metadata
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- text-generation-inference
🌟 Falcon-RW-1B-Instruct-OpenOrca
Falcon-RW-1B-Instruct-OpenOrca is a 1B parameter, causal decoder-only model based on Falcon-RW-1B and finetuned on the Open-Orca/SlimOrca dataset.
🚀 Motivations
- To create a smaller, open-source, instruction-finetuned, ready-to-use model accessible for users with limited computational resources; to make a decent ready-to-use model for running on lower-end consumer GPUs.
- To harness the strength of Falcon-RW-1B, a competitive model in its own right, and enhance its capabilities with instruction finetuning.
📖 How to Use
The model operates with a structured prompt format, incorporating <SYS>, <INST>, and <RESP> tags to demarcate different parts of the input. The system message and instruction are placed within these tags, with the <RESP> tag triggering the model's response.
📝 Example Code
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = 'ericzzz/falcon-rw-1b-instruct-openorca'
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
device_map="auto",
)
system_message = "You are a helpful assistant. Give short answers."
instruction = "What is AI? Give some examples."
prompt = f"<SYS> {system_message} <INST> {instruction} <RESP> "
response = pipeline(
prompt,
max_length=200,
repetition_penalty=1.05
)
print(response[0]['generated_text'])
# AI, or Artificial Intelligence, refers to the ability of machines and software to perform tasks that require human intelligence, such as learning, reasoning, and problem-solving. It can be used in various fields like computer science, engineering, medicine, and more. Some common applications include image recognition, speech translation, and natural language processing.