Instructions to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF",
	filename="Qwythos-9B-Claude-Mythos-5-1M-BF16.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 Settings

llama.cpp

How to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF: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 empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF: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 empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF",
		"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/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M

Ollama
How to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF with Ollama:
```
ollama run hf.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
```

Unsloth Studio

How to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF 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 empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF 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 empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF to start chatting

How to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF: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": "empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF: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 empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF with Docker Model Runner:
```
docker model run hf.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
```

Lemonade

How to use empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwythos-9B-Claude-Mythos-5-1M-GGUF-Q4_K_M

List all available models

lemonade list

Qwythos-9B

🚨 v3 released — please redownload the GGUFs

Hotfix for the chat template. If you downloaded this repo before v3, please redownload your GGUF.

Fixes in v3:

embedded chat template updated for preserved reasoning and adaptive thinking;
fixes looping during long generation traces;
fixes agentic use in harnesses like OpenCode, Abacus, Hermes, and Claude Code;

Qwythos-9B-Claude-Mythos-5-1M-GGUF

Developed by Empero

GGUF quantizations of empero-ai/Qwythos-9B-Claude-Mythos-5-1M for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes.

Qwythos-9B is a full-parameter reasoning model post-trained on over 500 million tokens of high-quality Claude Mythos / Claude Fable traces with chain-of-thought generated in-house by Empero AI's internal rethink tool. It dominates the base Qwen3.5-9B under matched evaluation (+34 pts MMLU, +30 pts gsm8k-strict, +19 pts gsm8k-flex), supports native function calling per the Qwen3.5 spec, and ships with a 1,048,576-token (1M) context window via YaRN rope-scaling enabled by default.

For full training details, evaluation numbers, and capability writeup, see the base model card.

Files

Normal text weights — fixed v3 replacements

File	Quant	Size	Notes
`Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf`	Q4_K_M	5.24 GiB / 5.63 GB	recommended default — fixed v3, best compatibility
`Qwythos-9B-Claude-Mythos-5-1M-Q5_K_M.gguf`	Q5_K_M	6.02 GiB / 6.47 GB	fixed v3, balanced quality / size
`Qwythos-9B-Claude-Mythos-5-1M-Q6_K.gguf`	Q6_K	6.85 GiB / 7.36 GB	fixed v3, high quality
`Qwythos-9B-Claude-Mythos-5-1M-Q8_0.gguf`	Q8_0	8.87 GiB / 9.53 GB	fixed v3, near-lossless
`Qwythos-9B-Claude-Mythos-5-1M-BF16.gguf`	BF16	16.69 GiB / 17.92 GB	fixed v3, full precision conversion base

If you don't know which to pick, Q4_K_M is the right starting point — it's the smallest practical quant with good quality preservation.

MTP-enabled text weights — fixed v3 variants

These include the restored Qwen3.5-compatible MTP head inside the GGUF. Use them with llama.cpp builds that support MTP draft speculation, for example --spec-type draft-mtp.

File	Quant	Size	Notes
`Qwythos-9B-Claude-Mythos-5-1M-MTP-Q4_K_M.gguf`	Q4_K_M + MTP	5.48 GiB / 5.89 GB	recommended MTP default
`Qwythos-9B-Claude-Mythos-5-1M-MTP-Q5_K_M.gguf`	Q5_K_M + MTP	6.26 GiB / 6.73 GB	MTP, balanced quality / size
`Qwythos-9B-Claude-Mythos-5-1M-MTP-Q6_K.gguf`	Q6_K + MTP	7.09 GiB / 7.62 GB	MTP, high quality
`Qwythos-9B-Claude-Mythos-5-1M-MTP-Q8_0.gguf`	Q8_0 + MTP	9.11 GiB / 9.79 GB	MTP, near-lossless
`Qwythos-9B-Claude-Mythos-5-1M-MTP-BF16.gguf`	BF16 + MTP	17.14 GiB / 18.41 GB	MTP, full precision conversion base

Vision projector — for image input

File	Size	Notes
`mmproj-Qwythos-9B-Claude-Mythos-5-1M-F16.gguf`	0.86 GiB / 0.92 GB	CLIP-style vision encoder + projector; required for images, pairs with any normal or MTP quant above

Qwythos inherits its vision tower from the Qwen3.5-9B base model — the vision path was frozen during SFT (training was text-only), so the vision behavior is identical to base Qwen3.5-9B's multimodal capability. The mmproj is interchangeable with any community-built Qwen3.5-9B mmproj-*.gguf.

Quick start

llama.cpp (`llama-cli`)

llama-cli \
  -m Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf \
  -p "Walk through the biochemistry of how organophosphate nerve agents inhibit acetylcholinesterase." \
  -n 8192 \
  --temp 0.6 --top-p 0.95 --top-k 20 --repeat-penalty 1.05 \
  -c 16384

Ollama

ollama run hf.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M

LM Studio / jan / KoboldCpp

Drop any of the .gguf files into your runtime's model directory. Qwythos uses the standard Qwen3.5 chat template; modern GGUF runtimes load it automatically from the file.

llama.cpp with MTP draft speculation

llama-server \
  -m Qwythos-9B-Claude-Mythos-5-1M-MTP-Q4_K_M.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 6 \
  -c 16384 --port 8080

MTP support requires a recent llama.cpp build. If your runtime does not support MTP yet, use the normal fixed v3 files above.

Vision (image input)

Qwythos supports image input out of the box. Download both a text quant and the mmproj-*.gguf file from this repo, then run with llama.cpp's multimodal CLI or server.

llama.cpp (`llama-mtmd-cli`)

llama-mtmd-cli \
  -m Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf \
  --mmproj mmproj-Qwythos-9B-Claude-Mythos-5-1M-F16.gguf \
  --image ./photo.jpg \
  -p "Describe this image in detail." \
  --temp 0.6 --top-p 0.95 --top-k 20 \
  -c 16384

llama.cpp server (OpenAI-compatible API with images)

llama-server \
  -m Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf \
  --mmproj mmproj-Qwythos-9B-Claude-Mythos-5-1M-F16.gguf \
  -c 16384 --port 8080

Then POST to /v1/chat/completions with an image URL or base64 payload — the standard OpenAI vision API shape works.

LM Studio

Load the text quant; LM Studio detects the matching mmproj-*.gguf in the same folder and enables the image-attach button automatically.

What vision unlocks

Since Qwythos inherits its vision tower unchanged from Qwen3.5-9B base, expect Qwen3.5-9B's documented vision capabilities: detailed image description, OCR (printed + handwritten), chart/table reading, UI/document understanding, basic spatial reasoning.

Honest note: the SFT used to produce Qwythos was text-only — we did not fine-tune the vision tower or train on any image-paired data. Image-grounded reasoning therefore inherits the base model's behavior; it has not been independently evaluated as part of this release. If your application is primarily vision-driven, validate on your own use case first.

Sampling recommendations

Qwythos is a reasoning model — every response opens with a <think>...</think> block before the final answer. Use these settings as defaults:

Parameter	Value
`temperature`	0.6
`top_p`	0.95
`top_k`	20
`repeat_penalty`	1.05
`max_new_tokens`	16384 (generous budget for `<think>` + answer)

These match Qwen3.5's official thinking-mode recommendations. Avoid greedy decoding and very-low-temperature sampling (T ≤ 0.3) — both can cause repetition loops on long reasoning generations.

Long context (1M tokens)

The GGUFs ship with YaRN rope-scaling baked in for a 1,048,576-token context window (4× extension over the 262k native).

To use the full 1M window in llama-cli, set -c 1010000 (or any context length up to that). For shorter prompts, lower -c to reduce KV-cache memory — at default settings llama.cpp will autosize.

A single H100/H200-class GPU comfortably handles 256k–512k; the full 1M typically needs tensor-parallel multi-GPU or aggressive KV-cache offload.

Capabilities (from the base model card)

+34 pts MMLU, +30 pts gsm8k-strict, +19 pts gsm8k-flex vs. base Qwen3.5-9B under matched lm-eval-harness evaluation
Native function calling per Qwen3.5's chat-template spec — emits <tool_call><function=NAME><parameter=NAME>VAL</parameter></function></tool_call> blocks ready for any tool-use loop
Self-correcting with tools: in a 7-prompt tool-use harness (Python executor + DuckDuckGo search), Qwythos produced source-cited correct answers on 7/7, including 4/4 closed-book failure-modes from the original review
Uncensored — engages seriously with technically demanding questions across cybersecurity, red-teaming, biology, pharmacology, and clinical medicine
1,048,576-token (1M) context — YaRN rope-scaling enabled by default

For full eval transcripts and per-task numbers, see the base model card's evals/ folder.

Limitations

Reasoning model. Every answer opens with a <think> block; allow generous max_new_tokens and parse/strip <think>...</think> for end users.
Use recommended sampling. Greedy / very-low-temp can cause repetition loops.
Verify specifics in safety-critical contexts. Like all closed-book LLMs in this weight class, Qwythos can over-commit to specific identifiers (CVEs, hashcat modes, drug positions) it isn't certain about. Pair with retrieval or function calling in such deployments — the model uses tools cleanly when offered them.
Uncensored — add your own application-level review/safety layer for end-user-facing deployments where that matters.

Stay in the loop

Support / Donate

If this model helped you, consider supporting the project:

BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x
XMR: 42Dbm5xg5Nq26fdyzfEU7KBnAJfhi7Cvz5J2ex5CzHXkfKuNEJzYCcmJ1GTbgjFZ5MBx72sdG1G9239Cd6rsZfv4QeDkYJY

Provenance & licensing

Weights are released under Apache-2.0, inherited from the Qwen3.5-9B base. Shared for research and experimentation, as-is.

Acknowledgements

Developed and released by Empero
Base model: Qwen3.5-9B (Alibaba Qwen team)
Quantization: llama.cpp (ggml-org)
Vision projector (mmproj): inherited from Qwen3.5-9B (vision tower unchanged); F16 GGUF re-hosted with thanks to Unsloth for the original conversion
HF model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M

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GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

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8-bit

16-bit

Model tree for empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

empero-ai/Qwythos-9B-Claude-Mythos-5-1M

Quantized

(62)

this model

Quantizations

1 model

Spaces using empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF 4

Collection including empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF

Qwen3.5-Claude-Mythos-5

Collection

A collection of our uncensored Claude Mythos fine tunes • 2 items • Updated 12 days ago • 21