How to use dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B", device_map="auto")
How to use dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16 # Run inference directly in the terminal: llama cli -hf dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16 # Run inference directly in the terminal: llama cli -hf dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16
# 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 dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16 # Run inference directly in the terminal: ./llama-cli -hf dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16
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 dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16
docker model run hf.co/dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16
How to use dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B with Ollama:
ollama run hf.co/dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16
How to use dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B with Docker Model Runner:
How to use dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dat-lequoc/fast-apply_gguf-16bit_0-v0.12-7B:F16
lemonade run user.fast-apply_gguf-16bit_0-v0.12-7B-F16
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