Hugging Face Falcon-7B Large Language Model

A new large language model (LLM) called Falcon-7B was developed using a sizable text and code training dataset. One of the biggest LLMs ever made, it has 7 billion parameters. Falcon-7B is capable of doing a wide range of jobs, such as creating text, translating languages, creating other types of creative content, and providing you with helpful answers to your inquiries.

What is Falcon-7B

The Falcon-7B model, developed by TII, is a 7 billion parameter causal decoder-only model that was trained on 1,500 billion tokens of the RefinedWeb that had been supplemented with selected corpora. It is distributed under the terms of the Apache 2.0 license. Falcon-7B is a potent tool that may be applied to a wide range of activities, including creative writing, machine translation, and natural language processing.

Why use the Falcon-7B

A model called Falcon-7B is claimed to perform better than related open-source models like MPT-7B, StableLM, and RedPajama. It was trained using a substantial quantity of data, namely 1,500B tokens from RefinedWeb that had been improved by curated corpora. The utilization of such a huge and varied dataset could be a factor in the performance improvement.

The Falcon-7B’s architecture has been enhanced for inference. It uses the multiquery technique introduced by Shazeer et al. in 2019 and the FlashAttention technique introduced by Dao et al. in 2022. These improvements are intended to improve the model’s capacity for query processing and response.

The licensing of the Falcon-7B is one noteworthy feature. It is made available under the open-source Apache 2.0 license, which does not impose any fees or limitations on commercial use. Those wishing to use the model for business applications may find the license flexibility helpful.

It’s important to keep in mind that Falcon-7B is referred to as a raw, pretrained model. This indicates that additional fine-tuning is often required to suit it to certain use scenarios. The suggestion is to investigate Falcon-7B-Instruct if you need a version of the model that is more suited for receiving general instructions in a chat style.

Usage of Falcon-7B

The reference to Falcon-40B as Falcon-7B’s big brother implies the existence of a more potent variant.

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "tiiuae/falcon-7b"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
sequences = pipeline(
   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
    max_length=200,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Uses

1. Direct Use:

• Research: With a basis for additional customization and fine-tuning for specific use cases like summarization, text generation, and chatbot development, Falcon-7B is appropriate for research on large language models.

2. Out-of-Scope Use:

• Production Use: It is not advised to use the Falcon-7B in production without properly evaluating the hazards and taking the required precautions.
• Irresponsible or Harmful Use: Avoid any use cases that could be viewed as reckless or potentially destructive.

3. Bias, Risks, and Limitations:

• Language Limitation: The Falcon-7B may not translate well to other languages because it was trained on data in English and French.
• Online Bias: Because it was developed using a sizable web corpus, Falcon-7B might contain prejudices and stereotypes that are frequently seen online.

4. Recommendations:

• Fine-tuning: Falcon-7B should be fine-tuned by users for their unique tasks of interest in order to improve performance and customize it to their domain or dataset.
• Risk Assessment: Before implementing Falcon-7B in production, risks should be adequately assessed, and potential harm should be carefully considered.
• Guardrails and Precautions: For the Falcon-7B to be used in real-world applications responsibly and ethically, the proper guardrails must be put in place, and the requisite safety measures must be taken.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "tiiuae/falcon-7b"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)
sequences = pipeline(
   "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
    max_length=200,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Training Details

1. Training Data
   • RefinedWeb: For the Falcon-7B to be used in real-world applications responsibly and ethically, the proper guardrails must be put in place, and the requisite safety measures must be taken.
   • Curated Corpora Enhancement: Curated corpora, or additional data that was carefully chosen and curated, were added to the RefinedWeb dataset to improve it.
   • Inspiration from The Pile: The Pile, a dataset presented by Gao et al. in 2020, served as an important source of inspiration for significant portions of the curated corpora used in Falcon-7B’s training.

Data source	Fraction	Tokens	Sources
RefinedWeb-English	79%	1,185B	massive web crawl
Books	7%	110B
Conversations	6%	85B	Reddit, StackOverflow, HackerNews
Code	3%	45B
RefinedWeb-French	3%	45B	massive web crawl
Technical	2%	30B	arXiv, PubMed, UPSTO, etc.

1. Training Procedure

Using ZeRO and a 2D parallelism technique (PP=2, DP=192), Falcon 7B was trained on 384 A100 40GB GPUs.

2. Training Hyperparameters

Hyperparameter	Value	Comment
Precision	bfloat16
Optimizer	AdamW
Learning rate	6e-4	4B tokens warm-up, cosine decay to 1.2e-5
Weight decay	1e-1
Z-loss	1e-4
Batch size	2304	30B tokens ramp-up

3. Speeds, Sizes, Times

Early March 2023 saw the start of training, which lasted roughly two weeks.

Technical Specifications of Falcon-7B

Model Architecture:

• A causal decoder-only model is Falcon 7B.
• The GPT-3 (Generative Pre-trained Transformer 3) model proposed in the 2020 paper by Brown et al. served as the foundation for the architecture.

Training Objective:

• The causal language modeling challenge that Falcon 7B is trained on involves predicting the subsequent token in a sequence given its previous context.

Architecture Enhancements:

• Positional Embeddings: The Falcon 7B uses rotational positional embeddings, which Su et al. first proposed in 2021. The relative positions of the tokens in the input sequence are better captured by these embeddings.
• Attention Mechanism: Two methods are used to improve the Falcon 7B’s attention mechanism. It makes use of FlashAttention (developed by Dao et al. in 2022) and multiquery attention (introduced by Shazeer et al. in 2019). These focus improvements make the model better at answering queries.
• Decoder-Block: Falcon 7B’s decoder-block uses parallel attention/MLP with a single layer normalization. Through the use of this configuration, the model is able to efficiently capture dependencies and carry out calculations.

Hyperparameter	Value	Comment
Layers	32
d_model	4544	Increased to compensate for multiquery
head_dim	64	Reduced to optimize for FlashAttention
Vocabulary	65024
Sequence length	2048

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Model Details

• Developed by: TII (Technology Innovation Institute) created the model. Their website is https://www.tii.ae, where you can learn more about them.
• Model type: A causal decoder-only model is Falcon 7B. This indicates that it is intended to produce text in response to an input or prompt.
• Language(s): French and English language processing activities are supported by Falcon-7B. It is able to understand both languages and produce text in them.
• License: The Apache 2.0 license governs the distribution of Falcon 7B. The commercial use is permitted under this permissive license without any limitations or royalties. For additional information on the permissible uses, please see the Apache 2.0 license in its entirety.

This article is to help you learn about the Falcon-7B. We trust that it has been helpful to you. Please feel free to share your thoughts and feedback in the comment section below.