metadata

base_model: microsoft/Phi-3.5-mini-instruct
language:
  - multilingual
library_name: transformers
license: mit
license_link: https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
  - nlp
  - code
  - llama-cpp
  - gguf-my-repo
widget:
  - messages:
      - role: user
        content: Can you provide ways to eat combinations of bananas and dragonfruits?

redhat6/Phi-3.5-mini-instruct-Q4_K_M-GGUF

This model was converted to GGUF format from microsoft/Phi-3.5-mini-instruct 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 Ollama

Create the model in Ollama

ollama create Phi-3.5-mini-instruct-Q4_K_M-GGUF -f Modelfile

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 redhat6/Phi-3.5-mini-instruct-Q4_K_M-GGUF --hf-file phi-3.5-mini-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo redhat6/Phi-3.5-mini-instruct-Q4_K_M-GGUF --hf-file phi-3.5-mini-instruct-q4_k_m.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 redhat6/Phi-3.5-mini-instruct-Q4_K_M-GGUF --hf-file phi-3.5-mini-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo redhat6/Phi-3.5-mini-instruct-Q4_K_M-GGUF --hf-file phi-3.5-mini-instruct-q4_k_m.gguf -c 2048