metadata

license: openrail
inference:
  parameters:
    num_beams: 3
    num_beam_groups: 3
    num_return_sequences: 1
    repetition_penalty: 3
    diversity_penalty: 3.01
    no_repeat_ngram_size: 2
    temperature: 0.8
    max_length: 64
widget:
  - text: >-
      paraphraser: Learn to build generative AI applications with an expert AWS
      instructor with the 2-day Developing Generative AI Applications on AWS
      course.
    example_title: AWS course
  - text: >-
      paraphraser: In healthcare, Generative AI can help generate synthetic
      medical data to train machine learning models, develop new drug
      candidates, and design clinical trials.
    example_title: Generative AI
  - text: >-
      paraphraser: By leveraging prior model training through transfer learning,
      fine-tuning can reduce the amount of expensive computing power and labeled
      data needed to obtain large models tailored to niche use cases and
      business needs.
    example_title: Fine Tuning
tags:
  - llama-cpp
  - gguf-my-repo
base_model: Ateeqq/Text-Rewriter-Paraphraser

sizrox/Text-Rewriter-Paraphraser-Q4_K_M-GGUF

This model was converted to GGUF format from Ateeqq/Text-Rewriter-Paraphraser 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 sizrox/Text-Rewriter-Paraphraser-Q4_K_M-GGUF --hf-file text-rewriter-paraphraser-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo sizrox/Text-Rewriter-Paraphraser-Q4_K_M-GGUF --hf-file text-rewriter-paraphraser-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 sizrox/Text-Rewriter-Paraphraser-Q4_K_M-GGUF --hf-file text-rewriter-paraphraser-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo sizrox/Text-Rewriter-Paraphraser-Q4_K_M-GGUF --hf-file text-rewriter-paraphraser-q4_k_m.gguf -c 2048