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 β Ollama wrapper around Qwen 3.6 27B (dense) | |
| # | |
| # Text + tool calling. Vision via Ollama is currently broken for this | |
| # architecture (ollama/ollama#15898 β the qwen35 arch entries are in | |
| # Ollama's Go text engine but missing from the C++ llama.cpp fallback | |
| # Ollama uses when an mmproj is attached). Use llama.cpp directly for | |
| # image input, or wait for the fix. See the Vision section in README.md. | |
| # | |
| # This repo bundles a single GGUF: Thanatos-27B.Q4_K_M.gguf (~17 GB), | |
| # stamped `general.architecture: 'qwen35'` β the upstream-canonical | |
| # arch entry every released llama.cpp / Ollama loads under for the | |
| # Qwen 3.5 / 3.6 hybrid SSM + attention family. `ollama create | |
| # thanatos-27b -f Modelfile && ollama run thanatos-27b` loads it | |
| # directly. See README "Architecture" for the full stamp history | |
| # (eight flips between qwen35 and qwen36, settled on qwen35 at | |
| # `e03e10e` after the 4th qwen36 round trip had its friction | |
| # re-tested in a fresh next-day session). | |
| # | |
| # For other quants (Q3_K_S, Q5_K_M, Q6_K, etc.), `make build QUANT=Q3_K_S` | |
| # downloads the chosen quant from unsloth/Qwen3.6-27B-GGUF and patches | |
| # FROM in a temp Modelfile copy. The Q3_K_S used to ship in this repo; | |
| # it was removed so HF's Ollama bridge picks Q4_K_M as the default | |
| # `:latest` tag instead of Q3_K_S (alphabetically-first heuristic). | |
| # | |
| # Other GGUF sources (use with `make build GGUF_PATH=...`): | |
| # https://huggingface.co/unsloth/Qwen3.6-27B-GGUF | |
| # https://huggingface.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF | |
| FROM ./Thanatos-27B.Q4_K_M.gguf | |
| # Chat template β Qwen 3.6 ChatML in Ollama Go-template form, with the | |
| # tool-calling blocks Ollama's capability detector looks for. Without a | |
| # TEMPLATE that references .Tools and .ToolCalls, /api/chat and | |
| # /v1/chat/completions reject any request carrying a `tools` array with | |
| # `<model> does not support tools`. Same template as the 35B sibling β | |
| # both share the Qwen 3.6 chat format. | |
| TEMPLATE """{{- $lastUserIdx := -1 -}} | |
| {{- range $idx, $msg := .Messages -}} | |
| {{- if eq $msg.Role "user" }}{{ $lastUserIdx = $idx }}{{ end -}} | |
| {{- end }} | |
| {{- if or .System .Tools }}<|im_start|>system | |
| {{ if .System }}{{ .System }} | |
| {{ end }} | |
| {{- if .Tools }}# Tools | |
| You may call one or more functions to assist with the user query. | |
| You are provided with function signatures within <tools></tools> XML tags: | |
| <tools> | |
| {{- range .Tools }} | |
| {"type": "function", "function": {{ .Function }}} | |
| {{- end }} | |
| </tools> | |
| For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags: | |
| <tool_call> | |
| {"name": <function-name>, "arguments": <args-json-object>} | |
| </tool_call> | |
| {{- end -}}<|im_end|> | |
| {{ end }} | |
| {{- range $i, $_ := .Messages }} | |
| {{- $last := eq (len (slice $.Messages $i)) 1 -}} | |
| {{- if eq .Role "user" }}<|im_start|>user | |
| {{ .Content }}<|im_end|> | |
| {{ else if eq .Role "assistant" }}<|im_start|>assistant | |
| {{ if (and $.IsThinkSet (and .Thinking (or $last (gt $i $lastUserIdx)))) -}} | |
| <think>{{ .Thinking }}</think> | |
| {{ end -}} | |
| {{ if .Content }}{{ .Content }}{{ end }} | |
| {{- if .ToolCalls }} | |
| {{- range .ToolCalls }} | |
| <tool_call> | |
| {"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}} | |
| </tool_call> | |
| {{- end }} | |
| {{- end }}{{ if not $last }}<|im_end|> | |
| {{ end }} | |
| {{- else if eq .Role "tool" }}<|im_start|>user | |
| <tool_response> | |
| {{ .Content }} | |
| </tool_response><|im_end|> | |
| {{ end }} | |
| {{- if and (ne .Role "assistant") $last }}<|im_start|>assistant | |
| <think> | |
| {{ end }} | |
| {{- end }}""" | |
| # Sampling tuned for reasoning + general use. See README "Recommended sampling" | |
| # for creative/RP alternatives. | |
| PARAMETER temperature 0.6 | |
| PARAMETER top_p 0.95 | |
| PARAMETER top_k 20 | |
| PARAMETER repeat_penalty 1.05 | |
| PARAMETER num_ctx 16384 | |
| # Stop tokens. Without these, Ollama only honors <|im_end|> from the GGUF | |
| # metadata; the model occasionally emits <|endoftext|> instead and Ollama | |
| # keeps generating past it (synthesising a fake new user turn). Listing | |
| # both β plus <|im_start|> as a belt-and-braces guard against the same | |
| # loop β keeps responses cleanly terminated. | |
| PARAMETER stop "<|im_end|>" | |
| PARAMETER stop "<|endoftext|>" | |
| PARAMETER stop "<|im_start|>" | |
| SYSTEM """You are Thanatos, a precise and capable assistant for reasoning, writing, coding, and long-form dialogue. | |
| Behavior rules: | |
| - Answer the user's actual request directly. | |
| - Be accurate, complete, and structured. | |
| - Think before answering, but do not get stuck in repetitive loops or meta-commentary. | |
| - If the request is ambiguous or incomplete, state what is missing and make the smallest reasonable assumption needed to continue. | |
| - If the user wants creative writing, preserve tone, continuity, and character consistency. | |
| - If the user wants analysis or technical help, prefer concrete steps, examples, and decisions over fluff. | |
| - Finish with a usable answer, not just planning.""" | |
| # Hardware notes | |
| # -------------- | |
| # Qwen 3.6 27B is *dense* β every parameter participates in every forward pass. | |
| # Q4_K_M GGUF is ~17 GB. Practical footprint: | |
| # weights mmap ~17 GB | |
| # compute graph alloc ~12 GB (smaller than 35B-A3B because dense β MoE) | |
| # KV cache @ 16K ctx ~1 GB (with OLLAMA_KV_CACHE_TYPE=q8_0) | |
| # total minimum ~30 GB | |
| # | |
| # Working configurations: | |
| # β RTX 3090 / 4090 24 GB β full Q4 offload, ~25-40 tok/s | |
| # β RTX 5090 32 GB β full offload at Q5/Q6 quant | |
| # β Mac Studio M2/M3 32 GB+ unified β ~15-25 tok/s | |
| # β Linux box with 32 GB+ RAM (CPU-only) β ~1-3 tok/s | |
| # β 32 GB unified-memory laptops β borderline at Q4, try | |
| # `make build QUANT=Q3_K_S` | |
| # (~12 GB) and trim num_ctx | |
| # | |
| # Measured data points (ASUS ROG Flow Z13 GZ302EA, Ryzen AI Max+ 395 + | |
| # Radeon 8060S iGPU, 32 GB unified, gfx1151, OLLAMA_FLASH_ATTENTION=1, | |
| # OLLAMA_KV_CACHE_TYPE=q8_0, num_ctx 16384, 3-prompt mix): | |
| # Vulkan (OLLAMA_VULKAN=1): | |
| # Q3_K_S β 12.31 tok/s aggregate (run 1) | |
| # (6182 tokens / 501.9 s; 12.67 / 12.55 / 12.25 short/medium/long) | |
| # Q3_K_S β 11.70 tok/s aggregate (run 2, 2026-05-19 evening) | |
| # (8009 tokens / 684.0 s; 12.23 / 12.12 / 11.66 short/medium/long) | |
| # Second run measured against a `thanatos-27b:latest` (pre-rename) | |
| # built via `make build QUANT=Q3_K_S` against the then-current | |
| # unsloth/Qwen3.6-27B-GGUF source. Aggregate is 4.9% below | |
| # run 1 (within the Β±20% noise band) β slightly longer | |
| # per-prompt outputs this run (8009 vs 6182 tokens) likely | |
| # contribute the difference, plus late-in-session thermal | |
| # pressure on the Strix Halo iGPU. | |
| # (Heretic v2 base is not benched here yet; rebundle pending.) | |
| # Q4_K_M β 9.31 tok/s aggregate (run 1) | |
| # (5356 tokens / 574.9 s; 9.48 / 9.43 / 9.28 short/medium/long) | |
| # Q4_K_M β 9.19 tok/s aggregate (run 2, 2026-05-19 afternoon) | |
| # (6210 tokens / 675.6 s; 9.40 / 9.29 / 9.16 short/medium/long) | |
| # Second run measured against the qwen36-stamped HF-bridge tag | |
| # after `make heal-hf` rebadged it to qwen35 in store β confirms | |
| # the in-place heal produces a model with the same performance | |
| # profile as `make load-bundle`. Aggregate is 1.3% below run 1 | |
| # (within the Β±20% noise band the README hardware section | |
| # warns about). | |
| # Q4_K_M β 9.32 tok/s aggregate (run 3, 2026-05-19 evening) | |
| # (4592 tokens / 492.7 s; 9.49 / 9.44 / 9.28 short/medium/long) | |
| # Third run, also against a heal-hf-rebadged qwen36-stamped | |
| # HF-bridge tag β this time the 3rd-round-trip bundle from | |
| # commit 973d7ef. Aggregate is within 0.1% of run 1's 9.31, | |
| # confirming the latest qwen36 -> qwen35 heal yields the same | |
| # performance profile as the prior two runs (no regression | |
| # from the third stamp flip). | |
| # ROCm (older snapshot, kept for backend comparison): | |
| # Q3_K_S β 10.14 tok/s aggregate | |
| # (8080 tokens / 796.5 s; 10.37 / 10.31 / 10.11 short/medium/long) | |