Instructions to use HOLOGRAMTECH/q-bonsai-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use HOLOGRAMTECH/q-bonsai-8b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="HOLOGRAMTECH/q-bonsai-8b", filename="tokenizer.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 HOLOGRAMTECH/q-bonsai-8b 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 HOLOGRAMTECH/q-bonsai-8b # Run inference directly in the terminal: llama cli -hf HOLOGRAMTECH/q-bonsai-8b
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf HOLOGRAMTECH/q-bonsai-8b # Run inference directly in the terminal: llama cli -hf HOLOGRAMTECH/q-bonsai-8b
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 HOLOGRAMTECH/q-bonsai-8b # Run inference directly in the terminal: ./llama-cli -hf HOLOGRAMTECH/q-bonsai-8b
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 HOLOGRAMTECH/q-bonsai-8b # Run inference directly in the terminal: ./build/bin/llama-cli -hf HOLOGRAMTECH/q-bonsai-8b
Use Docker
docker model run hf.co/HOLOGRAMTECH/q-bonsai-8b
- LM Studio
- Jan
- Ollama
How to use HOLOGRAMTECH/q-bonsai-8b with Ollama:
ollama run hf.co/HOLOGRAMTECH/q-bonsai-8b
- Unsloth Studio
How to use HOLOGRAMTECH/q-bonsai-8b 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 HOLOGRAMTECH/q-bonsai-8b 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 HOLOGRAMTECH/q-bonsai-8b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for HOLOGRAMTECH/q-bonsai-8b to start chatting
- Pi
How to use HOLOGRAMTECH/q-bonsai-8b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HOLOGRAMTECH/q-bonsai-8b
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": "HOLOGRAMTECH/q-bonsai-8b" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use HOLOGRAMTECH/q-bonsai-8b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HOLOGRAMTECH/q-bonsai-8b
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 HOLOGRAMTECH/q-bonsai-8b
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use HOLOGRAMTECH/q-bonsai-8b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HOLOGRAMTECH/q-bonsai-8b
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 "HOLOGRAMTECH/q-bonsai-8b" \ --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 HOLOGRAMTECH/q-bonsai-8b with Docker Model Runner:
docker model run hf.co/HOLOGRAMTECH/q-bonsai-8b
- Lemonade
How to use HOLOGRAMTECH/q-bonsai-8b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull HOLOGRAMTECH/q-bonsai-8b
Run and chat with the model
lemonade run user.q-bonsai-8b-{{QUANT_TAG}}List all available models
lemonade list
| license: apache-2.0 | |
| base_model: prism-ml/Bonsai-8B-gguf | |
| tags: | |
| - 1-bit | |
| - binary | |
| - webgpu | |
| - serverless | |
| - content-addressed | |
| - hologram | |
| - kappa | |
| # q-bonsai-8b — Bonsai-8B as a κ-object (streams into your browser like a film) | |
| **Created using [Bonsai](https://huggingface.co/prism-ml/Bonsai-8B-gguf) by Prism ML.** | |
| This is [prism-ml/Bonsai-8B](https://huggingface.co/prism-ml/Bonsai-8B-gguf) — a Qwen3-8B moved | |
| end-to-end into **binary {−1,+1} weights (1.125 true bits/weight, embeddings and LM head included)** — | |
| re-laid as a **content-addressed κ-object** for the [Hologram](https://github.com/Hologram-Technologies) | |
| serverless substrate. **No re-quantization anywhere**: the trained sign bits pass through byte-exact | |
| (f16 group scales widened to f32 once, exactly). The model runs **entirely in the browser on WebGPU** | |
| — no server, no account, no data leaves the device. | |
| ## What's here | |
| | File | What it is | | |
| |---|---| | |
| | `q-bonsai-8b.v1.holo` | ONE Range-streamable file: boot-ordered BLAKE3-verified blocks + embedded tokenizer. The reader serves tensors **while the download is still in flight** and warm-persists to OPFS (second visit = zero network, works offline). | | |
| | `manifest.json` + `b/*.gz` | The same blocks as loose content-addressed parts (sha256 transport axis). | | |
| | `manifest.blake3.json` + `sha256-to-blake3.map.json` | The canonical BLAKE3 (κ) axis. | | |
| | `tokenizer.gguf` | The source GGUF header (tokenizer + arch), for serverless load. | | |
| Every block is verified against its content address **before** it is decompressed or touches the GPU | |
| (Law L5). Any static file host can serve this repo — origins are interchangeable because the bytes, | |
| not the host, carry identity. | |
| ## Pins | |
| - manifest κ: `did:holo:sha256:a0dc81f26ec5ce98b28ee9c1fab620e91c20720f777e1de6bc416317f54d27e2` | |
| - root κ (tensor blocks): `sha256:8bf47176c881493a795cd911abb53603a27efc0d97ad8b5ed76f2df75b2e7816` | |
| - `.holo` determinism witness: `sha256:7c9d809ce70ad143…` (same input dir → byte-identical file) | |
| ## Measured (first light, 2026-07-15) | |
| Chrome / WebGPU, consumer laptop GPU: engine resident **7.8 s** after open (progressive reader | |
| returned at **1%** of the wire); decode **14–25 tok/s**; warm second visit loads from OPFS with | |
| **zero network**. Coherent Qwen3 thinking-mode output. | |
| ## Provenance | |
| Weights: Prism ML's Bonsai-8B (Apache-2.0) — see `LICENSE` and `NOTICE.txt`. | |
| Conversion: `compile2bit.mjs q1` mode (pass-through re-layout) + `holo-kappa-pack.mjs`, part of the | |
| Hologram Q substrate. Format `q1`: blob = `[signs N·K/8 B][f32 scales N·K/128·4 B]` per tensor; | |
| GEMV kernel dequantizes inside the matmul (weights never expand to dense f32 in memory). | |