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

christopher-oneill commited on about 10 hours ago

Commit

a9dba44

1 Parent(s): 83b8641

Add community GPQA Diamond evaluation result (72.22%, thinking mode)

Browse files

Files changed (1) hide show

.eval_results/gpqa_diamond.yaml +11 -0

.eval_results/gpqa_diamond.yaml ADDED Viewed

	@@ -0,0 +1,11 @@

+# .eval_results/gpqa.yaml
+- dataset:
+    id: Idavidrein/gpqa
+    task_id: diamond
+  value: 72.22
+  date: "2026-07-18"
+  source:
+    url: https://huggingface.co/datasets/Idavidrein/gpqa
+    name: Community evaluation — GPQA Diamond (thinking mode, 198 questions, prism-ml/llama.cpp)
+    user: christopher-oneill
+  notes: "Q2, CTX=32768, thinking mode ON, temp=0.7, top-p=0.95, top-k=20, 143/198 correct, 44 wrong, 11 no-answer (OOTK), 0 errors, RTX4090 ~84 tok/s avg"