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: 4,607 Bytes
25d5454 7197abd 25d5454 7197abd 25d5454 7197abd 25d5454 | 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 | #!/usr/bin/env python3
"""
Thanatos-27B — verify the README "Architecture" forward-pass bullets
against the actual GGUF metadata.
Reads either the qwen35- or qwen36-stamped bundle (or any GGUF that
declares one of those `general.architecture` values), prints each
README claim alongside the metadata key it derives from, and exits
non-zero if any value mismatches the expected README claim. Useful
as a manual audit after the bundle is re-stamped or after upstream
re-conversion.
Usage:
python3 scripts/verify_arch.py # default bundle
python3 scripts/verify_arch.py Thanatos-27B.Q4_K_M.gguf
python3 scripts/verify_arch.py /path/to/some-other.gguf
Exit code 0 = all claims verify, 1 = at least one mismatch.
Note: this does NOT verify the 27B parameter count directly (no such
KV in the GGUF) — that comes from llama.cpp's `case 64: LLM_TYPE_27B`
branch in `src/models/qwen35.cpp`, not from the file itself.
"""
from __future__ import annotations
import sys
from pathlib import Path
from gguf import GGUFReader
EXPECTED = {
"block_count": (64, "64 transformer layers"),
"context_length": (262144, "262 144 native context"),
"embedding_length": (5120, "Hidden size 5120"),
"feed_forward_length": (17408, "FFN intermediate 17408"),
"attention.head_count": (24, "Gated Attention: 24 Q-heads"),
"attention.head_count_kv": (4, "Gated Attention: 4 KV-heads (GQA)"),
"attention.key_length": (256, "Gated Attention: head_dim 256 (key)"),
"attention.value_length": (256, "Gated Attention: head_dim 256 (value)"),
"rope.dimension_count": (64, "Partial RoPE: 64 of 256 dims (factor 0.25)"),
"full_attention_interval": (4, "Hybrid stack: every 4th layer is full attention (16 cycles)"),
"ssm.state_size": (128, "Gated DeltaNet: head_dim 128"),
"ssm.time_step_rank": (48, "Gated DeltaNet: 48 V-heads"),
"ssm.group_count": (16, "Gated DeltaNet: 16 QK-heads"),
}
EXPECTED_VOCAB = 248320
EXPECTED_ARCHS = {"qwen35", "qwen36"}
def read_scalar(reader: GGUFReader, key: str):
f = reader.fields.get(key)
if f is None:
return None
arr = f.parts[f.data[0]]
val = arr.tolist() if hasattr(arr, "tolist") else arr
if isinstance(val, list) and len(val) == 1:
return val[0]
return val
def read_arch(reader: GGUFReader) -> str:
f = reader.fields["general.architecture"]
return bytes(f.parts[f.data[0]]).decode()
def main() -> int:
if len(sys.argv) > 2:
print(f"usage: {sys.argv[0]} [path/to/Thanatos-27B.Q4_K_M.gguf]", file=sys.stderr)
return 2
root = Path(__file__).resolve().parent.parent
default_paths = [
root / "Thanatos-27B.Q4_K_M.qwen35.gguf",
root / "Thanatos-27B.Q4_K_M.qwen36.gguf",
root / "Thanatos-27B.Q4_K_M.gguf",
]
if len(sys.argv) == 2:
path = Path(sys.argv[1])
else:
path = next((p for p in default_paths if p.exists() and p.stat().st_size > 1024), None)
if path is None:
print("[!] no Thanatos-27B GGUF found in repo root; pass a path explicitly", file=sys.stderr)
return 2
print(f"[*] reading: {path}")
reader = GGUFReader(str(path), "r")
arch = read_arch(reader)
if arch not in EXPECTED_ARCHS:
print(f"[!] unexpected general.architecture: {arch!r} (expected one of {EXPECTED_ARCHS})", file=sys.stderr)
return 1
print(f"[*] general.architecture: {arch}")
print()
mismatches = 0
fmt = " {marker} {claim:55s} {key:35s} = {actual}"
for suffix, (expected, claim) in EXPECTED.items():
key = f"{arch}.{suffix}"
actual = read_scalar(reader, key)
ok = actual == expected
marker = "[ ok ]" if ok else "[FAIL]"
print(fmt.format(marker=marker, claim=claim, key=key, actual=actual))
if not ok:
mismatches += 1
# Vocab count comes from the tokenizer tokens array length, not a scalar KV.
f = reader.fields.get("tokenizer.ggml.tokens")
vocab_actual = len(f.data) if f is not None else None
ok = vocab_actual == EXPECTED_VOCAB
marker = "[ ok ]" if ok else "[FAIL]"
print(fmt.format(marker=marker, claim=f"Vocab {EXPECTED_VOCAB}", key="tokenizer.ggml.tokens (length)", actual=vocab_actual))
if not ok:
mismatches += 1
print()
if mismatches:
print(f"[!] {mismatches} mismatch(es) — README Architecture claims disagree with GGUF metadata.")
return 1
print("[+] all Architecture claims verify against GGUF metadata.")
return 0
if __name__ == "__main__":
sys.exit(main())
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