Instructions to use TotoB12/totob-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TotoB12/totob-1.5B", dtype="auto") - llama-cpp-python
How to use TotoB12/totob-1.5B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TotoB12/totob-1.5B", filename="totob-1.5B.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use TotoB12/totob-1.5B with llama.cpp:
Install from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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 Studio new
How to use TotoB12/totob-1.5B 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 TotoB12/totob-1.5B 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 TotoB12/totob-1.5B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TotoB12/totob-1.5B to start chatting
- 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
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
- 15
We're not able to determine the quantization variants.
Model tree for TotoB12/totob-1.5B
Base model
deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B