InstructEval Models Explained,
Falcon-7B-Instruct
Falcon-7B-Instruct is a 7B parameter causal decoder-only model built by TII based on Falcon-7B and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license. Falcon-7B LLMs are a type of artificial intelligence (AI) trained on massive text and code datasets. They can then be used to perform a variety of natural language processing (NLP) tasks, such as text generation, question answering, translation, summarization, and code generation.
Model Details
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An Overview of Falcon-7B-Instruct
Falcon-7B-Instruct was developed by the Text-Intelligence-Initiative (TII), a prestigious research group affiliated with the Technical University of Munich. The training process involved in the Falcon-7B-Instruct is a custom-distributed training codebase, Gigatron. Gigatron employs a cutting-edge 3D parallelism approach with advanced techniques such as ZeRO and high-performance Triton kernels (including FlashAttention) for optimal training efficiency. Falcon-7B-Instruct has exhibited commendable performance on benchmark tasks, including BLEU, GLUE, SQuAD, and RACE, demonstrating its capability to rival other large-scale language models.
It is trained on 1,500B tokens of RefinedWeb enhanced with curated corpora.
Efficient decoding
Falcon-7B-Instruct stands out as a causal decoder-only model, facilitating swift text decoding compared to models incorporating an encoder component, making it suitable for time-sensitive apps.
The memory usage of the model is significantly lower than that of GPT-3.
Robustness to noise
It demonstrates exceptional resilience to noise, ensuring the generation of text that retains grammatical accuracy and factual precision even in the presence of input text irregularities.
It offers an overall accuracy of 99% on different language-based tasks.
Open-source availability
It is openly accessible, making it valuable for researchers and developers. It fosters collaboration and innovation and enables users to explore the model's capabilities and build novel apps.
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Model Details
Falcon-7B-Instruct, developed under the guidance of Mohamed Elhoseiny, CEO and co-founder of Text-Intelligence International (TII), represents a remarkable achievement. With a primary focus on generating creative text and effectively following instructions, this large language model was designed to excel in these specific domains. Notably, Falcon-7B-Instruct prioritizes efficiency, enabling seamless deployment on resource-constrained devices. Leveraging rotary position embeddings, a recent breakthrough in language modeling, the model achieves enhanced efficiency compared to traditional positional embeddings, leading to improved performance across various language tasks. Requiring approximately 15GB of memory for storage and 4GB for execution on a single GPU, Falcon-7B-Instruct emerges as an ideal solution for users contending with limited memory resources on their hardware platforms.
Model type: Causal decoder-only
Language(s) (NLP): English and French
License: Apache 2.0
Finetuned from model: Falcon-7B
Training Details
Training Data
The training data for Falcon-7B-Instruct, the Large Language Model (LLM), was meticulously collected from diverse sources to ensure the model's ability to generate high-quality text in various contexts. This involved leveraging public web crawls, such as CommonCrawl, and chat and instruct datasets like Bai ze, GPT4all, and GPTeacher. The RefinedWeb corpus, designed specifically for language model training, was also utilized. Stringent filtering measures were applied to eliminate machine-generated content and adult material, ensuring that the model generates safe and appropriate text. This meticulous approach to data collection establishes Falcon-7B-Instruct's proficiency in generating superior-quality text across a wide range of domains.
Training Infrastructure
The training infrastructure of Falcon-7B-Instruct was purposefully architected to prioritize efficiency and scalability. By harnessing the power of GPUs, the model training process was accelerated, enabling faster iterations. Moreover, leveraging the capability to distribute the model across multiple machines facilitated training on an expanded dataset, enhancing its overall performance. Adopting the GPT-3 language modeling framework further ensured efficient training procedures, solidifying the model's training efficacy.
Training Objective
The training objective of Falcon-7B-Instruct involved formulating a loss function to quantify the disparity between predicted and correct tokens. This loss function was minimized by utilizing the AdamW optimizer, an optimized variant of Adam specifically tailored for large language models. The training procedure encompassed two distinct phases: pre-training and fine-tuning. The model acquired a foundational understanding of language's fundamental principles during the pre-training phase. Subsequently, in the fine-tuning phase, the model underwent task-specific learning, focusing on tasks like chat and instruct, thereby enhancing its proficiency in these particular domains.
Training Observation
The Falcon-7B-Instruct Large Language Model (LLM) underwent training on an extensive text and code dataset. Utilizing 384 GPUs on AWS over two months, the model achieved state-of-the-art performance across a diverse range of NLP tasks. It acquired a profound understanding of long-range dependencies and demonstrated robust generalization to novel tasks and domains.
Other InstructEval Models
Tk-Instruct
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ChatGLM-6B
ChatGLM-6B is an open-source bilingual dialogue language model crafted to enable seamless question-answering tasks in both Chinese and English.
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Guanaco
The Guanaco models are open-source chatbots that have been finely tuned using a 4-bit QLoRA approach on LLaMA base models, leveraging the OASST1 dataset.
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