Model Card
We provide a low-rank adapter for an instruction-tuned 6.7B-parameter GPT3-style language model.
Example instruction:
Write a list of things I need to do to plan a watch party for Game 4 of the Knicks-Cavs playoff series.
Corresponding response from the model:
1. Find out where the game will be played and purchase tickets.
2. Decide on food, drinks, decorations, and entertainment.
3. Plan the route to get there.
4. Arrange transportation.
5. Organize games and activities for guests.
6. Make sure everyone has their tickets.
7. Set up the venue.
8. Prepare for the event.
9. Clean up after the event.
10. Celebrate with friends and family.
Quickstart
Setting up the model using transformers and peft:
from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("cerebras/Cerebras-GPT-6.7B")
tokenizer.pad_token_id = 0
model = AutoModelForCausalLM.from_pretrained(
"cerebras/Cerebras-GPT-6.7B",
load_in_8bit=True,
device_map="auto",
)
model = PeftModel.from_pretrained(
model,
"lucasmccabe-lmi/GPT-6.7B-CleanedAlpaca-1"
)
Prompting
For instructions that do not require extraneous inputs, the recommended prompt is:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
< -- instruction goes here --- >
### Response:
For instructions that do require extraneous inputs, the recommended prompt is:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
< -- instruction goes here -- >
### Input:
< -- extraneous input goes here -- >
### Response:
Since the model performs causal language modeling, the model's response to the prompt is the text completing the sequence beginning with the prompt.
Instruction-Tuning
This model was instruction-tuned on a cleaned version of the Stanford Alpaca dataset, consisting of 52k post-processed instruction-input-output triplets derived from OpenAI's text-davinci-003.
Epochs: 3
Batch size: 128
Cutoff length: 512
Learning rate: 2e-5
LoRA r: 4
LoRA alpha: 16
LoRA dropout: 0.05
LoRA target modules:
c_attnDataset: yahma/alpaca-cleaned
License: The instruction-tuning data is subject to the Creative Commons 4.0 license.
Base Model
This model was instruction-tuned from a 6.7B variant from the Cerebras-GPT family. These models were pre-trained to the "Chinchilla-optimal" 20*6.7B tokens from EleutherAI/The Pile.
- Repository: cerebras/Cerebras-GPT-6.7B
- Paper: arxiv:2304.03208
- License: The base model is subject to the Apache 2.0 license.
- Model type: Transformer-based Language Model
Software
We used LMI's internal liger library, which is built on PyTorch and the excellent Hugging Face stack (transformers, accelerate, etc.).
Licensing Information
We release this adapter under the Creative Commons NonCommercial (CC BY-NC 4.0) license.
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