Instructions to use NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant", dtype="auto")

llama-cpp-python

How to use NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant",
	filename="DeepSeek-R1-Distill-Llama-8B-NexaQuant.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 NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant
# Run inference directly in the terminal:
llama-cli -hf NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant
# Run inference directly in the terminal:
llama-cli -hf NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant

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 NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant
# Run inference directly in the terminal:
./llama-cli -hf NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant

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 NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant

Use Docker

docker model run hf.co/NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant

LM Studio
Jan
Ollama
How to use NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant with Ollama:
```
ollama run hf.co/NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant
```

Unsloth Studio

How to use NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant 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 NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant 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 NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant to start chatting

Docker Model Runner
How to use NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant with Docker Model Runner:
```
docker model run hf.co/NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant
```

Lemonade

How to use NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NexaAI/DeepSeek-R1-Distill-Llama-8B-NexaQuant

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Llama-8B-NexaQuant-{{QUANT_TAG}}

List all available models

lemonade list

Davidqian123 commited on Feb 6, 2025

Commit

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1 Parent(s): edbdc2b

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@@ -8,21 +8,20 @@ tags:
 - llama
 - llama-3
 - meta
 ---
 # DeepSeek-R1-Distill-Llama-8B-NexaQuant
 ## Background + Overview
 DeepSeek-R1 has been making headlines for rivaling OpenAI’s O1 reasoning model while remaining fully open-source. Many users want to run it locally to ensure data privacy, reduce latency, and maintain offline access. However, fitting such a large model onto personal devices typically requires quantization (e.g. Q4_K_M), which often sacrifices accuracy (up to ~22% accuracy loss) and undermines the benefits of the local reasoning model.
 We’ve solved the trade-off by quantizing the DeepSeek R1 Distilled model to one-fourth its original size—without losing any accuracy. This lets you run powerful on-device reasoning wherever you are, with no compromises. Tests on an **HP Omnibook AIPC** with an **AMD Ryzen™ AI 9 HX 370 processor** showed a decoding speed of **66.40 tokens per second** and a peak RAM usage of just **1228 MB** in NexaQuant version—compared to only **25.28 tokens** per second and **3788 MB RAM** in the unquantized version—while **maintaining full precision model accuracy.**
----
 ## How to run locally
-NexaQuant is compatible with **Nexa-SDK**, **Ollama**, **LM Studio**, **Llama.cpp**, and any llama.cpp based project.
-Below, we outline multiple ways to run the model locally.
 #### Option 1: Using Nexa SDK

 - llama
 - llama-3
 - meta
+- GGUF
 ---
 # DeepSeek-R1-Distill-Llama-8B-NexaQuant
 ## Background + Overview
 DeepSeek-R1 has been making headlines for rivaling OpenAI’s O1 reasoning model while remaining fully open-source. Many users want to run it locally to ensure data privacy, reduce latency, and maintain offline access. However, fitting such a large model onto personal devices typically requires quantization (e.g. Q4_K_M), which often sacrifices accuracy (up to ~22% accuracy loss) and undermines the benefits of the local reasoning model.
 We’ve solved the trade-off by quantizing the DeepSeek R1 Distilled model to one-fourth its original size—without losing any accuracy. This lets you run powerful on-device reasoning wherever you are, with no compromises. Tests on an **HP Omnibook AIPC** with an **AMD Ryzen™ AI 9 HX 370 processor** showed a decoding speed of **66.40 tokens per second** and a peak RAM usage of just **1228 MB** in NexaQuant version—compared to only **25.28 tokens** per second and **3788 MB RAM** in the unquantized version—while **maintaining full precision model accuracy.**
 ## How to run locally
+NexaQuant is compatible with **Nexa-SDK**, **Ollama**, **LM Studio**, **Llama.cpp**, and any llama.cpp based project. Below, we outline multiple ways to run the model locally.
 #### Option 1: Using Nexa SDK