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 generousmax_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_count33->32,nextn_predict_layers1->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.
License
Apache 2.0 (inherited from Qwen3.5-9B base)
Acknowledgements
- Developed by Empero AI
- Base model: Qwen3.5-9B (Alibaba Qwen team)
- Training: TRL + Transformers
- Linear-attention kernels: flash-linear-attention, causal_conv1d