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

Libraries

How to use cortexso/deepseek-r1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cortexso/deepseek-r1",
	filename="deepseek-r1-distill-llama-70b-q4_k_m.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 cortexso/deepseek-r1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/deepseek-r1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/deepseek-r1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/deepseek-r1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/deepseek-r1:Q4_K_M

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 cortexso/deepseek-r1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cortexso/deepseek-r1:Q4_K_M

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 cortexso/deepseek-r1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cortexso/deepseek-r1:Q4_K_M

Use Docker

docker model run hf.co/cortexso/deepseek-r1:Q4_K_M

LM Studio
Jan

vLLM

How to use cortexso/deepseek-r1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortexso/deepseek-r1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortexso/deepseek-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortexso/deepseek-r1:Q4_K_M

Ollama
How to use cortexso/deepseek-r1 with Ollama:
```
ollama run hf.co/cortexso/deepseek-r1:Q4_K_M
```

Unsloth Studio new

How to use cortexso/deepseek-r1 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 cortexso/deepseek-r1 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 cortexso/deepseek-r1 to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use cortexso/deepseek-r1 with Docker Model Runner:
```
docker model run hf.co/cortexso/deepseek-r1:Q4_K_M
```

Lemonade

How to use cortexso/deepseek-r1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/deepseek-r1:Q4_K_M

Run and chat with the model

lemonade run user.deepseek-r1-Q4_K_M

List all available models

lemonade list

Minh141120 commited on Feb 6, 2025

Commit

b525c36

verified ·

1 Parent(s): 5252b8d

Create README.md

Browse files

Files changed (1) hide show

README.md +59 -0

README.md ADDED Viewed

	@@ -0,0 +1,59 @@

+---
+## license: mit
+## Overview
+**DeepSeek** developed and released the **DeepSeek-R1** series, featuring multiple model sizes fine-tuned for high-performance text generation. These models are optimized for dialogue, reasoning, and information-seeking tasks, providing a balance of efficiency and accuracy while maintaining a smaller footprint compared to their original counterparts.
+The DeepSeek-R1 models include distilled and full-scale variants of both **Qwen** and **Llama** architectures, catering to various applications such as customer support, conversational AI, research, and enterprise automation.
+## Variants
+### DeepSeek-R1
+| No | Variant                                                                                        | Branch | Cortex CLI command                          |
+| -- | ---------------------------------------------------------------------------------------------- | ------- | ------------------------------------------ |
+| 1  | [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/cortexso/deepseek-r1/tree/1.5b)         | 1.5b    | `cortex run deepseek-r1-distill-qwen-1.5b` |
+| 2  | [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/cortexso/deepseek-r1/tree/7b)             | 7b      | `cortex run deepseek-r1-distill-qwen-7b`   |
+| 3  | [DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/cortexso/deepseek-r1/tree/8b)           | 8b      | `cortex run deepseek-r1-distill-llama-8b`  |
+| 4  | [DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/cortexso/deepseek-r1/tree/14b)           | 14b     | `cortex run deepseek-r1-distill-qwen-14b`  |
+| 5  | [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/cortexso/deepseek-r1/tree/32b)           | 32b     | `cortex run deepseek-r1-distill-qwen-32b`  |
+| 6  | [DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/cortexso/deepseek-r1/tree/70b)         | 70b     | `cortex run deepseek-r1-distill-llama-70b` |
+Each branch contains a default quantized version:
+- **Qwen-1.5B:** q4-km
+- **Qwen-7B:** q4-km
+- **Llama-8B:** q4-km
+- **Qwen-14B:** q4-km
+- **Qwen-32B:** q4-km
+- **Llama-70B:** q4-km
+## Use it with Jan (UI)
+1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)
+2. Use in Jan model Hub:
+   ```text
+   cortexso/deepseek-r1 [WIP]
+   cortexso/deepseek-r1 [WIP]
+   ```
+## Use it with Cortex (CLI)
+1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart)
+2. Run the model with command:
+   ```bash
+   cortex run [WIP]
+   ```
+   or
+   ```bash
+   cortex run [WIP]
+   ```
+## Credits
+- **Author:** DeepSeek
+- **Converter:** [Homebrew](https://www.homebrew.ltd/)
+- **Original License:** [License](https://huggingface.co/deepseek-ai/DeepSeek-R1#license)
+- **Papers:** [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://arxiv.org/html/2501.12948v1)