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README.md

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+---
+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
+```