Instructions to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF",
	filename="mmproj-qwythos-9b-f16.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 FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-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 FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

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 FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

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 FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

Use Docker

docker model run hf.co/FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

LM Studio
Jan

vLLM

How to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-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": "FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-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/FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

Ollama
How to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16
```

Unsloth Studio

How to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-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 FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-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 FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF to start chatting

How to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

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": "FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-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 FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

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 FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16
```

Lemonade

How to use FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF:F16

Run and chat with the model

lemonade run user.Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF-F16

List all available models

lemonade list

Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF

MXFP4 GGUF quantization of empero-ai/Qwythos-9B-Claude-Mythos-5-1M -- a full-parameter reasoning model built on a deeply uncensored Qwen3.5-9B base, post-trained on 500M+ tokens of Claude Mythos and Claude Fable traces with chain-of-thought generated in-house by Empero AI's internal tool rethink.

What makes Qwythos special

1M token context -- YaRN rope-scaling enabled by default for a full 1,048,576-token context window. One of the longest context windows in any 9B open-weight model. Suitable for whole-codebase reasoning, multi-document research, and long agentic trajectories.
Massive benchmark gains over base -- +34 pts MMLU, +30 pts gsm8k-strict, +19 pts gsm8k-flex under matched evaluation.
Native function calling -- OpenAI/Qwen3.5-style tool use out of the box. Pass tools=[...] and the model emits valid <tool_call> blocks. Self-corrects with Python executor and web search (7/7 test prompts succeeded).
Uncensored by design -- Engages substantively with technically demanding questions across cybersecurity, red-teaming, biology, pharmacology, and clinical medicine where over-aligned models refuse or hedge.
Reasoning model -- Every answer opens with a <think> block before the final response. Use generous max_new_tokens (16,384 recommended).

Domain strengths

Cybersecurity -- SQL injection mitigations, TLS handshake structure, EDR/process-injection detection, MITRE ATT&CK ransomware kill chains, hashcat modes, CVE analysis.
Biomedical -- CRISPR-Cas9 mechanisms, mRNA vaccines, SARS-CoV-2 spike protein, antibiotic resistance, receptor pharmacology, organophosphate AChE inhibition.
Clinical medicine -- ACS chest-pain differential, type-2 diabetes pathophysiology, sepsis recognition (qSOFA), therapeutic-window reasoning.
Math -- 86% gsm8k, multi-step word problems, competition math. Verified by Python executor when invoked.

About MXFP4

MXFP4 (Microscaling FP4) is an open standard (OCP) 4-bit floating point format (E2M1) supported by NVIDIA, AMD, Microsoft, and Meta.

Works on any GPU with MX support
Open standard -- not vendor locked
Block-scaled format with shared scale factors

When to use MXFP4 vs other formats:

MXFP4 -- Open standard, broad hardware support
NVFP4 -- NVIDIA Blackwell native, best performance on RTX 50-series
Q4_K_M -- Best for pre-Blackwell GPUs and CPU inference

Files

File	Type	Size	Description
`qwythos-9b-mxfp4.gguf`	MXFP4	~4.8 GB	Text model (4.52 BPW)
`mmproj-qwythos-9b-f16.gguf`	F16	~918 MB	Vision encoder (SigLIP ViT, 27 layers)

Quantization Details

Property	Value
Format	MXFP4 (E2M1)
Bits Per Weight	4.52 BPW
Source Model	empero-ai/Qwythos-9B-Claude-Mythos-5-1M
Architecture	Qwen3_5ForConditionalGeneration
Parameters	9.4B (BF16 source)
Layers	32 (hybrid Gated DeltaNet + full attention)
Hidden Size	4096
Context Length	1,048,576 (1M, YaRN)
Vision	Yes (SigLIP ViT, frozen from base)
Thinking	Enabled by default (opt-out via `enable_thinking=false`)
Training	500M+ tokens, Claude Mythos/Fable traces, full SFT

Usage

llama.cpp CLI

# Text only
./llama-cli -m qwythos-9b-mxfp4.gguf -p "Hello" -n 100

# With vision (requires mmproj)
./llama-server -m qwythos-9b-mxfp4.gguf \
  --mmproj mmproj-qwythos-9b-f16.gguf \
  --host 0.0.0.0 --port 8080 -ngl 99

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="qwythos-9b-mxfp4.gguf",
    n_gpu_layers=-1,
    chat_format="chatml"
)

output = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Explain how organophosphate nerve agents inhibit acetylcholinesterase."}],
    max_tokens=4096
)
print(output["choices"][0]["message"]["content"])

huggingface-hub

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF",
    filename="qwythos-9b-mxfp4.gguf"
)
mmproj_path = hf_hub_download(
    repo_id="FreedomAISVR/Qwythos-9B-Claude-Mythos-5-1M-MXFP4-GGUF",
    filename="mmproj-qwythos-9b-f16.gguf"
)

Sampling recommendations

Qwythos was trained as a reasoning model. Use these settings for best results:

temperature=0.6
top_p=0.95
top_k=20
repetition_penalty=1.05
max_new_tokens=16384

Greedy decoding or very-low-temperature (T<=0.3) can cause repetition loops on long generations.

Quantization Pipeline

Download source: empero-ai/Qwythos-9B-Claude-Mythos-5-1M
Convert to F16 GGUF: convert_hf_to_gguf.py --outtype f16
Extract mmproj: convert_hf_to_gguf.py --mmproj --outtype f16
Quantize text: llama-quantize input-f16.gguf output-mxfp4.gguf MXFP4
Patch GGUF metadata: block_count 33->32, nextn_predict_layers 1->0

Hardware Requirements

Component	Requirement
GPU	Any with MX support, or CPU fallback
VRAM	~6 GB minimum
RAM	~16 GB recommended
Storage	~6 GB

Limitations

Reasoning model -- Every answer opens with <think> block. Allow generous token budget.
Text-only fine-tune -- Vision tower was frozen; vision behavior is inherited from base and was not tuned.
Uncensored -- Add application-level safety layer for end-user deployments.
Verify specifics -- Like all 9B models, can over-commit to specific identifiers (CVEs, drug dosages). Pair with tools for accuracy-critical deployments.