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.
📊 Evaluation Results
Falcon-RW-1B-Instruct-OpenOrca is the #1 ranking model on Open LLM Leaderboard in ~1.5B parameters category!
| Metric | falcon-rw-1b-instruct-openorca | falcon-rw-1b |
|---|---|---|
| ARC | 34.56 | 35.07 |
| HellaSwag | 60.93 | 63.56 |
| MMLU | 28.77 | 25.28 |
| TruthfulQA | 37.42 | 35.96 |
| Winogrande | 60.69 | 62.04 |
| GSM8K | 1.21 | 0.53 |
| DROP | 21.94 | 4.64 |
| Average | 35.08 | 32.44 |
🚀 Motivations
- To create a smaller, open-source, instruction-finetuned, ready-to-use model accessible for users with limited computational resources (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.
📬 Contact
For further inquiries or feedback, please contact at eric.fu96@aol.com.