In the rapidly evolving landscape of artificial intelligence, language models are at the forefront, transforming the way we interact with technology and comprehend information. The development and fine-tuning of these advanced AI models continue to push the boundaries of machine learning, paving the way for increasingly sophisticated applications. This article takes a deep dive into the crests of this technological wave, focusing specifically on the most capable 13 billion parameters (13b) Large Language Models (LLMs).
We’ll discuss how these models were trained, the unique strategies employed, and the kind of performance they deliver. We will delve into the impressive feats they have achieved and examine some of the potential concerns that come with these powerful tools. Whether you’re a seasoned AI enthusiast or a curious newcomer, this exploration of the cutting-edge 13b LLMs will provide a fascinating and insightful journey into the advanced world of AI.
Related: 6 Best Interface for Running local LLM
Best 13B LLM Model
Llama2-chat-AYB-13B
| Key Features | Fit For |
|---|---|
| – Ensemble Model | – Chatbots |
| – 13 Billion Parameters | – Text Generation |
| – HuggingFace Transformers | – Sentiment Analysis |
| – Diverse Datasets | – Translation |
| – Question-Answering Systems |
Llama2-chat-AYB-13B is an innovative Large Language Model (LLM) developed by Posicube Inc., based on the LLaMA-2-13b-chat backbone model. The model’s development involved an interesting approach aimed at enhancing its performance. The creators at Posicube Inc. diverged from the Llama-2-13b-chat-hf and explored the concept of ensemble learning to achieve maximum performance. This approach involves combining the top-ranking models from various benchmarks, effectively leveraging the strengths of each individual model to create an ensemble model that excels in natural language understanding and generation tasks.
The model is built using the HuggingFace Transformers library, which is widely recognized for its contributions to the field of natural language processing. To train this model, Posicube Inc. utilized Orca-style and Alpaca-style datasets, which are known for their diverse and comprehensive text sources. This diverse dataset ensures that Llama2-chat-AYB-13B is well-equipped to handle a wide range of natural language processing tasks, making it a versatile tool for various applications.
Stable-Platypus2-13B
| Key Features | Fit For |
|---|---|
| – Open-Platypus Dataset | – STEM Tasks |
| – Dataset Optimization | – Minimizing Redundancy |
| – Contamination Resolution | – Addressing Data Issues |
| – Fine-Tuning and Merging | – Enhanced Performance |
| – Efficient Instruction Tuning | – Domain-Specific Tasks |
Stable-Platypus2-13B is a cutting-edge LLM model trained with a focus on STEM and logic-based tasks. Derived from garage-bAInd/Platypus2-70B, this model showcases the efficiency of dataset distillation and instruction tuning, yielding exceptional performance while incorporating domain-specific expertise.
Key Features:
- Open-Platypus Dataset: A meticulously curated collection from 11 open-source datasets, prioritizing STEM and logic proficiency. Over 90% human-crafted questions, minimizing reliance on LLM-generated content.
- Dataset Optimization: Employing a unique similarity exclusion method to streamline the dataset and mitigate data redundancy effectively.
- Contamination Resolution: Thoroughly addressing contamination issues in open LLM training sets, utilizing a robust data filtering process.
- Fine-Tuning and Merging: Implementing LoRA module selection, merging, and fine-tuning techniques, inspired by established methodologies, to enhance model performance.
Orca Mini v3 13B
| Key Features | Fit For |
|---|---|
| – Fine-Tuning from Larger Models | – Transfer Learning |
| – Evaluated Using LM Evaluation Harness | – Diverse Tasks |
| – Inherits Capabilities from Orca Style Datasets | – Language Challenges |
| – Uncensored Model | – Versatile Usage (with limitations) |
Orca Mini v3 13B is a remarkable LLM model that has gained popularity due to its unique training approach. Built upon the foundation of the Llama2-13b model, Orca Mini v3 showcases the potential of larger models transferring their reasoning capabilities to smaller counterparts. The model achieves this through a meticulous fine-tuning process, where the insights and reasoning of a larger model are distilled into the smaller one.
Evaluated extensively using the Language Model Evaluation Harness developed by EleutherAI, Orca Mini v3 13B has demonstrated its prowess across a diverse array of tasks. This model inherits its capabilities from the Orca Style datasets, allowing it to excel in various language-related challenges.
It’s important to note that Orca Mini v3 13B is an uncensored model, carrying the usage constraints of the original Llama-2 model. As with any advanced AI, users should be aware of its limitations and utilize it responsibly. It’s crucial to mention that this model is provided without any guarantees or warranties.
Dolphin Llama 13B
| Key Features | Fit For |
|---|---|
| – Improved Reasoning Abilities | – Various Tasks |
| – Detective Reasoning Approach | – Enhanced Reasoning |
| – Uncensored Model | – Versatile Usage (with alignment layer) |
Dolphin Llama 13B is an Open Source Uncensored Language Model (LLM) that is designed for non-commercial use, based on the llama1 architecture.
One of the notable features of Dolphin Llama 13B is its ability to exhibit improved and increased reasoning capabilities compared to other models of similar or even larger size. This advancement is achieved through a unique technique based on a research paper from Microsoft. The model employs a step-by-step detective reasoning approach, wherein larger models impart their reasoning strategies to the smaller models, resulting in enhanced reasoning abilities in Dolphin Llama 13B.
Due to its uncensored nature, Dolphin Llama 13B is highly versatile, accepting a wide range of inputs and queries. However, users are advised to implement their own alignment layer before deploying the model as a service to ensure ethical and responsible usage.
Nous Hermes Llama 2 13B
| Key Features | Fit For |
|---|---|
| – Longer Responses | – Role-play Story Writing |
| – Reduced Hallucination Rate | – Advanced Question Answering |
| – Uncensored Model | – Versatile Usage |
The Nous Hermes Llama 2 13B is an advanced language model fine-tuned on extensive instructions, totaling over 300,000. Developed by Nous Research, with Teknium and Emozilla leading the fine-tuning process and dataset curation, this model benefits from sponsorship by Redmond AI and contributions from various other collaborators.
A notable feature of this model is its longer responses, reduced hallucination rate, and most importantly, the absence of any censorship mechanisms from OpenAI. Its uncensored nature enables a wide range of applications, including role-play story writing and advanced question answering. Serving as a successor to Hermes on Llama-1, it maintains consistency with its predecessor while offering enhanced capabilities, making it a powerful and versatile language model.
WizardLM-13B V1.2
WizardLM-13B V1.2 is the latest iteration in the highly acclaimed WizardLM series, surpassing its predecessor, 1.1, to claim the throne as the best-performing 13 billion parameter LLM model. With an impressive score of 7.06 on the MT-Bench Leaderboard, 89.17% on the AlpacaEval Leaderboard, and an astounding 101.4% on WizardLM Eval, it sets a new standard of excellence in the 13B LLM range. Competing fiercely with ChatGPT, it outshines in various aspects, making it a compelling choice for various language processing tasks. Its enhanced capabilities and cutting-edge performance make WizardLM-13B V1.2 a top contender in the ever-evolving world of language models.
Wizard-Vicuna-13B-Uncensored
Wizard-Vicuna-13B-Uncensored is a powerful AI model that builds upon the foundations of the wizard-vicuna-13b model. However, it has been specifically trained with a subset of the dataset, carefully removing responses that contained alignment or moralizing aspects. The objective behind this training approach is to create a WizardLM that doesn’t inherently possess alignment, allowing alignment of any sort to be added separately. For instance, reinforcement learning from human feedback (RLHF) with a LoRA (Learning from Rewarding Agents) framework can be used to incorporate alignment.
What sets Wizard-Vicuna-13B-Uncensored apart is its uncensored nature. Unlike other models, it does not have guardrails in place. This lack of guardrails allows the model to generate responses without any inherent limitations or restrictions. This uncensored capability makes it one of the best, if not the best, 13B LLM (Large Language Model) available. With its immense capacity for generating unrestricted content, this model opens up new possibilities for creative and unrestricted AI-generated interactions.
Nous-Hermes-13b Llama 1
Nous-Hermes-13b is an innovative language model developed through a collaborative fine-tuning process involving Nous Research, Teknium, Karan4D, and other contributors. The model, enhanced from the Llama 13b, is trained on an extensive range of over 300,000 instructions, resulting in exceptional performance on a variety of tasks. What sets this model apart is its ability to generate long responses, maintain a low rate of hallucinations, and bypass OpenAI’s censorship mechanisms. The model was fine-tuned for over 50 hours on an 8x a100 80GB DGX machine with a sequence length of 2000.
Wizard-Mega-13b
Wizard-Mega-13b is a Llama 13B model, distinguished by its fine-tuning on ShareGPT, WizardLM, and Wizard-Vicuna datasets. These datasets have been carefully curated to exclude instances where the model may refuse to respond or self-reference as an AI language model. Notably, this model does not employ human preference alignment or in-the-loop response filtering, leading to the possibility of uncensored and sometimes problematic outputs.
Airoboros-13b
Airoboros-13b is an advanced 13b parameter LlaMa model, standing out for its exclusive use of synthetic training data created by the Airoboros GitHub project. It embraces an uncensored approach, utilising a jailbreak prompt to generate instructions that would typically be censored. Despite its initial weaknesses in math/extrapolation, closed-question answering, and coding, the model has been refined through further fine-tuning to significantly improve these areas. Although its data was largely untouched, several rounds of manual clean-up were implemented to remove unfavorable prompts.
Vicuna-EvolInstruct-13B
Vicuna-EvolInstruct-13B is an evolution of the Vicuna model, distinguished by its use of Evol-Instruct for training. This unique method has led to impressive performance enhancements, elevating it beyond the capabilities of the regular Vicuna model. The model exhibits a high degree of versatility, aptitude, and proficiency across a wide range of tasks.
Manticore-13b
Manticore-13b was conceived as an upgrade to the Wizard-Mega-13b, although its performance slightly trails its predecessor. Nevertheless, it shines in specific domains like story writing and roleplay. Manticore-13b’s hallmark lies in its logical coherence, which enables it to generate contextually relevant and consistent outputs, thereby enhancing its utility in various applications.
Wizard-Vicuna-13b
Wizard-Vicuna-13b is a fine-tuned model that relies on a subset of the dataset, excluding responses containing alignment or moralizing. This deliberate omission aims to create a version of WizardLM free from built-in alignment, enabling the addition of alignment separately through methods such as RLHF LoRA. As an uncensored model, Wizard-Vicuna-13b is void of any guardrails, thereby placing full responsibility for its application on the user. This model’s design ethos reflects the classic trade-off between freedom and responsibility in AI use.
Robin 13B v2
Robin-13B v2 is an impressive LLM (Large Language Model) that has made its mark in the field. Despite being a 13B model, it has achieved remarkable performance, even surpassing some 60B models in the LLM leaderboard with an impressive ranking of sixth place.
Robin-13B v2 shines particularly in conversational tasks, demonstrating its ability to engage in meaningful and coherent conversations with users. Whether you need to discuss a wide range of topics or seek assistance in generating natural and flowing dialogues, Robin-13B v2 excels in maintaining engaging and interactive conversations.
Orca Mini 13B
Orca Mini 13B LLM, a high-performance language model designed specifically for logic tasks and question answering. This model, built on the OpenLLaMa-13B architecture, has undergone extensive training using explain tuned datasets derived from WizardLM, Alpaca, and Dolly-V2.
The Orca Mini 13B LLM incorporates advanced techniques outlined in the Orca Research Paper to construct a powerful dataset. By combining approximately 70,000 explain tuned examples from WizardLM and thousands more from Alpaca and Dolly-V2, this model has acquired a deep understanding of various contexts and effective problem-solving approaches.
What sets the Orca Mini 13B LLM apart is its ability to leverage the knowledge imparted by its teacher model, ChatGPT (version gpt-3.5-turbo-0301). By integrating 15 system instructions from the Orca Research Paper, this model learns intricate thought processes, enhancing its capabilities and ensuring high-quality responses.
With the Orca Mini 13B LLM, incorporating system prompts is seamless. Simply add a system prompt before each instruction to effortlessly guide the model’s responses and receive accurate, insightful answers. Whether tackling complex logic tasks or dynamic question answering, this model delivers exceptional results.
