Instructions to use WaveCut/Bonsai-web-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use WaveCut/Bonsai-web-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="WaveCut/Bonsai-web-GGUF", filename="1_7b/Bonsai-1.7B-Q1_0.gguf-00001-of-00001.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 WaveCut/Bonsai-web-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 WaveCut/Bonsai-web-GGUF:Q1_0 # Run inference directly in the terminal: llama cli -hf WaveCut/Bonsai-web-GGUF:Q1_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf WaveCut/Bonsai-web-GGUF:Q1_0 # Run inference directly in the terminal: llama cli -hf WaveCut/Bonsai-web-GGUF:Q1_0
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 WaveCut/Bonsai-web-GGUF:Q1_0 # Run inference directly in the terminal: ./llama-cli -hf WaveCut/Bonsai-web-GGUF:Q1_0
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 WaveCut/Bonsai-web-GGUF:Q1_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf WaveCut/Bonsai-web-GGUF:Q1_0
Use Docker
docker model run hf.co/WaveCut/Bonsai-web-GGUF:Q1_0
- LM Studio
- Jan
- vLLM
How to use WaveCut/Bonsai-web-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WaveCut/Bonsai-web-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": "WaveCut/Bonsai-web-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/WaveCut/Bonsai-web-GGUF:Q1_0
- Ollama
How to use WaveCut/Bonsai-web-GGUF with Ollama:
ollama run hf.co/WaveCut/Bonsai-web-GGUF:Q1_0
- Unsloth Studio
How to use WaveCut/Bonsai-web-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 WaveCut/Bonsai-web-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 WaveCut/Bonsai-web-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for WaveCut/Bonsai-web-GGUF to start chatting
- Pi
How to use WaveCut/Bonsai-web-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf WaveCut/Bonsai-web-GGUF:Q1_0
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": "WaveCut/Bonsai-web-GGUF:Q1_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use WaveCut/Bonsai-web-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 WaveCut/Bonsai-web-GGUF:Q1_0
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 WaveCut/Bonsai-web-GGUF:Q1_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use WaveCut/Bonsai-web-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf WaveCut/Bonsai-web-GGUF:Q1_0
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 "WaveCut/Bonsai-web-GGUF:Q1_0" \ --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 WaveCut/Bonsai-web-GGUF with Docker Model Runner:
docker model run hf.co/WaveCut/Bonsai-web-GGUF:Q1_0
- Lemonade
How to use WaveCut/Bonsai-web-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull WaveCut/Bonsai-web-GGUF:Q1_0
Run and chat with the model
lemonade run user.Bonsai-web-GGUF-Q1_0
List all available models
lemonade list
Bonsai Web GGUF
Browser-ready, deterministic shards for the
WaveCut/bonsai-webgpu-chat
Space. The files are the pinned Prism ML Bonsai Q1_0 GGUF releases, re-sharded
with llama-gguf-split --split-max-size 512M so every browser download stays
below the current wllama single-file limit.
Contents
| Tier | Architecture | Download | Shards | CPU fallback |
|---|---|---|---|---|
1_7b |
Qwen3 dense | 248,302,336 bytes | 1 | yes |
4b |
Qwen3 dense | 572,270,816 bytes | 2 | yes |
8b |
Qwen3 dense | 1,158,654,816 bytes | 3 | yes |
27b |
Qwen3.5 hybrid | 3,803,453,440 bytes | 8 | no |
The repository also carries
dflash/Qwen3.6-27B-DFlash-Q8_0.gguf (1,849,481,440 bytes) as a
native correctness reference only. It is not part of a tier download and
is not loaded by the browser Space.
manifest.json is the machine-readable contract: immutable source provenance,
ordered shard paths, bytes, SHA-256, context defaults, WebGPU limits, chat
template evidence, and the default-off DFlash research status. SHA256SUMS
is the independent shard checksum list.
Source provenance
| Tier | Source repository | Pinned revision | Original SHA-256 |
|---|---|---|---|
1_7b |
prism-ml/Bonsai-1.7B-gguf |
210a9e99f79cb184909d49595906526eb2b3dd9a |
3d7c6c90dd98717a203adb22d5eacd2581850e40aa5327e144b97766cae5f7e3 |
4b |
prism-ml/Bonsai-4B-gguf |
78f2c2bacd0904ffaba24b4873ed975e5818354a |
4524b3f997f0f06444e568d1f26e2efd69effa3218c7ad3047432fb171e42168 |
8b |
prism-ml/Bonsai-8B-gguf |
48516770dd04643643e9f9019a2a349cf26c5dbd |
284a335aa3fb2ced3b1b01fcb40b08aa783e3b70832767f0dd2e3fdfa134bd54 |
27b |
prism-ml/Bonsai-27B-gguf |
0cf7e3d21581b169b4df1de8bf01316000e2fbb7 |
17ef842e47450caeb8eaa3ebfbbab5d2f2278b62b79be107985fb69a2f819aa0 |
The shard container checksums differ from each original monolithic file because GGUF splitting adds split metadata. No quantization or tensor conversion is performed.
The first shard of every tier was loaded through llama.cpp's automatic multi-file resolver and compared with its monolithic source under greedy sampling. Token IDs and text match exactly: 64/64 tokens for 1.7B and 27B, and 16/16 tokens for 4B and 8B. The shorter middle-tier checks are load and reassembly guards, not quality evaluations.
Loading with wllama
Pass the ordered URLs from manifest.json to loadModelFromUrl:
await wllama.loadModelFromUrl(model.files.map((file) => file.url), {
n_ctx: model.defaultContext,
n_gpu_layers: 99999,
flash_attn: false,
useCache: true,
});
The 27B tier is WebGPU-only in the Space. The application must reject it when adapter limits, storage quota, or full-graph placement are insufficient; it must not silently fall back to CPU.
Runtime evidence
The release gate uses pinned llama.cpp 00fa7cb284cbf133fc426733bd64238a3588a33e
and Dawn 18eb229ef5f707c1464cc581252e7603c73a3ef0. On Apple M2 Max, all required
Q1_0 and Qwen3.5 hybrid WebGPU operation tests passed. Full-graph 1.7B and 27B
inference requires the token embedding tensor to remain on WebGPU; Flash
Attention stays disabled because quantized K/V combinations are not fully
supported.
DFlash
DFlash remains inspection-only and default-off in the browser. The manifest
pins the BF16 source and the bundled Q8_0 GGUF reference. Shape, complete
tokenizer equality, target taps [2, 17, 32, 47, 62], conversion, and a
32-token temperature-0 native Bonsai-27B parity smoke pass.
The current custom wllama build contains the DFlash llama.cpp architecture,
but its TypeScript/worker/glue load path does not yet expose
spec_type=draft-dflash. The Q8_0 fc.weight is 139,264,000 bytes, above the
128 MiB baseline WebGPU storage-buffer binding limit. Browser activation,
acceptance measurement, and a positive decode-speed result therefore remain
hard release gates. The reference must not be presented as a browser-ready
drafter.
License
The Bonsai model files are published under Apache License 2.0. The DFlash
reference is MIT-licensed. The source license is included as LICENSE and
tier/DFlash attribution is preserved under notices/.
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Model tree for WaveCut/Bonsai-web-GGUF
Base model
Qwen/Qwen3.6-27B
docker model run hf.co/WaveCut/Bonsai-web-GGUF:Q1_0