Instructions to use mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-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 mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-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 mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-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 mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-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 mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k-GGUF / Deepseek-Coder-V2-Lite-13B-Instruct-sft-math7k.Q3_K_S.gguf

mradermacher

uploaded from marco

22d42df verified about 1 year ago

Download

History Blame

7.49 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 3c8e25d1c1af33cd4603dd58bca5f9718f08eb28b23c11a1990421a20f848aee
Size of remote file:: 7.49 GB
SHA256:: cced039746ad93589d77e3456bafe4c2a817f2ba8111f669ae3a895755f81089

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.