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README.md

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+---
+license: apache-2.0
+language:
+- en
+datasets:
+- togethercomputer/RedPajama-Data-1T
+- togethercomputer/RedPajama-Data-Instruct
+widget:
+- text: |-
+    Label the tweets as either 'positive', 'negative', 'mixed', or 'neutral': 
+
+    Tweet: I can say that there isn't anything I would change.
+    Label: positive
+
+    Tweet: I'm not sure about this.
+    Label: neutral
+
+    Tweet: I liked some parts but I didn't like other parts.
+    Label: mixed
+
+    Tweet: I think the background image could have been better.
+    Label: negative
+
+    Tweet: 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
+---
+
+# 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)

config.json

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+{
+  "_name_or_path": "togethercomputer/RedPajama-INCITE-7B-Instruct",
+  "architectures": [
+    "GPTNeoXForCausalLM"
+  ],
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "hidden_act": "gelu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "gpt_neox",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 1.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.1",
+  "use_cache": true,
+  "use_parallel_residual": false,
+  "vocab_size": 50432
+}

ct_output_models/config.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "layer_norm_epsilon": null,
+  "unk_token": "<|endoftext|>"
+}

ct_output_models/model.bin

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+oid sha256:b8570b40313c8703bb9051c5f4ef13f58f658bdac4a4feb8d19e3df3d9c23ba7
+size 6867593490

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.28.1"
+}

special_tokens_map.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 2048,
+  "tokenizer_class": "GPTNeoXTokenizer",
+  "unk_token": "<|endoftext|>"
+}