Instructions to use prism-ml/Ternary-Bonsai-27B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prism-ml/Ternary-Bonsai-27B-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prism-ml/Ternary-Bonsai-27B-gguf",
	filename="Ternary-Bonsai-27B-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prism-ml/Ternary-Bonsai-27B-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 prism-ml/Ternary-Bonsai-27B-gguf:F16
# Run inference directly in the terminal:
llama cli -hf prism-ml/Ternary-Bonsai-27B-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prism-ml/Ternary-Bonsai-27B-gguf:F16
# Run inference directly in the terminal:
llama cli -hf prism-ml/Ternary-Bonsai-27B-gguf:F16

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 prism-ml/Ternary-Bonsai-27B-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf prism-ml/Ternary-Bonsai-27B-gguf:F16

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 prism-ml/Ternary-Bonsai-27B-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prism-ml/Ternary-Bonsai-27B-gguf:F16

Use Docker

docker model run hf.co/prism-ml/Ternary-Bonsai-27B-gguf:F16

LM Studio
Jan

vLLM

How to use prism-ml/Ternary-Bonsai-27B-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prism-ml/Ternary-Bonsai-27B-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": "prism-ml/Ternary-Bonsai-27B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prism-ml/Ternary-Bonsai-27B-gguf:F16

Ollama
How to use prism-ml/Ternary-Bonsai-27B-gguf with Ollama:
```
ollama run hf.co/prism-ml/Ternary-Bonsai-27B-gguf:F16
```

Unsloth Studio

How to use prism-ml/Ternary-Bonsai-27B-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 prism-ml/Ternary-Bonsai-27B-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 prism-ml/Ternary-Bonsai-27B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prism-ml/Ternary-Bonsai-27B-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use prism-ml/Ternary-Bonsai-27B-gguf with Docker Model Runner:
```
docker model run hf.co/prism-ml/Ternary-Bonsai-27B-gguf:F16
```

Lemonade

How to use prism-ml/Ternary-Bonsai-27B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prism-ml/Ternary-Bonsai-27B-gguf:F16

Run and chat with the model

lemonade run user.Ternary-Bonsai-27B-gguf-F16

List all available models

lemonade list

New discussion

Resources

View closed (3)

Unable to pull Q8_0

#26 opened about 16 hours ago by

ly2025

vLLM kernels question + RAG concurrency analysis

#25 opened about 17 hours ago by

Astezelex

Benchmark: 27B Q2_0 vs Qwen3.6-27B IQ2_XXS on one RTX 5060 Ti 16 GB (quality@budget, speed, VRAM, energy)

🔥 4

#24 opened about 18 hours ago by

Astezelex

Thank you for enabling local models to be useful! - MoE models next please!

🔥 3

#23 opened about 19 hours ago by

KhaledAshour

Failed to load the model

#22 opened about 19 hours ago by

parseco

NPU?

#21 opened about 20 hours ago by

neoOpus

Script to convert PQ2_0 to Q2_0 - code in-line here

#20 opened 1 day ago by

sfrav

Generates gibberish and repeats itself in the middle of thinking and token generation

🚀👀 2

#19 opened 1 day ago by

rbtasd

What parameters can generally be set in LM Studio to achieve optimal performance?

#18 opened 1 day ago by

pymo

turboquant for 2 bit model https://github.com/jarkevithwlad/turboquant-prismml-cuda

❤️👍 6

#17 opened 1 day ago by

jarkevithwlad

10 tok/s on an RTX 3060 12GB (Q2_0)

🔥 4

#16 opened 2 days ago by

bukit

it hallicunates hard

🚀 1

#15 opened 2 days ago by

AsThirtyThree

Ternary-Bonsai-27B Q2_0 works on ROCm / RDNA3 (RX 7800 XT, gfx1101) — benchmarks + iGPU gotcha

👍🔥 2

#14 opened 2 days ago by

basselhajj

We need bonsai gemma 4 12B

👍 2

#13 opened 2 days ago by

CNWPlayer

Unable to get D-Spark Q4_1 working with Q2_0 Model file

#11 opened 2 days ago by

AceTheUnfazed

Documenting what each GGUF file does

👍 8

#10 opened 3 days ago by

croqaz

Cost for quantisation?

👀➕ 4

#9 opened 3 days ago by

Luke2642

approved online demo?

#8 opened 3 days ago by

ct-2

Please share this research to be replicable

🔥 6

#7 opened 3 days ago by

CYISNOTHERE

WOAH 🔥

🤗 4

#6 opened 3 days ago by

el4

next do Ternary bonsai Gemma 31B, 🙏

👍👀 6

#4 opened 3 days ago by

BriggoBoy

Ternary-Bonsai-27B-Q2_g64.gguf refuses to load

#3 opened 3 days ago by

llama-anone

Thanks.

❤️👍 13

#2 opened 3 days ago by

Hansdudin202

Missing g128 version

#1 opened 3 days ago by

CurbStomper