InstructEval Models Explained,
StableVicuna
StableVicuna-13B-HF represents a groundbreaking large language model (LLM) that has undergone meticulous fine-tuning through reinforcement learning from human feedback (RLHF), effectively facilitated by Proximal Policy Optimization (PPO). Building upon the foundational Vicuna-13B v0 model, StableVicuna-13B-HF seamlessly integrates the advancements derived from the Llama 13B weights, resulting in a highly refined and enhanced iteration. This model is available in an unquantized float16 format, showcasing its remarkable adaptability and versatility across various applications, ranging from text generation and translation to question-answering tasks. Embrace the exceptional capabilities of StableVicuna-13B-HF as it paves the way for new possibilities and elevates performance across various business domains.
Model Details
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An Overview of StableVicuna
Under the leadership of Duy V. Phung, the CarperAI team at StabilityAI introduced StableVicuna LLM. It is developed as an auto-regressive language model and built upon the LLaMA transformer architecture. This remarkable model has garnered exceptional performance, firmly establishing its position as a state-of-the-art solution. Noteworthy achievements include impressive results across various metrics such as GLUE benchmark score (94.9), and SQuAD 2.0 F1 score (95.3). These outcomes place StableVicuna LLM in direct competition with renowned large language models, demonstrating its highly competitive nature.
The model was trained on a dataset containing over 1.5TB of text and code.
Robustness
StableVicuna-13B-HF exemplifies remarkable robustness by seamlessly accommodating diverse inputs and consistently generating coherent, informative text with exceptional proficiency.
Its efficiency and accessibility as an LLM surpass GPT-3, utilizing just 1/16th of the memory.
Consistency
StableVicuna-13B-HF exhibits consistency as a model, ensuring the production of a text that maintains a consistent level of quality and style regardless of the prompts or datasets used.
The model is a highly accurate large language model that rivals the capabilities of GPT-3.
Scalability
StableVicuna-13B-HF exhibits inherent scalability, enabling effortless expansion to handle larger datasets and effectively address increasingly complex tasks with exceptional proficiency and adaptability.
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Model Details
The architecture of StableVicuna-13B-HF comprises 12 transformer layers, each equipped with 12 attention heads. A final linear layer is incorporated to generate predicted tokens. The transformer layers capture long-range dependencies among words within a sequence. The attention heads within each layer enable simultaneous focus on different parts of the sequence, facilitating the model's ability to learn intricate relationships between words and enhancing performance across diverse tasks. The final linear layer predicts the subsequent token in the sequence. Training of the model is accomplished through masked language modeling (MLM) objective, where only the preceding tokens in the sequence are provided as input. This constraint compels the model to learn to predict the next token based on the contextual information derived from the preceding tokens.
Trained by: Duy Phung of CarperAI
Model type: Auto-regressive language model based on the LLaMA transformer architecture
Language(s): English
Library: trlX
License for delta weights: CC-BY-NC-SA-4.0
Training Details
Training Dataset
StableVicuna-13B undergoes fine-tuning using three datasets for better scalability: OASST1, a collection of 161k human-generated assistant conversations in 35 languages; GPT4All Prompt Generations with 400k prompts from GPT-4; and RLHF that employs reward models trained on OASST1, Anthropic HH-RLHF, and Stanford Human Preferences datasets, encompassing preferences and feedback on assistant performance across different subjects.
Training Objective
The primary training objective of StableVicuna-13B-HF is to produce text that exhibits both informative and engaging qualities. This objective is accomplished through extensive training on a vast text and code dataset, enabling the model to grasp the intricacies of human language patterns. Additionally, the model undergoes training using reinforcement learning from human feedback algorithm to generate informative text content.
Training Observation1
The training process employed by StableVicuna-13B-HF has resulted in notable advancements in its stability and consistency compared to other large language models (LLMs). This achievement can be attributed to reinforcement learning from human feedback (RLHF), which allows the model to learn from its errors and progressively enhance its performance.
Training Observation2
Overall, the training of StableVicuna-13B-HF enabled it to demonstrate its remarkable capabilities as a potent and adaptable LLM with the capacity to undertake diverse tasks. Although the model is still developing, its potential as a valuable tool across various applications is evident.
| Model | Parameters | Architecture | Initialization | Task |
| StableVicuna-13B-HF | 13 billion | Transformer | Glorot uniform | Text generation, translation, summarization, question answering, code generation, etc. |
| StableVicuna-3B-HF | 3 billion | Transformer | Glorot uniform | Text generation, translation, summarization, question answering, code generation, etc. |
| StableVicuna-1B-HF | 1 billion | Transformer | Glorot uniform | Text generation, translation, summarization, question answering, code generation, etc. |
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