Text Generation
GGUF
code
quantized
GGUF
imatrix
quantization
imat
static
16bit
8bit
6bit
5bit
4bit
3bit
2bit
1bit
conversational
Instructions to use legraphista/Nxcode-CQ-7B-orpo-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/Nxcode-CQ-7B-orpo-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/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf legraphista/Nxcode-CQ-7B-orpo-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/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama cli -hf legraphista/Nxcode-CQ-7B-orpo-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/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf legraphista/Nxcode-CQ-7B-orpo-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/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S
Use Docker
docker model run hf.co/legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S
- Llama new
- LM Studio
- Jan
- vLLM
How to use legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "legraphista/Nxcode-CQ-7B-orpo-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/Nxcode-CQ-7B-orpo-IMat-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S
- Ollama
How to use legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF with Ollama:
ollama run hf.co/legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S
- Unsloth Desktop
- Docker Model Runner
How to use legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF with Docker Model Runner:
docker model run hf.co/legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S
- Lemonade
How to use legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.Nxcode-CQ-7B-orpo-IMat-GGUF-Q4_K_S
List all available models
lemonade list
- Atomic Chat
Download Nxcode-CQ-7B-orpo.Q2_K_S.gguf from legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 3.03 GB
-
https://huggingface.co/legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF/resolve/main/Nxcode-CQ-7B-orpo.Q2_K_S.gguf
- Command line
-
hf download hf://legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF/Nxcode-CQ-7B-orpo.Q2_K_S.gguf
-
curl -L -o Nxcode-CQ-7B-orpo.Q2_K_S.gguf https://huggingface.co/legraphista/Nxcode-CQ-7B-orpo-IMat-GGUF/resolve/main/Nxcode-CQ-7B-orpo.Q2_K_S.gguf
3.03 GB
- Xet hash:
- dfc084ec01ac76b49a480d25c84cbbceff7c3b385f6816a945af80cc77d41876
- Size of remote file:
- 3.03 GB
- SHA256:
- e1a76f9fd46f4b72e235ec4f0506cd96f1c1bd07c1b8329a4a8ad83d6e36b3ed
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