metadata

license: apache-2.0
language:
  - en
pipeline_tag: summarization
widget:
  - text: >-
      Hugging Face: Revolutionizing Natural Language Processing Introduction In
      the rapidly evolving field of Natural Language Processing (NLP), Hugging
      Face has emerged as a prominent and innovative force. This article will
      explore the story and significance of Hugging Face, a company that has
      made remarkable contributions to NLP and AI as a whole. From its inception
      to its role in democratizing AI, Hugging Face has left an indelible mark
      on the industry.  The Birth of Hugging Face Hugging Face was founded in
      2016 by Clément Delangue, Julien Chaumond, and Thomas Wolf. The name
      Hugging Face was chosen to reflect the company's mission of making AI
      models more accessible and friendly to humans, much like a comforting hug.
      Initially, they began as a chatbot company but later shifted their focus
      to NLP, driven by their belief in the transformative potential of this
      technology. Transformative Innovations Hugging Face is best known for its
      open-source contributions, particularly the Transformers library. This
      library has become the de facto standard for NLP and enables researchers,
      developers, and organizations to easily access and utilize
      state-of-the-art pre-trained language models, such as BERT, GPT-3, and
      more. These models have countless applications, from chatbots and virtual
      assistants to language translation and sentiment analysis. 
example_title: Summarization Example 1
base_model: Falconsai/text_summarization
tags:
  - llama-cpp
  - gguf-my-repo

tonyc666/text_summarization-Q4_K_M-GGUF

This model was converted to GGUF format from Falconsai/text_summarization 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 tonyc666/text_summarization-Q4_K_M-GGUF --hf-file text_summarization-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo tonyc666/text_summarization-Q4_K_M-GGUF --hf-file text_summarization-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 tonyc666/text_summarization-Q4_K_M-GGUF --hf-file text_summarization-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo tonyc666/text_summarization-Q4_K_M-GGUF --hf-file text_summarization-q4_k_m.gguf -c 2048