Instructions to use daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF",
	filename="DeepSeek-Coder-V2-Lite-Instruct_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 daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_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 daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_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 daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M

Use Docker

docker model run hf.co/daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M

Ollama
How to use daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF with Ollama:
```
ollama run hf.co/daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M
```

Unsloth Studio new

How to use daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_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 daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_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 daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF to start chatting

Docker Model Runner
How to use daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF with Docker Model Runner:
```
docker model run hf.co/daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M
```

Lemonade

How to use daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-Coder-V2-Lite-Instruct_GGUF-Q4_K_M

List all available models

lemonade list

DeepSeek-Coder-V2-Lite-Instruct - GGUF High-Quality Quantizations

This repository provides GGUF quantized versions of the deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct model, optimized for local execution using llama.cpp and compatible ecosystems.

📌 Version Notes

All quantizations were generated from the official FP16 weights.

Target: Efficient execution on consumer hardware, mobile/edge devices, and systems with limited memory.
Performance: The output quality (reasoning, coherence, and accuracy) is strictly dependent on the base model's parameter scale (9B).

📊 Quantization Table

File	Method	Bit	Description
fp16.gguf	FP16	16-bit	Original Weights. No quantization applied. Maximum fidelity.
Q8_0.gguf	Q8_0	8-bit	Near-lossless. Practically identical to the original model with lower memory footprint.
Q5_K_M.gguf	Q5_K_M	5-bit	High Precision. Minimizes quantization error for critical tasks.
Q4_K_M.gguf	Q4_K_M	4-bit	Recommended. Best balance between speed and performance.
Q4_K_S.gguf	Q4_K_S	4-bit	Fast/Small. Optimized for maximum throughput and low RAM usage.

🛠️ Technical Details

Quantization Date: 2026-03-13
Tool used: llama-quantize (llama.cpp)
Method: K-Quantization (optimized for AVX2/AVX-512 and modern GPU architectures).

🚀 How to Use

Start a local OpenAI-compatible server with a web UI:

llama.cpp (CLI) using model from HuggingFace

./llama-cli -hf daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M -p "User: Hello! Assistant:" -n 512 --temp 0.7

llama.cpp (CLI) using downloaded model

./llama-cli -m path/to/DeepSeek-Coder-V2-Lite-Instruct_Q4_K_M.gguf -p "User: Hello! Assistant:" -n 512 --temp 0.7

llama.cpp (SERVER) using model from HuggingFace

./llama-server -hf daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF:Q4_K_M --port 8080 -c 4096

llama.cpp (SERVER) using downloaded model

./llama-server -m /path/to/DeepSeek-Coder-V2-Lite-Instruct_Q4_K_M.gguf --port 8080 -c 4096

Downloads last month: 322

GGUF

Model size

16B params

Architecture

deepseek2

Hardware compatibility

4-bit

5-bit

8-bit

View +1 variant

Model tree for daniloreddy/DeepSeek-Coder-V2-Lite-Instruct_GGUF

Base model

deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct

Quantized

(64)

this model