metadata

base_model: google/flan-t5-large
datasets:
  - svakulenk0/qrecc
  - taskmaster2
  - djaym7/wiki_dialog
  - deepmind/code_contests
  - lambada
  - gsm8k
  - aqua_rat
  - esnli
  - quasc
  - qed
language:
  - en
  - fr
  - ro
  - de
  - multilingual
license: apache-2.0
tags:
  - text2text-generation
  - llama-cpp
  - gguf-my-repo
widget:
  - text: 'Translate to German:  My name is Arthur'
    example_title: Translation
  - text: >-
      Please answer to the following question. Who is going to be the next
      Ballon d'or?
    example_title: Question Answering
  - text: >-
      Q: Can Geoffrey Hinton have a conversation with George Washington? Give
      the rationale before answering.
    example_title: Logical reasoning
  - text: >-
      Please answer the following question. What is the boiling point of
      Nitrogen?
    example_title: Scientific knowledge
  - text: >-
      Answer the following yes/no question. Can you write a whole Haiku in a
      single tweet?
    example_title: Yes/no question
  - text: >-
      Answer the following yes/no question by reasoning step-by-step. Can you
      write a whole Haiku in a single tweet?
    example_title: Reasoning task
  - text: 'Q: ( False or not False or False ) is? A: Let''s think step by step'
    example_title: Boolean Expressions
  - text: >-
      The square root of x is the cube root of y. What is y to the power of 2,
      if x = 4?
    example_title: Math reasoning
  - text: >-
      Premise:  At my age you will probably have learnt one lesson. Hypothesis: 
      It's not certain how many lessons you'll learn by your thirties. Does the
      premise entail the hypothesis?
    example_title: Premise and hypothesis

tianlp/flan-t5-large-Q4_K_M-GGUF

This model was converted to GGUF format from google/flan-t5-large 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 tianlp/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo tianlp/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-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 tianlp/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo tianlp/flan-t5-large-Q4_K_M-GGUF --hf-file flan-t5-large-q4_k_m.gguf -c 2048