Instructions to use Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF 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 Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K

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 Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K

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 Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K

Use Docker

docker model run hf.co/Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF with Ollama:
```
ollama run hf.co/Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K
```

Unsloth Studio

How to use Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF 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 Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF 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 Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF to start chatting

Docker Model Runner
How to use Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF with Docker Model Runner:
```
docker model run hf.co/Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K
```

Lemonade

How to use Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF:Q2_K

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF-Q2_K

List all available models

lemonade list

Atomic Chat

Produced by Antigma Labs

llama.cpp quantization

Using llama.cpp release b4944 for quantization. Original model: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B Run them directly with llama.cpp, or any other llama.cpp based project

Prompt format

<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜>

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Split
deepseek-r1-distill-llama-70b-q2_k.gguf	Q2_K	24.56 GB	False

Downloading using huggingface-cli

Click to view download instructions

First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download https://huggingface.co/Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF --include "deepseek-r1-distill-llama-70b-q2_k.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download https://huggingface.co/Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF --include "deepseek-r1-distill-llama-70b-q2_k.gguf/*" --local-dir ./ ``` You can either specify a new local-dir (deepseek-ai_DeepSeek-V3-0324-Q8_0) or download them all in place (./)

Downloads last month: 44

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

2-bit

Inference Providers NEW

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Model tree for Brianpuz/DeepSeek-R1-Distill-Llama-70B-Q2_K-GGUF

Base model

deepseek-ai/DeepSeek-R1-Distill-Llama-70B

Quantized

(69)

this model