Instructions to use Chopinab/clef-flash-Q2_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chopinab/clef-flash-Q2_K-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Chopinab/clef-flash-Q2_K-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Chopinab/clef-flash-Q2_K-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Chopinab/clef-flash-Q2_K-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 Chopinab/clef-flash-Q2_K-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf Chopinab/clef-flash-Q2_K-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Chopinab/clef-flash-Q2_K-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf Chopinab/clef-flash-Q2_K-GGUF:Q2_K
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Chopinab/clef-flash-Q2_K-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama cli -hf Chopinab/clef-flash-Q2_K-GGUF:Q2_K
Use Docker
docker model run hf.co/Chopinab/clef-flash-Q2_K-GGUF:Q2_K
- Llama new
- LM Studio
- Jan
- vLLM
How to use Chopinab/clef-flash-Q2_K-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Chopinab/clef-flash-Q2_K-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chopinab/clef-flash-Q2_K-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Chopinab/clef-flash-Q2_K-GGUF:Q2_K
- SGLang
How to use Chopinab/clef-flash-Q2_K-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 "Chopinab/clef-flash-Q2_K-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": "Chopinab/clef-flash-Q2_K-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 "Chopinab/clef-flash-Q2_K-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": "Chopinab/clef-flash-Q2_K-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use Chopinab/clef-flash-Q2_K-GGUF with Ollama:
ollama run hf.co/Chopinab/clef-flash-Q2_K-GGUF:Q2_K
- Unsloth Desktop
- Docker Model Runner
How to use Chopinab/clef-flash-Q2_K-GGUF with Docker Model Runner:
docker model run hf.co/Chopinab/clef-flash-Q2_K-GGUF:Q2_K
- Lemonade
How to use Chopinab/clef-flash-Q2_K-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Chopinab/clef-flash-Q2_K-GGUF:Q2_K
Run and chat with the model
lemonade run user.clef-flash-Q2_K-GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
Chopinab/clef-flash-Q2_K-GGUF
This model was converted to GGUF format from Cloudflare/clef-flash 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 Chopinab/clef-flash-Q2_K-GGUF --hf-file clef-flash-q2_k.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Chopinab/clef-flash-Q2_K-GGUF --hf-file clef-flash-q2_k.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 Chopinab/clef-flash-Q2_K-GGUF --hf-file clef-flash-q2_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Chopinab/clef-flash-Q2_K-GGUF --hf-file clef-flash-q2_k.gguf -c 2048
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