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": "What is the capital of France?"
		}
	]
)

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": "What is the capital of France?"
			}
		]
	}'

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

🚨 v2 released — please redownload the GGUFs

The v2 GGUFs replace the original normal filenames and add explicit -MTP- variants. If you downloaded this repo before v2, please redownload your GGUF.

Fixes in v2:

tokenizer metadata normalized for Qwen3.5 GGUF runtimes;
embedded chat template updated for reliable tool/function calling and OpenCode-style agent loops;
Qwythos/Empero identity prompt embedded in the template;
MTP-enabled variants added as Qwythos-9B-Claude-Mythos-5-1M-MTP-*.gguf;
Q4/Q8 tool-calling, MTP draft speculation, 1M-context allocation, and vision projector smoke-tested with current llama.cpp.

Use the normal files for maximum runtime compatibility. Use the -MTP- files when you want llama.cpp MTP draft speculation.

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 v2 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 v2, best compatibility
`Qwythos-9B-Claude-Mythos-5-1M-Q5_K_M.gguf`	Q5_K_M	6.02 GiB / 6.47 GB	fixed v2, balanced quality / size
`Qwythos-9B-Claude-Mythos-5-1M-Q6_K.gguf`	Q6_K	6.85 GiB / 7.36 GB	fixed v2, high quality
`Qwythos-9B-Claude-Mythos-5-1M-Q8_0.gguf`	Q8_0	8.87 GiB / 9.53 GB	fixed v2, near-lossless
`Qwythos-9B-Claude-Mythos-5-1M-BF16.gguf`	BF16	16.69 GiB / 17.92 GB	fixed v2, 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 — v2 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 v2 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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6-bit

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

(10)

this model

Quantizations

2 models

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

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 4 days ago • 3