Instructions to use TotoB12/totob-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TotoB12/totob-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="TotoB12/totob-1.5B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TotoB12/totob-1.5B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use TotoB12/totob-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 TotoB12/totob-1.5B
# Run inference directly in the terminal:
llama cli -hf TotoB12/totob-1.5B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TotoB12/totob-1.5B
# Run inference directly in the terminal:
llama cli -hf TotoB12/totob-1.5B

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 TotoB12/totob-1.5B
# Run inference directly in the terminal:
./llama-cli -hf TotoB12/totob-1.5B

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 TotoB12/totob-1.5B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TotoB12/totob-1.5B

Use Docker

docker model run hf.co/TotoB12/totob-1.5B

LM Studio
Jan

vLLM

How to use TotoB12/totob-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TotoB12/totob-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": "TotoB12/totob-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TotoB12/totob-1.5B

SGLang

How to use TotoB12/totob-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 "TotoB12/totob-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": "TotoB12/totob-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 "TotoB12/totob-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": "TotoB12/totob-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use TotoB12/totob-1.5B with Ollama:
```
ollama run hf.co/TotoB12/totob-1.5B
```
Unsloth Desktop
Docker Model Runner
How to use TotoB12/totob-1.5B with Docker Model Runner:
```
docker model run hf.co/TotoB12/totob-1.5B
```

Lemonade

How to use TotoB12/totob-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TotoB12/totob-1.5B

Run and chat with the model

lemonade run user.totob-1.5B-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

totob-1.5B

Overview

DeepSeek-R1 has garnered attention for matching OpenAI’s O1 reasoning model while being fully open-source, making it an attractive option for users who value local deployment for data privacy, reduced latency, and offline access. Traditionally, running such large models on personal devices involves quantization (e.g., Q4_K_M), which can compromise accuracy by as much as ~22% and diminish the benefits of local inference. With our new totob-1.5B model, we’ve overcome this trade-off by quantizing the DeepSeek-R1 Distilled model to just a quarter of its original size — without any loss in accuracy.

Benchmarks

Coming soon!!

Downloads last month: 18

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for TotoB12/totob-1.5B

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Quantized

(273)

this model

TotoB12
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totob-1.5B

totob-1.5B

Overview

Benchmarks

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