README.md

---
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':\
    \ \n\nSentence: I can say that there isn't anything I would change.\nLabel: positive\n\
    \nSentence: I'm not sure about this.\nLabel: neutral\n\nSentence: I liked some\
    \ parts but I didn't like other parts.\nLabel: mixed\n\nSentence: I think the\
    \ background image could have been better.\nLabel: negative\n\nSentence: I really\
    \ like it.\nLabel:"
  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`](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Instruct) 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/togethercomputer/RedPajama-INCITE-7B-Instruct) for more details on the model.
## Use with llama.cpp

Install llama.cpp through brew.

```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.

CLI:

```bash
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:

```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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
```