Instructions to use HOLOGRAMTECH/q-bonsai-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HOLOGRAMTECH/q-bonsai-27b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HOLOGRAMTECH/q-bonsai-27b",
	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-27b 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-27b
# Run inference directly in the terminal:
llama cli -hf HOLOGRAMTECH/q-bonsai-27b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HOLOGRAMTECH/q-bonsai-27b
# Run inference directly in the terminal:
llama cli -hf HOLOGRAMTECH/q-bonsai-27b

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-27b
# Run inference directly in the terminal:
./llama-cli -hf HOLOGRAMTECH/q-bonsai-27b

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-27b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HOLOGRAMTECH/q-bonsai-27b

Use Docker

docker model run hf.co/HOLOGRAMTECH/q-bonsai-27b

LM Studio
Jan
Ollama
How to use HOLOGRAMTECH/q-bonsai-27b with Ollama:
```
ollama run hf.co/HOLOGRAMTECH/q-bonsai-27b
```

Unsloth Studio

How to use HOLOGRAMTECH/q-bonsai-27b 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-27b 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-27b 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-27b to start chatting

How to use HOLOGRAMTECH/q-bonsai-27b 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-27b

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-27b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use HOLOGRAMTECH/q-bonsai-27b 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-27b

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-27b

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use HOLOGRAMTECH/q-bonsai-27b 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-27b

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-27b" \
  --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-27b with Docker Model Runner:
```
docker model run hf.co/HOLOGRAMTECH/q-bonsai-27b
```

Lemonade

How to use HOLOGRAMTECH/q-bonsai-27b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HOLOGRAMTECH/q-bonsai-27b

Run and chat with the model

lemonade run user.q-bonsai-27b-{{QUANT_TAG}}

List all available models

lemonade list

q-bonsai-27b — Bonsai-27B as a κ-object (the full 27B mind, streamed like a film)

Created using Bonsai by Prism ML.

This is prism-ml/Bonsai-27B — a 27-billion-parameter hybrid-attention model (Qwen3.5 family: 16 full-attention + 48 gated-delta linear layers) 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 serverless substrate. No re-quantization anywhere: the trained sign bits pass through byte-exact.

What's here

File	What it is
`q-bonsai-27b.v1.holo`	ONE Range-streamable file: boot-ordered BLAKE3-verified blocks + embedded tokenizer, lm_head packed early so a progressive reader serves the decode-critical path first.
`manifest.json` + `b/*.gz`	The same 803 blocks as loose content-addressed parts (sha256 transport axis) — the parallel-fetch fast path on CDN origins.
`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. Any static file host serving these bytes is an equal origin — the bytes, not the host, carry identity.

Pins

manifest κ: did:holo:sha256:00beea03509a79f6567879f019c04bb15dda8721f6320f8295d9ea1dd9ba54af
root κ (tensor blocks): sha256:a5f7b8ed89546306702547e1e47541572ef58… (full value in manifest)

Architecture (from the GGUF header)

64 blocks, d=5120, ff=17408, vocab 248320; full_attention_interval=4 → 16 full-attention layers (24 heads × 256, 4 KV heads, per-head q/k RMSNorm, gated output) + 48 linear layers (gated-delta SSM: conv kernel 4, state 128, 16 groups, dt-rank 48, inner 6144); MRoPE sections [11,11,10,0], rope base 1e7; 262K trained context.

Provenance

Weights: Prism ML's Bonsai-27B (Apache-2.0) — see LICENSE and NOTICE.txt. Conversion: compile2bit.mjs q1 pass-through + holo-kappa-pack.mjs (Hologram Q substrate). Format q1: blob = [signs N·K/8 B][f32 scales N·K/128·4 B] per tensor; the GEMV kernel dequantizes inside the matmul. Browser engine support for the hybrid architecture is in active development on the Hologram substrate; the 8B sibling (HOLOGRAMTECH/q-bonsai-8b) runs live today.

Downloads last month: -

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for HOLOGRAMTECH/q-bonsai-27b

Base model

Qwen/Qwen3.6-27B

Quantized

prism-ml/Bonsai-27B-gguf

Quantized

(3)

this model