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
| # Thanatos-27B — tok/s benchmark via Ollama. | |
| # | |
| # Reads timing from Ollama's /api/chat response metadata (eval_count and | |
| # eval_duration are authoritative — no client-side stopwatch noise) and | |
| # averages over a handful of prompts that vary in output length so the | |
| # number generalises a bit beyond a single shape. | |
| # | |
| # Usage: | |
| # ./scripts/bench.sh # uses MODEL=thanatos-27b | |
| # MODEL=thanatos-27b ./scripts/bench.sh | |
| # HOST=http://localhost:11434 ./scripts/bench.sh | |
| # | |
| # Requires: curl, jq, a running Ollama daemon with the model created. | |
| set -euo pipefail | |
| MODEL="${MODEL:-thanatos-27b}" | |
| HOST="${HOST:-http://localhost:11434}" | |
| red() { printf "\033[31m%s\033[0m\n" "$*" >&2; } | |
| green() { printf "\033[32m%s\033[0m\n" "$*"; } | |
| blue() { printf "\033[34m%s\033[0m\n" "$*"; } | |
| # tok_per_s <eval_count> <eval_duration_ns> -> "X.YZ" (2 dp, floor). | |
| tok_per_s() { | |
| jq -n --argjson c "$1" --argjson n "$2" '($c / ($n / 1e9)) | . * 100 | floor / 100' | |
| } | |
| for dep in curl jq; do | |
| if ! command -v "$dep" >/dev/null 2>&1; then | |
| red "[!] missing dependency: $dep"; exit 1 | |
| fi | |
| done | |
| # Single /api/tags fetch covers both checks below. | |
| if ! TAGS="$(curl -fsS "${HOST}/api/tags")"; then | |
| red "[!] Ollama not reachable at ${HOST}" | |
| exit 1 | |
| fi | |
| # Match case-insensitively: Ollama 0.24's API tag list preserves the | |
| # case of whatever `general.name` it inferred at create time, which | |
| # can differ from the case the user passed to `ollama create` / typed | |
| # into `ollama run`. Both `ollama show <lower>` and `ollama show | |
| # <Mixed>` resolve to the same model, so the bench check should too. | |
| if ! jq -e --arg m "${MODEL}" '.models[] | select(.name | ascii_downcase | startswith($m | ascii_downcase))' >/dev/null <<<"${TAGS}"; then | |
| red "[!] Model '${MODEL}' not found. Build it first: ./scripts/build.sh" | |
| exit 1 | |
| fi | |
| # Mix of short / medium / long output lengths — single shape would skew | |
| # the average toward whatever the model decides to do for that prompt. | |
| PROMPTS=( | |
| "Reply with only the word OK." | |
| "Explain the time complexity of mergesort in one short paragraph." | |
| "Write a 120-word explanation of what a Bloom filter is and when to use it." | |
| ) | |
| blue "[*] host: ${HOST}" | |
| blue "[*] model: ${MODEL}" | |
| blue "[*] prompts: ${#PROMPTS[@]}" | |
| echo | |
| # Warmup — first call pays the model-load cost; we don't want that in | |
| # the average. Result is discarded. | |
| blue "[*] warmup..." | |
| curl -fsS "${HOST}/api/chat" \ | |
| -H 'Content-Type: application/json' \ | |
| -d "$(jq -n --arg m "${MODEL}" '{ | |
| model: $m, | |
| messages: [{role:"user", content:"warmup"}], | |
| stream: false | |
| }')" >/dev/null | |
| TOTAL_TOKENS=0 | |
| TOTAL_NS=0 | |
| printf "%-4s %8s %12s %8s\n" "#" "tokens" "eval_ms" "tok/s" | |
| printf "%-4s %8s %12s %8s\n" "----" "--------" "------------" "--------" | |
| for i in "${!PROMPTS[@]}"; do | |
| prompt="${PROMPTS[$i]}" | |
| 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 | |
| }')")" | |
| eval_count="$(jq -r '.eval_count // 0' <<<"$resp")" | |
| eval_ns="$(jq -r '.eval_duration // 0' <<<"$resp")" | |
| if [[ "$eval_count" -eq 0 || "$eval_ns" -eq 0 ]]; then | |
| red "[!] prompt $((i+1)) returned no timing data" | |
| echo "$resp" | jq -r '.message.content // .' | head -3 | |
| exit 1 | |
| fi | |
| eval_ms=$(( eval_ns / 1000000 )) | |
| toks_per_s="$(tok_per_s "$eval_count" "$eval_ns")" | |
| printf "%-4s %8s %12s %8s\n" "$((i+1))" "$eval_count" "$eval_ms" "$toks_per_s" | |
| TOTAL_TOKENS=$(( TOTAL_TOKENS + eval_count )) | |
| TOTAL_NS=$(( TOTAL_NS + eval_ns )) | |
| done | |
| echo | |
| avg="$(tok_per_s "$TOTAL_TOKENS" "$TOTAL_NS")" | |
| green "[+] aggregate: ${TOTAL_TOKENS} tokens / $(( TOTAL_NS / 1000000 )) ms = ${avg} tok/s" | |