Text Generation
GGUF
English
llama.cpp
quantized
qwen3.5
reasoning
uncensored
long-context
1M-context
function-calling
multimodal
vision
cybersecurity
biomedical
agentic
conversational
Instructions to use alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use alexukraine/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="alexukraine/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 alexukraine/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 alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf alexukraine/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 alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf alexukraine/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 alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf alexukraine/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 alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use alexukraine/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 "alexukraine/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": "alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
- Ollama
How to use alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF with Ollama:
ollama run hf.co/alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
- Unsloth Studio
How to use alexukraine/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 alexukraine/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 alexukraine/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 alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF to start chatting
- Pi
How to use alexukraine/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 alexukraine/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": "alexukraine/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 alexukraine/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 alexukraine/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 alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF with Docker Model Runner:
docker model run hf.co/alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF:Q4_K_M
- Lemonade
How to use alexukraine/Qwythos-9B-Claude-Mythos-5-1M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull alexukraine/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
File size: 3,721 Bytes
6ee9cef | 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 | # Qwythos GGUF v2 release verification
Release verification was performed on 2026-06-22 with llama.cpp `d0f9d2e5ac5d4f51763755958b8f353fed01aaa2` and an NVIDIA RTX 5090.
## Artifact manifest
| Artifact | Bytes | SHA-256 |
|---|---:|---|
| `Qwythos-9B-Claude-Mythos-5-1M-BF16.gguf` | 17,920,697,344 | `c49e2e419c0fc9f7cca0a7e0699fa3075fa1ec2913bda6f6841194b19cf4ce29` |
| `Qwythos-9B-Claude-Mythos-5-1M-Q4_K_M.gguf` | 5,629,109,248 | `0de41ff56ab4eff26764437b276f0bd5d20f44232add34e14c4f593fa1aeb08f` |
| `Qwythos-9B-Claude-Mythos-5-1M-Q5_K_M.gguf` | 6,467,970,048 | `51dee6e6ddaeb08a06bee258ac5c58f4186f55d156d6403c2db67cf2f3226280` |
| `Qwythos-9B-Claude-Mythos-5-1M-Q6_K.gguf` | 7,359,259,648 | `05f2420a669a83ae239d1328c6e83d9fc19e8a4b8dab5d0673cab28cc161dfc5` |
| `Qwythos-9B-Claude-Mythos-5-1M-Q8_0.gguf` | 9,527,501,824 | `ecf51ef3904694e04a1967a06fe7539619c34e6e81a721fc5f5415827081d8f6` |
| `Qwythos-9B-Claude-Mythos-5-1M-MTP-BF16.gguf` | 18,407,321,536 | `c704996e63c3e31a1e7040278b1204aabf8a6dad0b07f3c8da7b429d2ed30b31` |
| `Qwythos-9B-Claude-Mythos-5-1M-MTP-Q4_K_M.gguf` | 5,887,668,160 | `24ee22e0f5d9f0d3d615809607f365c728d9b0c3f3fb6eb19d8bd83a1c2933d8` |
| `Qwythos-9B-Claude-Mythos-5-1M-MTP-Q5_K_M.gguf` | 6,726,528,960 | `c4e3c47b17a93cc1566abcee4a0c9a4413b172a6f8be7f2a8824495907e9e893` |
| `Qwythos-9B-Claude-Mythos-5-1M-MTP-Q6_K.gguf` | 7,617,818,560 | `eae2adb4b451f673218fba96ebdc024fb95c4026ef35a56837dc9af47f99ca39` |
| `Qwythos-9B-Claude-Mythos-5-1M-MTP-Q8_0.gguf` | 9,786,060,736 | `34eada291b4f4d1b8592f53d1739c7a2e029011b5bc2624b979b0a544562a5e9` |
| `mmproj-Qwythos-9B-Claude-Mythos-5-1M-F16.gguf` | 918,165,472 | `f977efc337a2ac2ba183eea0c73e25b75fc240d56c05ed4d9b56ab451f64c82c` |
`shasum -a 256 -c SHA256SUMS` passed for all 11 GGUF artifacts after conversion and testing.
## Structural checks
All ten text GGUFs passed these shared assertions:
- GGUF v3 and `qwen35` architecture;
- 1,048,576-token declared context;
- Qwen3.6-derived chat template markers present;
- fixed Qwythos/Empero AI identity instruction present.
The five normal v2 replacement files additionally verified as 32-block trunk-only GGUFs with no `nextn_predict_layers` key and no MTP tensors.
The five `-MTP-` v2 variants additionally verified as 33-block GGUFs with `qwen35.nextn_predict_layers = 1`, all 15 restored MTP tensors present, and MTP matrices retained at Q8_0 in every quantized variant.
The MTP tensors come from pinned Qwen3.5-9B base commit `c202236235762e1c871ad0ccb60c8ee5ba337b9a`. The source fine-tune declares one MTP layer but does not publish its MTP tensors. Only the draft head is restored; the Qwythos trunk and output layers remain unchanged.
## Live tests
| Variant / mode | Identity | Generation | Tools | Other |
|---|---:|---:|---:|---|
| BF16 MTP | Pass | Pass | — | — |
| Q4_K_M MTP | Pass | Pass | Pass | Tool result round trip passed |
| Q5_K_M MTP | Pass | Pass | — | — |
| Q6_K MTP | Pass | Pass | — | — |
| Q8_0 MTP | Pass | Pass | Pass | Tool result round trip passed; no malformed output |
| Q4_K_M MTP + draft speculation | Pass | Pass | Pass | 76/150 draft tokens accepted (50.7%) |
| Q4_K_M MTP + 1M context | Pass | Pass | — | Server loaded `n_ctx = 1048576` |
| Q4_K_M MTP + F16 mmproj | Pass | Pass | — | Correctly identified a generated red-square image |
The normal v2 replacement files were rebuilt from the same fixed tokenizer/template source with `--no-mtp` and structurally verified after quantization. The 1M-context check verifies full context allocation and successful generation at that setting; it is not a million-token retrieval benchmark.
Machine-readable responses are in `reports/`; relevant llama.cpp logs are in `logs/`.
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