README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - llama-cpp
  - gguf-my-repo
datasets:
  - togethercomputer/RedPajama-Data-1T
  - togethercomputer/RedPajama-Data-Instruct
widget:
  - text: >-
      Label the sentences as either 'positive', 'negative', 'mixed', or
      'neutral': 


      Sentence: I can say that there isn't anything I would change.

      Label: positive


      Sentence: I'm not sure about this.

      Label: neutral


      Sentence: I liked some parts but I didn't like other parts.

      Label: mixed


      Sentence: I think the background image could have been better.

      Label: negative


      Sentence: I really like it.

      Label:
    example_title: Sentiment Analysis
  - text: |-
      Please answer the following question:

      Question: What is the capital of Canada?
      Answer: Ottawa

      Question: What is the currency of Switzerland?
      Answer: Swiss franc

      Question: In which country is Wisconsin located?
      Answer:
    example_title: Question Answering
  - text: >-
      Given a news article, classify its topic.

      Possible labels: 1. World 2. Sports 3. Business 4. Sci/Tech


      Article: A nearby star thought to harbor comets and asteroids now appears
      to be home to planets, too.

      Label: Sci/Tech


      Article: Soaring crude prices plus worries about the economy and the
      outlook for earnings are expected to hang over the stock market next week
      during the depth of the summer doldrums.

      Label: Business


      Article: Murtagh a stickler for success Northeastern field hockey coach
      Cheryl Murtagh doesn't want the glare of the spotlight that shines on her
      to detract from a team that has been the America East champion for the
      past three years and has been to the NCAA tournament 13 times.

      Label::
    example_title: Topic Classification
  - text: |-
      Paraphrase the given sentence into a different sentence.

      Input: Can you recommend some upscale restaurants in New York?
      Output: What upscale restaurants do you recommend in New York?

      Input: What are the famous places we should not miss in Paris?
      Output: Recommend some of the best places to visit in Paris?

      Input: Could you recommend some hotels that have cheap price in Zurich?
      Output:
    example_title: Paraphrasing
  - text: >-
      Given a review from Amazon's food products, the task is to generate a
      short summary of the given review in the input.


      Input: I have bought several of the Vitality canned dog food products and
      have found them all to be of good quality. The product looks more like a
      stew than a processed meat and it smells better. My Labrador is finicky
      and she appreciates this product better than most.

      Output: Good Quality Dog Food


      Input: Product arrived labeled as Jumbo Salted Peanuts...the peanuts were
      actually small sized unsalted. Not sure if this was an error or if the
      vendor intended to represent the product as 'Jumbo'.

      Output: Not as Advertised


      Input: My toddler loves this game to a point where he asks for it. That's
      a big thing for me. Secondly, no glitching unlike one of their competitors
      (PlayShifu). Any tech I don’t have to reach out to support for help is a
      good tech for me. I even enjoy some of the games and activities in this.
      Overall, this is a product that shows that the developers took their time
      and made sure people would not be asking for refund. I’ve become bias
      regarding this product and honestly I look forward to buying more of this
      company’s stuff. Please keep up the great work.

      Output:
    example_title: Text Summarization
  - text: |-
      Identify which sense of a word is meant in a given context.

      Context: The river overflowed the bank.
      Word: bank
      Sense: river bank

      Context: A mouse takes much more room than a trackball.
      Word: mouse
      Sense: computer mouse

      Context: The bank will not be accepting cash on Saturdays.
      Word: bank
      Sense: commercial (finance) banks

      Context: Bill killed the project
      Word: kill
      Sense:
    example_title: Word Sense Disambiguation
  - text: >-
      Given a pair of sentences, choose whether the two sentences agree
      (entailment)/disagree (contradiction) with each other.

      Possible labels: 1. entailment 2. contradiction


      Sentence 1: The skier was on the edge of the ramp. Sentence 2: The skier
      was dressed in winter clothes.

      Label: entailment


      Sentence 1: The boy skated down the staircase railing. Sentence 2: The boy
      is a newbie skater.

      Label: contradiction


      Sentence 1: Two middle-aged people stand by a golf hole. Sentence 2: A
      couple riding in a golf cart.

      Label:
    example_title: Natural Language Inference
inference:
  parameters:
    temperature: 0.7
    top_p: 0.7
    top_k: 50
    max_new_tokens: 128

DavidAU/RedPajama-INCITE-7B-Instruct-Q6_K-GGUF

This model was converted to GGUF format from togethercomputer/RedPajama-INCITE-7B-Instruct 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.

brew install ggerganov/ggerganov/llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo DavidAU/RedPajama-INCITE-7B-Instruct-Q6_K-GGUF --model redpajama-incite-7b-instruct.Q6_K.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo DavidAU/RedPajama-INCITE-7B-Instruct-Q6_K-GGUF --model redpajama-incite-7b-instruct.Q6_K.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

git clone https://github.com/ggerganov/llama.cpp &&             cd llama.cpp &&             make &&             ./main -m redpajama-incite-7b-instruct.Q6_K.gguf -n 128