falcon-rw-1b-instruct-openorca/README.md

language, license, tags, datasets, pipeline_tag, inference, model-index

language	license	tags	datasets	pipeline_tag	inference	model-index
en	apache-2.0	text-generation-inference	Open-Orca/SlimOrca	text-generation	false	name results falcon-rw-1b-instruct-openorca task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 34.56 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-instruct-openorca Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 60.93 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-instruct-openorca Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 28.77 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-instruct-openorca Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 37.42 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-instruct-openorca Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 60.69 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-instruct-openorca Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 3.41 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ericzzz/falcon-rw-1b-instruct-openorca Open LLM Leaderboard

🌟 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.

✨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

1. To create a smaller, open-source, instruction-finetuned, ready-to-use model accessible for users with limited computational resources (lower-end consumer GPUs).
2. 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.

⚠️ Limitations

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.

📬 Contact

For further inquiries or feedback, please contact at eric.fu96@aol.com.

Open LLM Leaderboard Evaluation Results

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