Instructions to use reduxdev/OpenMythos-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use reduxdev/OpenMythos-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="reduxdev/OpenMythos-GGUF", filename="OpenMythos-27B-Q4_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use reduxdev/OpenMythos-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 reduxdev/OpenMythos-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf reduxdev/OpenMythos-GGUF:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf reduxdev/OpenMythos-GGUF:Q6_K # Run inference directly in the terminal: llama cli -hf reduxdev/OpenMythos-GGUF:Q6_K
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 reduxdev/OpenMythos-GGUF:Q6_K # Run inference directly in the terminal: ./llama-cli -hf reduxdev/OpenMythos-GGUF:Q6_K
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 reduxdev/OpenMythos-GGUF:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf reduxdev/OpenMythos-GGUF:Q6_K
Use Docker
docker model run hf.co/reduxdev/OpenMythos-GGUF:Q6_K
- LM Studio
- Jan
- Ollama
How to use reduxdev/OpenMythos-GGUF with Ollama:
ollama run hf.co/reduxdev/OpenMythos-GGUF:Q6_K
- Unsloth Studio
How to use reduxdev/OpenMythos-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 reduxdev/OpenMythos-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 reduxdev/OpenMythos-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for reduxdev/OpenMythos-GGUF to start chatting
- Pi
How to use reduxdev/OpenMythos-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reduxdev/OpenMythos-GGUF:Q6_K
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": "reduxdev/OpenMythos-GGUF:Q6_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use reduxdev/OpenMythos-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 reduxdev/OpenMythos-GGUF:Q6_K
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 reduxdev/OpenMythos-GGUF:Q6_K
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use reduxdev/OpenMythos-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reduxdev/OpenMythos-GGUF:Q6_K
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 "reduxdev/OpenMythos-GGUF:Q6_K" \ --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 reduxdev/OpenMythos-GGUF with Docker Model Runner:
docker model run hf.co/reduxdev/OpenMythos-GGUF:Q6_K
- Lemonade
How to use reduxdev/OpenMythos-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull reduxdev/OpenMythos-GGUF:Q6_K
Run and chat with the model
lemonade run user.OpenMythos-GGUF-Q6_K
List all available models
lemonade list
| license: apache-2.0 | |
| tags: | |
| - gguf | |
| - qwen3.5 | |
| - openmythos | |
| - build-small-hackathon | |
| datasets: | |
| - build-small-hackathon/CVE_Vulnerailities_Detailed | |
| - himanshu17HF/ArvixImport-Filtered-Final | |
| base_model: | |
| - build-small-hackathon/OpenMythos | |
| - Qwen/Qwen3.6-27B | |
| # OpenMythos 27B - GGUF | |
| GGUF quantisation of [build-small-hackathon/OpenMythos](https://huggingface.co/build-small-hackathon/OpenMythos), | |
| a fine-tune of [Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B). | |
| Converted with `convert_hf_to_gguf.py --no-mtp` from llama.cpp build 9658. | |
| The fine-tune does not include MTP head weights (dropped during training), so MTP | |
| is not available in this GGUF. | |
| ## Available Quantisations | |
| | File | Size | Type | | |
| |------|------|------| | |
| | OpenMythos-27B-F16.gguf | 53.8 GB | F16 | | |
| | OpenMythos-27B-Q5_K.gguf | 18.3 GB | Q5_K_M | | |
| | OpenMythos-27B-Q4_K.gguf | 15.4 GB | Q4_K_M | | |
| | OpenMythos-27B-Q6_K.gguf | 21.2 GB | Q6_K | | |
| ## Benchmark | |
| Evaluated with [SecEval](https://github.com/XuanwuAI/SecEval) (commit 7aef317) on 2189 | |
| multiple-choice security questions. Backend: llama.cpp OpenAI-compatible server, fully | |
| offloaded to GPU. No chain-of-thought / reasoning enabled (`enable_thinking=false`). | |
| Prompt formatted with a system prompt requesting letter-only answers (no explanation). | |
| | Set | Model | Score | | |
| |-----|-------|-------| | |
| | A | OpenMythos-27B-Q5_K | 1703 / 2189 (77.8%) | | |
| | B | VulnLLM-R-7B | 1315 / 2189 (60.1%) | | |
| ### OpenMythos-27B-Q5_K test parameters | |
| - model: `OpenMythos-27B-Q5_K.gguf` | |
| - inference: `temp=0.2`, `top_p=0.8`, `top_k=20`, `min_p=0.05`, `repeat_penalty=1.02` | |
| - benchmark script: `/mnt/storage/SecEval-tmp/run_bench.py` | |
| - output: `seceval-1781809723.json` | |
| - prompt speed: 282 tok/s | generation speed: 68 tok/s | |
| #### Per-topic scores | |
| | Topic | Score | | |
| |-------|-------| | |
| | PenTest | 84.2% | | |
| | MemorySafety | 83.3% | | |
| | WebSecurity | 82.7% | | |
| | Vulnerability | 77.8% | | |
| | NetworkSecurity | 77.4% | | |
| | SoftwareSecurity | 75.0% | | |
| | ApplicationSecurity | 74.8% | | |
| | SystemSecurity | 73.6% | | |
| | Cryptography | 71.4% | | |
| ### VulnLLM-R-7B test parameters | |
| - model: `VulnLLM-R-7B.Q6_K.gguf` | |
| - inference: same settings as above | |
| - output: `seceval-1781811525.json` | |
| - prompt speed: 148 tok/s | generation speed: 39 tok/s | |
| #### Per-topic scores | |
| | Topic | Score | | |
| |-------|-------| | |
| | PenTest | 70.9% | | |
| | WebSecurity | 66.4% | | |
| | Vulnerability | 58.7% | | |
| | NetworkSecurity | 58.3% | | |
| | SystemSecurity | 56.4% | | |
| | SoftwareSecurity | 54.7% | | |
| | ApplicationSecurity | 54.7% | | |
| | MemorySafety | 54.2% | | |
| | Cryptography | 28.6% | | |
| Full detailed results are included in this repo: `seceval-1781809723.json` and | |
| `seceval-1781811525.json`. | |
| ## Usage | |
| ### llama-server (recommended) | |
| ```ini | |
| [OpenMythos-27B] | |
| model = /mnt/storage/models/OpenMythos/OpenMythos-27B-Q5_K.gguf | |
| chat-template-file = /mnt/storage/llama-server/chat_template-v15.jinja | |
| ctx-size = 65536 | |
| cache-type-k = q8_0 | |
| cache-type-v = q8_0 | |
| cache-prompt = on | |
| cache-reuse = 2048 | |
| batch-size = 4096 | |
| ubatch-size = 4096 | |
| kv-unified = on | |
| parallel = 1 | |
| gpu-layers = all | |
| temp = 0.2 | |
| top-p = 0.8 | |
| top-k = 20 | |
| min-p = 0.05 | |
| presence-penalty = 0.2 | |
| repeat-penalty = 1.02 | |
| spec-type = ngram-mod | |
| spec-draft-n-max = 5 | |
| reasoning-format = deepseek | |
| swa-checkpoints = 5 | |
| ``` | |
| ### llama-cli | |
| ```bash | |
| /mnt/storage/llama.cpp/build/bin/llama-cli \ | |
| -m /mnt/storage/models/OpenMythos/OpenMythos-27B-Q5_K.gguf \ | |
| --chat-template-file /mnt/storage/llama-server/chat_template-v15.jinja \ | |
| -c 65536 -b 4096 --ubatch-size 4096 \ | |
| --cache-type-k q8_0 --cache-type-v q8_0 \ | |
| --kv-unified -t 8 -fa \ | |
| --temp 0.2 --top-p 0.8 --top-k 20 --min-p 0.05 \ | |
| --presence-penalty 0.2 --repeat-penalty 1.02 \ | |
| -ngl all \ | |
| -p "Your prompt here" | |
| ``` | |