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ericzzz/falcon-rw-1b-instruct-openorca
falcon-rw-1b-instruct-openorca is a text generation model from ericzzz. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
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.
Downloads · 30 days
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From the Hugging Face model README
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.
✨Check out our new conversational model Falcon-RW-1B-Chat!✨
📊 Evaluation Results
Falcon-RW-1B-Instruct-OpenOrca was the #1 ranking model (unfortunately not anymore) on Open LLM Leaderboard in ~1.5B parameters category! A detailed result can be found here.
| 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 | 3.41 | 0.53 |
| Average | 37.63 | 37.07 |
🚀 Motivations
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.
This model may generate inaccurate or misleading information and is prone to hallucination, creating plausible but false narratives. It lacks the ability to discern factual content from fiction and may inadvertently produce biased, harmful or offensive content. Its understanding of complex, nuanced queries is limited. Users should be aware of this and verify any information obtained from the model.
The model is provided 'as is' without any warranties, and the creators are not liable for any damages arising from its use. Users are responsible for their interactions with the model.
For further inquiries or feedback, please contact at [email protected].
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 37.63 |
| AI2 Reasoning Challenge (25-Shot) | 34.56 |
| HellaSwag (10-Shot) | 60.93 |
| MMLU (5-Shot) | 28.77 |
| TruthfulQA (0-shot) | 37.42 |
| Winogrande (5-shot) | 60.69 |
| GSM8k (5-shot) | 3.41 |