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: 3,881 Bytes
d344201 7197abd d344201 7197abd d344201 7197abd d344201 32d9533 d344201 32d9533 d344201 32d9533 d344201 124302d d344201 32d9533 d344201 32d9533 d344201 | 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 | #!/usr/bin/env bash
# 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"
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