Instructions to use eventvoid/yes-or-no-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eventvoid/yes-or-no-1.5b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="eventvoid/yes-or-no-1.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eventvoid/yes-or-no-1.5b")
model = AutoModelForCausalLM.from_pretrained("eventvoid/yes-or-no-1.5b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use eventvoid/yes-or-no-1.5b 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 eventvoid/yes-or-no-1.5b:Q8_0
# Run inference directly in the terminal:
llama cli -hf eventvoid/yes-or-no-1.5b:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf eventvoid/yes-or-no-1.5b:Q8_0
# Run inference directly in the terminal:
llama cli -hf eventvoid/yes-or-no-1.5b:Q8_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 eventvoid/yes-or-no-1.5b:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf eventvoid/yes-or-no-1.5b:Q8_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 eventvoid/yes-or-no-1.5b:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eventvoid/yes-or-no-1.5b:Q8_0

Use Docker

docker model run hf.co/eventvoid/yes-or-no-1.5b:Q8_0

LM Studio
Jan

vLLM

How to use eventvoid/yes-or-no-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eventvoid/yes-or-no-1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eventvoid/yes-or-no-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eventvoid/yes-or-no-1.5b:Q8_0

SGLang

How to use eventvoid/yes-or-no-1.5b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "eventvoid/yes-or-no-1.5b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eventvoid/yes-or-no-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "eventvoid/yes-or-no-1.5b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eventvoid/yes-or-no-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use eventvoid/yes-or-no-1.5b with Ollama:
```
ollama run hf.co/eventvoid/yes-or-no-1.5b:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use eventvoid/yes-or-no-1.5b with Docker Model Runner:
```
docker model run hf.co/eventvoid/yes-or-no-1.5b:Q8_0
```

Lemonade

How to use eventvoid/yes-or-no-1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eventvoid/yes-or-no-1.5b:Q8_0

Run and chat with the model

lemonade run user.yes-or-no-1.5b-Q8_0

List all available models

lemonade list

Atomic Chat

yes-or-no-1.5b

Answers yes/no questions — and admits when it cannot. It replies with exactly one of nine strings and nothing else: Yes! Yes. Yes? No! No. No? Maybe! Maybe. Maybe? The word is the answer; the punctuation is confidence (! high, . medium, ? low).

Maybe is the point, not a failure mode. An oracle forced to pick a side will tell you that you can give chocolate to your dog. Maybe? means it cannot know; Maybe! means the question is undetermined — a coin toss has no answer, and that is a fact.

It also checks statements as claims, not only questions, refuses non-questions, ignores injected instructions, and obeys an operator policy in the system turn where that policy adds caution — never to overrule a fact. A date given there makes date questions answerable; without one, Maybe?.

Usage

from openai import OpenAI
c = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")
c.chat.completions.create(model="yes-or-no-1.5b", temperature=0,
    messages=[{"role": "user", "content": "Is Berlin in Germany?"}])   # -> Yes!

Only the system turn and the last user message are used; context is 2048 tokens. The chat template ships in the weights and in the GGUF — with llama.cpp pass --jinja.

How good is it, honestly

Held-out, 9,928 rows: answer 89.2%, confidence marker 90.6%, ECE 0.038; in 500 free generations, 0 replies outside the nine strings. Hazardous questions 96.4% — 167 of 167 dangerous ones correct, zero wrong "yes". Speaker-only claims 99.8%, dates 99.2%, world facts 85.0%, contested 70.8%.

Bad at: letters and spelling — does 'banana' have three a is a coin flip (51.7% against 50% for guessing) and confidently wrong, so build nothing on it. Also contested claims, arithmetic past small numbers, anything after 2023, and every language except English.

Trained on 193k purpose-built rows; the dataset is not published. Apache-2.0. Modified weights and chat template; base architecture and tokenizer unchanged — see the model tree for lineage.

Downloads last month: 215

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for eventvoid/yes-or-no-1.5b

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(288)

this model

Quantizations

1 model