Instructions to use John1604/DeepSeek-R1-Distill-Llama-70B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use John1604/DeepSeek-R1-Distill-Llama-70B-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 John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf John1604/DeepSeek-R1-Distill-Llama-70B-gguf: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 John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf John1604/DeepSeek-R1-Distill-Llama-70B-gguf: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 John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M

Use Docker

docker model run hf.co/John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use John1604/DeepSeek-R1-Distill-Llama-70B-gguf with Ollama:
```
ollama run hf.co/John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M
```

Unsloth Studio

How to use John1604/DeepSeek-R1-Distill-Llama-70B-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 John1604/DeepSeek-R1-Distill-Llama-70B-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 John1604/DeepSeek-R1-Distill-Llama-70B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for John1604/DeepSeek-R1-Distill-Llama-70B-gguf to start chatting

Docker Model Runner
How to use John1604/DeepSeek-R1-Distill-Llama-70B-gguf with Docker Model Runner:
```
docker model run hf.co/John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M
```

Lemonade

How to use John1604/DeepSeek-R1-Distill-Llama-70B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull John1604/DeepSeek-R1-Distill-Llama-70B-gguf:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Llama-70B-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

Deepseek-r1 70B

This is distilled Deepseek-r1. Make sure you have enough ram/gpu to run. On the right of model card, you may see the size of each quantized models.

Use the model in ollama

First download and install ollama.

https://ollama.com/download

Command

in windows command line, or in terminal in ubuntu, type:

ollama run hf.co/John1604/DeepSeek-R1-Distill-Llama-70B-gguf:q3_k_m

(q3_k_m is the model quant type, q5_k_s, q4_k_m, ..., can also be used)

C:\Users\developer>ollama run hf.co/John1604/DeepSeek-R1-Distill-Llama-70B-gguf:q3_k_m
pulling manifest
...
verifying sha256 digest
writing manifest
success

>>> Send a message (/? for help)

Use the model in LM Studio

download and install LM Studio

https://lmstudio.ai/

Discover models

In the LM Studio, click "Discover" icon. "Mission Control" popup window will be displayed.

In the "Mission Control" search bar, type "John1604/DeepSeek-R1-Distill-Llama-70B-gguf" and check "GGUF", the model should be found.

Download the model.

Load the model.

Ask questions.

quantized models

Type	Bits	Quality	Description
Q2_K	2-bit	🟥 Low	Minimal footprint; only for tests
Q3_K_S	3-bit	🟧 Low	“Small” variant (less accurate)
Q3_K_M	3-bit	🟧 Low–Med	“Medium” variant
Q4_K_S	4-bit	🟨 Med	Small, faster, slightly less quality
Q4_K_M	4-bit	🟩 Med–High	“Medium” — best 4-bit balance
Q5_K_S	5-bit	🟩 High	Slightly smaller than Q5_K_M
Q5_K_M	5-bit	🟩🟩 High	Excellent general-purpose quant
Q6_K	6-bit	🟩🟩🟩 Very High	Almost FP16 quality, larger size
Q8_0	8-bit	🟩🟩🟩🟩	Near-lossless baseline

Downloads last month: 137

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for John1604/DeepSeek-R1-Distill-Llama-70B-gguf

Base model

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

Quantized

(69)

this model