo's qwen36 bundle"
exit 1
fi
green "[+] model present"
# 3. Capability guard: the Modelfile TEMPLATE must expose .Tools / .ToolCalls
# so Ollama lists `tools` under capabilities. Without it, /api/chat with a
# tools array returns 400 "does not support tools" even though plain chat
# works. Catches Modelfile regressions that strip or break the TEMPLATE.
CAPS="$(curl -fsS "${HOST}/api/show" -H 'Content-Type: application/json' \
-d "$(jq -n --arg m "${MODEL}" '{name: $m}')" | jq -r '.capabilities[]?')"
if ! grep -qx -- 'tools' <<<"${CAPS}"; then
red "[!] model missing capability: tools"
red " Modelfile likely missing TEMPLATE that references .Tools / .ToolCalls."
echo "----- present capabilities -----"
echo "${CAPS:-<none>}"
echo "--------------------------------"
exit 1
fi
green "[+] capabilities include: tools"
# 4. Round-trip
blue "[*] sending test prompt..."
RESP="$(curl -fsS "${HOST}/api/chat" \
-H 'Content-Type: application/json' \
-d "$(jq -n --arg m "${MODEL}" --arg p "${PROMPT}" '{
model: $m,
messages: [{role:"user", content:$p}],
stream: false
}')" | jq -r '.message.content // empty')"
if [[ -z "${RESP}" ]]; then
red "[!] empty response from model"
exit 1
fi
# Token-leakage guard: if any of the chat-template control tokens show up
# verbatim in the response, the Modelfile stop-token list is broken and
# the model is bleeding past EOS. We caught this in a real regression
# (commit 6672746) — the model said OK then emitted "<|endoftext|>
# <|im_start|>user ..." and Ollama kept generating.
LEAKED=()
for tok in '<|im_start|>' '<|im_end|>' '<|endoftext|>'; do
if grep -qF -- "${tok}" <<<"${RESP}"; then
LEAKED+=("${tok}")
fi
done
if (( ${#LEAKED[@]} )); then
red "[!] response contains raw control tokens: ${LEAKED[*]}"
red " Modelfile likely missing PARAMETER stop directives."
echo "----- model said -----"
echo "${RESP}"
echo "----------------------"
exit 1
fi
green "[+] round-trip OK"
echo "----- model said -----"
echo "${RESP}"
echo "----------------------"
# 6. Tool-call round-trip (opt-in via TOOLS_TEST=1)
#
# Capability advertisement (step 3) only checks the TEMPLATE references
# .Tools / .ToolCalls. It does NOT check the model actually emits a
# parseable tool call. A regression in the prompt scaffolding (e.g. the
# system-prompt instructions inside the TEMPLATE going stale) can leave
# capabilities reported correctly but tool calls failing — the assistant
# prose-describes the call instead of emitting <tool_call>{...}</tool_call>.
# This block sends a tools-array request, parses .message.tool_calls, and
# asserts the shape matches.
if [[ "${TOOLS_TEST:-0}" == "1" ]]; then
blue "[*] tool-call round-trip..."
TOOL_RESP="$(curl -fsS "${HOST}/api/chat" \
-H 'Content-Type: application/json' \
-d "$(jq -n --arg m "${MODEL}" '{
model: $m,
messages: [{role:"user", content:"Call get_weather for Tokyo. Respond ONLY with the tool call."}],
tools: [{
type: "function",
function: {
name: "get_weather",
description: "Get the weather for a city",
parameters: {
type: "object",
properties: {city: {type: "string"}},
required: ["city"]
}
}
}],
stream: false,
options: {num_predict: 1024, temperature: 0.3}
}')")"
TC_COUNT="$(jq -r '.message.tool_calls // [] | length' <<<"${TOOL_RESP}")"
if [[ "${TC_COUNT}" -lt 1 ]]; then
red "[!] model did not emit a tool call"
echo "----- response -----"
echo "${TOOL_RESP}" | jq .
echo "--------------------"
exit 1
fi
TC_NAME="$(jq -r '.message.tool_calls[0].function.name // empty' <<<"${TOOL_RESP}")"
TC_CITY="$(jq -r '.message.tool_calls[0].function.arguments.city // empty' <<<"${TOOL_RESP}")"
if [[ "${TC_NAME}" != "get_weather" ]]; then
red "[!] unexpected tool name: '${TC_NAME}' (wanted 'get_weather')"
exit 1
fi
if [[ "${TC_CITY,,}" != "tokyo" ]]; then
red "[!] unexpected city argument: '${TC_CITY}' (wanted 'Tokyo' case-insensitive)"
exit 1
fi
green "[+] tool-call round-trip OK (name=${TC_NAME} city=${TC_CITY})"
fi
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