Dolphy-1.0-Q4_0-GGUF/README.md

tags, base_model, pipeline_tag, license, datasets

tags	base_model	pipeline_tag	license	datasets
text-generation-inference llama-cpp gguf-my-repo	Dolphy-AI/Dolphy-1.0	text-generation	apache-2.0	Qwen/PolyMath Open-Orca/OpenOrca OpenAssistant/oasst1 tatsu-lab/alpaca TIGER-Lab/MathInstruct QuixiAI/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split allenai/openbookqa euclaise/writingprompts HuggingFaceH4/CodeAlpaca_20K allenai/c4 HuggingFaceH4/ultrachat_200k roskoN/dailydialog bavard/personachat_truecased

PJRM/Dolphy-1.0-Q4_0-GGUF

This model was converted to GGUF format from Dolphy-AI/Dolphy-1.0 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 PJRM/Dolphy-1.0-Q4_0-GGUF --hf-file dolphy-1.0-q4_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo PJRM/Dolphy-1.0-Q4_0-GGUF --hf-file dolphy-1.0-q4_0.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 PJRM/Dolphy-1.0-Q4_0-GGUF --hf-file dolphy-1.0-q4_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo PJRM/Dolphy-1.0-Q4_0-GGUF --hf-file dolphy-1.0-q4_0.gguf -c 2048