README.md

---
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`](https://huggingface.co/Falconsai/text_summarization) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Falconsai/text_summarization) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
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:
```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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"
```
or 
```
./llama-server --hf-repo tonyc666/text_summarization-Q4_K_M-GGUF --hf-file text_summarization-q4_k_m.gguf -c 2048
```