Introduction

There's an exciting development on the frontlines of Natural Language Processing (NLP) that's set to redefine the benchmarks of language models. HuggingFace has recently unveiled its groundbreaking model, Falcon 180B. This phenomenal model boasts a staggering 180 billion parameters, trained on a jaw-dropping 3.5 trillion tokens from the RefinedWeb dataset. It marks one of the most expansive single-epoch pre-training exercises for an open-source model.

Understanding Falcon-180B

The Falcon 180B model is the newest entrant to the esteemed Falcon family models. It's essentially an upgraded version of the Falcon 40B model, with enhanced scalability features such as multiquery attention. The model has undergone training involving up to 4096 GPUs concurrently, utilizing a significant portion of web data sourced from the RefinedWeb dataset.

Falcon 180B Performance Analysis

In terms of performance, Falcon 180B has established itself as the frontrunner among open-access pre-trained models. Its stellar performance across various NLP tasks brings it at par with leading proprietary models. Falcon 180B has set a new record in terms of scoring on the Hugging Face Leaderboard, surpassing scores of other pre-trained models.

Getting Started with Falcon 180B

Users keen on exploring Falcon 180B can interact with it via the Hugging Face Hub. However, given the colossal size of the model, certain hardware prerequisites must be met. The interaction with the chat model follows a simple conversation structure.

Falcon 180B and Transformers

With the release of Transformers 4.33, utilizing Falcon 180B has become significantly more streamlined. Plus, users can enjoy access to a myriad of other tools within the HF ecosystem, like training & inference scripts, examples, safe file format, assisted generation, and more.

Interacting with Falcon 180B

The chat model of Falcon 180B follows a straightforward conversation structure. Users initiate the conversation with a prompt while the model generates responses based on the given prompt.

Hardware Requirements for Falcon 180B

Given its enormous size, Falcon 180B demands specific hardware configurations for effective functionality. The specifics of these requirements have been detailed in the original blog post.

8-bit and 4-bit with bitsandbytes

Interestingly, the 8-bit and 4-bit quantized versions of Falcon 180B exhibit virtually no difference in evaluation when compared to the bfloat16 reference. This allows users to use the quantized version, thereby reducing hardware requirements.

Conclusion

In wrapping up, it's undeniable that Falcon 180B represents a significant milestone in language models. It competes neck-and-neck with some of the best proprietary models available today. The model opens up exciting opportunities for the global tech community to delve into its capabilities and leverage its advanced technology.

Code Sample: Interacting with Falcon 180B

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model_id = "tiiuae/falcon-180B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "My name is Pedro, I live in"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

output = model.generate(
    input_ids=inputs["input_ids"],
    attention_mask=inputs["attention_mask"],
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
    max_new_tokens=50,
)
output = output[0].to("cpu")
print(tokenizer.decode(output)

Note: Replace "huggingface/Falcon-180B" with the correct identifier for the Falcon 180B model. Given the requirement for specific hardware configurations due to the size of Falcon 180B, please ensure that your setup meets these requirements before attempting to run the model.