README.md

---
language:
- en
license: apache-2.0
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
model-index:
- name: RedPajama-INCITE-Instruct-7B-v0.1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 44.11
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 72.02
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 37.62
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 33.96
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 64.96
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 1.59
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1
      name: Open LLM Leaderboard
---

# RedPajama-INCITE-7B-Instruct

RedPajama-INCITE-7B-Instruct was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION. 

The model was fine-tuned for few-shot applications on the data of [GPT-JT](https://huggingface.co/togethercomputer/GPT-JT-6B-v1), with exclusion of tasks that overlap with the HELM core scenarios.

  - Base Model: [RedPajama-INCITE-7B-Base](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base)
  - Instruction-tuned Version: [RedPajama-INCITE-7B-Instruct](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Instruct)
  - Chat Version: [RedPajama-INCITE-7B-Chat](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Chat)


## Model Details
- **Developed by**: Together Computer.
- **Model type**: Language Model
- **Language(s)**: English
- **License**: Apache 2.0
- **Model Description**: A 6.9B parameter pretrained language model.

# Quick Start

Please note that the model requires `transformers` version >= 4.25.1.

## GPU Inference

This requires a GPU with 16GB memory.

```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

MIN_TRANSFORMERS_VERSION = '4.25.1'

# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'

# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct", torch_dtype=torch.float16)
model = model.to('cuda:0')
# infer
prompt = "Q: The capital of France is?\nA:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
    **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Paris
"""
```

## GPU Inference in Int8

This requires a GPU with 12GB memory.

To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command:

```bash
pip install accelerate
pip install bitsandbytes
```

Then you can run inference with int8 as follows:

```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

MIN_TRANSFORMERS_VERSION = '4.25.1'

# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'

# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True)

# infer
prompt = "Q: The capital of France is?\nA:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
    **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Paris
"""
```

## CPU Inference

```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

MIN_TRANSFORMERS_VERSION = '4.25.1'

# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'

# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct", torch_dtype=torch.bfloat16)
# infer
prompt = "Q: The capital of France is?\nA:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
    **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Paris
"""
```

Please note that since `LayerNormKernelImpl` is not implemented in fp16 for CPU, we use `bfloat16` for CPU inference.


# Uses

## Direct Use 

Excluded uses are described below.

### Misuse, Malicious Use, and Out-of-Scope Use

It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.

#### Out-of-Scope Use

RedPajama-INCITE-7B-Instruct is a language model and may not perform well for other use cases outside of its intended scope. 
For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society. 
It is important to consider the limitations of the model and to only use it for its intended purpose.

#### Misuse and Malicious Use

RedPajama-INCITE-7B-Instruct is designed for language modeling.
Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the project.

Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

- Generating fake news, misinformation, or propaganda
- Promoting hate speech, discrimination, or violence against individuals or groups
- Impersonating individuals or organizations without their consent
- Engaging in cyberbullying or harassment
- Defamatory content
- Spamming or scamming
- Sharing confidential or sensitive information without proper authorization
- Violating the terms of use of the model or the data used to train it
- Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming

## Limitations

RedPajama-INCITE-7B-Instruct, like other language models, has limitations that should be taken into consideration. 
For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data. 
We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.

## Training

**Training Data**

Please refer to [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)

**Training Procedure**

- **Hardware:** 8 A100
- **Optimizer:** Adam
- **Gradient Accumulations**: 1
- **Num of Tokens:** 1B tokens
- **Learning rate:** 1e-5

## Community

Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-Instruct-7B-v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |42.38|
|AI2 Reasoning Challenge (25-Shot)|44.11|
|HellaSwag (10-Shot)              |72.02|
|MMLU (5-Shot)                    |37.62|
|TruthfulQA (0-shot)              |33.96|
|Winogrande (5-shot)              |64.96|
|GSM8k (5-shot)                   | 1.59|