Instructions to use k0valik-21/Carnice-27b-Q3_K_S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k0valik-21/Carnice-27b-Q3_K_S-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="k0valik-21/Carnice-27b-Q3_K_S-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("k0valik-21/Carnice-27b-Q3_K_S-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use k0valik-21/Carnice-27b-Q3_K_S-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 k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: llama cli -hf k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: llama cli -hf k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_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 k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: ./llama-cli -hf k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_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 k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S
Use Docker
docker model run hf.co/k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S
- LM Studio
- Jan
- vLLM
How to use k0valik-21/Carnice-27b-Q3_K_S-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "k0valik-21/Carnice-27b-Q3_K_S-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "k0valik-21/Carnice-27b-Q3_K_S-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S
- SGLang
How to use k0valik-21/Carnice-27b-Q3_K_S-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "k0valik-21/Carnice-27b-Q3_K_S-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "k0valik-21/Carnice-27b-Q3_K_S-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "k0valik-21/Carnice-27b-Q3_K_S-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "k0valik-21/Carnice-27b-Q3_K_S-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use k0valik-21/Carnice-27b-Q3_K_S-GGUF with Ollama:
ollama run hf.co/k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S
- Unsloth Desktop
- Docker Model Runner
How to use k0valik-21/Carnice-27b-Q3_K_S-GGUF with Docker Model Runner:
docker model run hf.co/k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S
- Lemonade
How to use k0valik-21/Carnice-27b-Q3_K_S-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S
Run and chat with the model
lemonade run user.Carnice-27b-Q3_K_S-GGUF-Q3_K_S
List all available models
lemonade list
- Atomic Chat
Run and chat with the model
lemonade run user.Carnice-27b-Q3_K_S-GGUF-Q3_K_SList all available models
lemonade listk0valik-21/Carnice-27b-Q3_K_S-GGUF
This model was converted to GGUF format from kai-os/Carnice-27b using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo k0valik-21/Carnice-27b-Q3_K_S-GGUF --hf-file carnice-27b-q3_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo k0valik-21/Carnice-27b-Q3_K_S-GGUF --hf-file carnice-27b-q3_k_s.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo k0valik-21/Carnice-27b-Q3_K_S-GGUF --hf-file carnice-27b-q3_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo k0valik-21/Carnice-27b-Q3_K_S-GGUF --hf-file carnice-27b-q3_k_s.gguf -c 2048
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Pull the model
# Download Lemonade from https://lemonade-server.ai/lemonade pull k0valik-21/Carnice-27b-Q3_K_S-GGUF:Q3_K_S