Instructions to use HTPQ/Focus-5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use HTPQ/Focus-5-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 HTPQ/Focus-5-GGUF # Run inference directly in the terminal: llama cli -hf HTPQ/Focus-5-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf HTPQ/Focus-5-GGUF # Run inference directly in the terminal: llama cli -hf HTPQ/Focus-5-GGUF
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 HTPQ/Focus-5-GGUF # Run inference directly in the terminal: ./llama-cli -hf HTPQ/Focus-5-GGUF
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 HTPQ/Focus-5-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf HTPQ/Focus-5-GGUF
Use Docker
docker model run hf.co/HTPQ/Focus-5-GGUF
- LM Studio
- Jan
- vLLM
How to use HTPQ/Focus-5-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HTPQ/Focus-5-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": "HTPQ/Focus-5-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/HTPQ/Focus-5-GGUF
- Ollama
How to use HTPQ/Focus-5-GGUF with Ollama:
ollama run hf.co/HTPQ/Focus-5-GGUF
- Unsloth Studio
How to use HTPQ/Focus-5-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 HTPQ/Focus-5-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 HTPQ/Focus-5-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for HTPQ/Focus-5-GGUF to start chatting
- Pi
How to use HTPQ/Focus-5-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HTPQ/Focus-5-GGUF
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": "HTPQ/Focus-5-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use HTPQ/Focus-5-GGUF with Docker Model Runner:
docker model run hf.co/HTPQ/Focus-5-GGUF
- Lemonade
How to use HTPQ/Focus-5-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull HTPQ/Focus-5-GGUF
Run and chat with the model
lemonade run user.Focus-5-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use HTPQ/Focus-5-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 HTPQ/Focus-5-GGUF
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 HTPQ/Focus-5-GGUF
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use HTPQ/Focus-5-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HTPQ/Focus-5-GGUF
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 "HTPQ/Focus-5-GGUF" \ --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"
Focus 5
A 495 MB GGUF that thinks before it answers, replies in your language, and reaches for a tool instead of guessing.
Focus-5.gguf 495,111,584 bytes Β· 472.18 MiB Β· 310 tensors Β· 40,960 ctx
sha256 4190e0525083ad020e6f1b2a23a2c14497c0eba1a6cc047d7fc1e01fa5c7faf9
Read this first
Focus 5 is not trained from scratch, and it is not a frontier model.
The weights are Qwen3-0.6B at Q6_K, copied byte for byte and not retrained β all
310 tensors verify identical to the base file. What this build changes is the
metadata block at the head of the GGUF: the identity, the tokenizer.chat_template,
and a set of focus.* / sinythos.* keys that a runtime reads back to configure
itself.
| Component | Origin | Changed? |
|---|---|---|
| Weights (310 tensors, 0.6B params, Q6_K) | Qwen3-0.6B | No β byte-identical, SHA-256 verified per tensor |
| Chat template | This project | Yes |
| System directive (six laws) | This project | Yes β baked into the file |
sinythos.* metadata |
This project | Yes |
That is the honest description, and it is written into general.description inside
the file itself so nobody downstream can mistake it for something else.
What 500 MB can and cannot be
500 MB of Q6_K weights is about 0.6 billion parameters. It can be genuinely multilingual, open a reasoning block on every turn, call tools correctly, and write working code for ordinary tasks. It cannot match a frontier model on benchmarks β not with better training, not with a better dataset, not with any quantisation trick. The gap is information-theoretic, not effort-based.
What does close a large part of the practical gap is the scaffolding: a small model that searches the web, scrapes the page, and computes with an exact calculator beats a much larger model working from memory alone on most real tasks. The six laws below exist to push it towards those tools on every turn where they apply.
The six laws
Baked into tokenizer.chat_template, so they apply even when the caller sets no
system prompt at all. A user-supplied system prompt is kept, but framed as operator
instructions that apply within the laws.
- Think first, in your language. Opens
<think>on every non-trivial turn. Detects the user's language and answers in it. - Do not guess β look it up. Anything recent, changing, or uncertain goes to
web_search+scrape_pagebefore answering. Inventing a fact, date, quote or URL is the one unforgivable failure. - Never do arithmetic in your head. Every calculation goes to
calculate. - Code is complete or it is not delivered. Real imports, real error handling,
no
TODO, no.... - 3D is real geometry. Real mesh, PBR materials, shadows β not coloured boxes.
- Break your own work before shipping it. Adversarial second pass, then say what was fixed.
The directive is readable straight out of the file under the
sinythos.system_directive metadata key.
Run it
llama-server -m Focus-5.gguf -c 40960 --jinja --host 0.0.0.0 --port 8080
--jinja is not optional. Without it llama.cpp ignores the embedded chat template,
the six laws never reach the model, and you get a stock Qwen3-0.6B. That is the single
easiest mistake to make with this file, and the symptom is "it behaves like plain Qwen".
LM Studio and Jan read the template from the file on their own, so no flag is needed there β just drag the file in.
Recommended sampling (Qwen3 thinking mode): temperature 0.6, top_p 0.95,
top_k 20, min_p 0. Do not use greedy decoding β it makes this family loop.
Tools it expects
web_search, scrape_page, calculate, run_code, write_file, make_asset β
listed in the sinythos.tools.required metadata key, advertised in Qwen's
XML-wrapped-JSON tool-call format. A reference runtime that implements all six,
including an exact BigInt-rational calculator, is linked below.
Verify it yourself
sha256sum Focus-5.gguf
# 4190e0525083ad020e6f1b2a23a2c14497c0eba1a6cc047d7fc1e01fa5c7faf9
The build is reproducible: fetch Qwen3-0.6B-Q6_K.gguf from
unsloth/Qwen3-0.6B-GGUF, re-mux it
with this project's build script, and you get the same digest.
Licence
Apache 2.0, inherited from Qwen3-0.6B β the weights are Qwen's work and are unmodified. The chat template, the system directive and the metadata are this project's, also Apache 2.0.
- Downloads last month
- 198
We're not able to determine the quantization variants.