Text Generation
GGUF
quantized
GGUF
quantization
imat
imatrix
static
8bit
6bit
5bit
4bit
3bit
2bit
1bit
conversational
Instructions to use legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use legraphista/DeepSeek-Coder-V2-Instruct-IMat-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 legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S
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 legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S
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 legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S
Use Docker
docker model run hf.co/legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "legraphista/DeepSeek-Coder-V2-Instruct-IMat-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": "legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S
- Ollama
How to use legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF with Ollama:
ollama run hf.co/legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S
- Unsloth Desktop
- Docker Model Runner
How to use legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF with Docker Model Runner:
docker model run hf.co/legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S
- Lemonade
How to use legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull legraphista/DeepSeek-Coder-V2-Instruct-IMat-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.DeepSeek-Coder-V2-Instruct-IMat-GGUF-Q4_K_S
List all available models
lemonade list
- Atomic Chat
Ctrl+K
- DeepSeek-Coder-V2-Instruct.IQ1_M
- DeepSeek-Coder-V2-Instruct.IQ1_S
- DeepSeek-Coder-V2-Instruct.IQ2_M
- DeepSeek-Coder-V2-Instruct.IQ2_S
- DeepSeek-Coder-V2-Instruct.IQ2_XS
- DeepSeek-Coder-V2-Instruct.IQ2_XXS
- DeepSeek-Coder-V2-Instruct.IQ3_M
- DeepSeek-Coder-V2-Instruct.IQ3_S
- DeepSeek-Coder-V2-Instruct.IQ3_XS
- DeepSeek-Coder-V2-Instruct.IQ3_XXS
- DeepSeek-Coder-V2-Instruct.IQ4_NL
- DeepSeek-Coder-V2-Instruct.IQ4_XS
- DeepSeek-Coder-V2-Instruct.Q2_K
- DeepSeek-Coder-V2-Instruct.Q2_K_S
- DeepSeek-Coder-V2-Instruct.Q3_K
- DeepSeek-Coder-V2-Instruct.Q3_K_L
- DeepSeek-Coder-V2-Instruct.Q3_K_S
- DeepSeek-Coder-V2-Instruct.Q4_K
- DeepSeek-Coder-V2-Instruct.Q4_K_S
- DeepSeek-Coder-V2-Instruct.Q5_K
- DeepSeek-Coder-V2-Instruct.Q5_K_S
- DeepSeek-Coder-V2-Instruct.Q6_K
- DeepSeek-Coder-V2-Instruct.Q8_0
- 16.2 kB
- 10.1 kB
- 456 MB xet
- 280 kB
- 12.5 kB