Instructions to use ashwinij2/deepseek-r1-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ashwinij2/deepseek-r1-1.5b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ashwinij2/deepseek-r1-1.5b",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ashwinij2/deepseek-r1-1.5b 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 ashwinij2/deepseek-r1-1.5b
# Run inference directly in the terminal:
llama cli -hf ashwinij2/deepseek-r1-1.5b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ashwinij2/deepseek-r1-1.5b
# Run inference directly in the terminal:
llama cli -hf ashwinij2/deepseek-r1-1.5b

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 ashwinij2/deepseek-r1-1.5b
# Run inference directly in the terminal:
./llama-cli -hf ashwinij2/deepseek-r1-1.5b

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 ashwinij2/deepseek-r1-1.5b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ashwinij2/deepseek-r1-1.5b

Use Docker

docker model run hf.co/ashwinij2/deepseek-r1-1.5b

LM Studio
Jan
Ollama
How to use ashwinij2/deepseek-r1-1.5b with Ollama:
```
ollama run hf.co/ashwinij2/deepseek-r1-1.5b
```

Unsloth Studio

How to use ashwinij2/deepseek-r1-1.5b 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 ashwinij2/deepseek-r1-1.5b 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 ashwinij2/deepseek-r1-1.5b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ashwinij2/deepseek-r1-1.5b to start chatting

Atomic Chat new
Docker Model Runner
How to use ashwinij2/deepseek-r1-1.5b with Docker Model Runner:
```
docker model run hf.co/ashwinij2/deepseek-r1-1.5b
```

Lemonade

How to use ashwinij2/deepseek-r1-1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ashwinij2/deepseek-r1-1.5b

Run and chat with the model

lemonade run user.deepseek-r1-1.5b-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

DeepSeek R1: 1.5B

This is the DeepSeek R1: 1.5B model in GGUF format. It is a lightweight model designed for efficient inference.

Model Details

Model Family: Qwen2
Model Type: 1.8B
File Type: Q4_K_M
Architecture: amd64
OS: Linux

Usage

This model can be used with vLLM or other inference frameworks that support GGUF format.

License

This model is licensed under the MIT License. See the LICENSE file for details.

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GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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