Instructions to use gghfez/DeepSeek-V3-0324-IQ2_KS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gghfez/DeepSeek-V3-0324-IQ2_KS with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="gghfez/DeepSeek-V3-0324-IQ2_KS",
	filename="DeepSeek-V3-0324-IQ2_KS-00001-of-00005.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use gghfez/DeepSeek-V3-0324-IQ2_KS with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K
# Run inference directly in the terminal:
llama-cli -hf gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K
# Run inference directly in the terminal:
llama-cli -hf gghfez/DeepSeek-V3-0324-IQ2_KS: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 gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf gghfez/DeepSeek-V3-0324-IQ2_KS: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 gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K

Use Docker

docker model run hf.co/gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K

LM Studio
Jan

vLLM

How to use gghfez/DeepSeek-V3-0324-IQ2_KS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gghfez/DeepSeek-V3-0324-IQ2_KS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gghfez/DeepSeek-V3-0324-IQ2_KS",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K

Ollama
How to use gghfez/DeepSeek-V3-0324-IQ2_KS with Ollama:
```
ollama run hf.co/gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K
```

Unsloth Studio

How to use gghfez/DeepSeek-V3-0324-IQ2_KS 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 gghfez/DeepSeek-V3-0324-IQ2_KS 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 gghfez/DeepSeek-V3-0324-IQ2_KS to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for gghfez/DeepSeek-V3-0324-IQ2_KS to start chatting

Atomic Chat new
Docker Model Runner
How to use gghfez/DeepSeek-V3-0324-IQ2_KS with Docker Model Runner:
```
docker model run hf.co/gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K
```

Lemonade

How to use gghfez/DeepSeek-V3-0324-IQ2_KS with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gghfez/DeepSeek-V3-0324-IQ2_KS:Q2_K

Run and chat with the model

lemonade run user.DeepSeek-V3-0324-IQ2_KS-Q2_K

List all available models

lemonade list

`ik_llama.cpp` imatrix MLA Quantizations of DeepSeek-V3-0324

This is an IQ2_KS quant of DeepSeek-V3-0324 using ubergarm's IQ2_KS recipe from ubergarm/DeepSeek-TNG-R1T2-Chimera-GGUF and Imatrix file from ubergarm/DeepSeek-V3-0324-GGUF.

This quant collection REQUIRES ik_llama.cpp fork to support advanced non-linear SotA quants and Multi-Head Latent Attention (MLA). Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

See ubergarm/DeepSeek-V3-0324-GGUF for his other quants and more details about them.

I've uploaded the converted BF16 weights gghfez/DeepSeek-V3-0324-256x21B-BF16 if I, or anyone else wants to create similar quants in the future.

TODO: fix links, etc in the model card.

Downloads last month: 12

GGUF

Model size

672B params

Architecture

deepseek2

Hardware compatibility

2-bit

Model tree for gghfez/DeepSeek-V3-0324-IQ2_KS

Base model

deepseek-ai/DeepSeek-V3-0324

Quantized

(25)

this model

Collection including gghfez/DeepSeek-V3-0324-IQ2_KS

ik_llama quants

Collection

8 items • Updated Mar 2 • 1

ik_llama.cpp imatrix MLA Quantizations of DeepSeek-V3-0324

Model tree for gghfez/DeepSeek-V3-0324-IQ2_KS

Collection including gghfez/DeepSeek-V3-0324-IQ2_KS

`ik_llama.cpp` imatrix MLA Quantizations of DeepSeek-V3-0324