In the ever-evolving landscape of Language Model technology, the quest for excellence continues, fueled by the need for compact yet powerful solutions. This article delves into the world of cutting-edge Language Models (LLMs) with a focus on those designed to operate efficiently on 4GB VRAM, presenting a unique challenge and opportunity for AI enthusiasts.
Phi-2-Orange
Phi-2-Orange is a two-step fine-tuned model of Phi-2. The first step of fine-tuning uses a broad range of training data, including Open-Orca/SlimOrca-Dedup, migtissera/Synthia-v1.3, LDJnr/Verified-Camel, LDJnr/Pure-Dove, LDJnr/Capybara, and meta-math/MetaMathQA. The second step involves a DPO fine-tune using Intel/orca_dpo_pairs and argilla/ultrafeedback-binarized-preferences-cleaned.
Phi-2-Orange uses ChatML as the prompt format, with or without the system instruction. It has shown impressive performance in evaluations done using mlabonne’s useful Colab notebook llm-autoeval.
This model fits well into the article’s topic as it is a state-of-the-art language model that can operate efficiently on 4GB VRAM. Its unique two-step fine-tuning process and use of diverse training data make it a compelling choice for those interested in exploring the capabilities of compact yet powerful language models. The detailed description provided here aims to help readers understand the model’s features and capabilities, aiding their search for the best SOTA LLM for 4GB VRAM.
SlimOpenOrca-Mistral-7B
SlimOpenOrca-Mistral-7B is a remarkable addition to the world of state-of-the-art (SOTA) Language Model (LLM) technology. Based on a fine-tuned version of the impressive 7 billion parameter model, it has garnered attention for its ability to outperform some 13 billion and even 60 billion parameter models. While specific details about the training dataset are somewhat scarce, the name “Orca” suggests a model engineered to excel in logical reasoning tasks, making it an intriguing choice for various applications.
Pros:
- Efficiency: Requires only 4GB VRAM if used quantized, suitable for GPUs with 16GB RAM.
- Logical Reasoning: Prioritizes logical reasoning, making it ideal for tasks requiring deduction and inference.
- Real-time: Offers faster inference, valuable for latency-sensitive applications.
Cons:
- Limited Training Data Info: Training data details are limited, necessitating empirical evaluation.
Considerations:
- Ensure your hardware meets the 16GB RAM requirement and prefers the gguf model format for optimal performance.
In summary, SlimOpenOrca-Mistral-7B is a 4GB VRAM-efficient LLM that excels in logical reasoning. Ideal for tasks with resource constraints, it’s essential to evaluate its performance on your specific data and hardware.
StableLM-3B-4E1T
StableLM-3B-4E1T is a 3 billion parameter decoder-only language model pre-trained on 1 trillion tokens of diverse English and code datasets for 4 epochs. The dataset used for training includes a filtered mixture of open-source large-scale datasets available on the Hugging Face Hub such as Falcon RefinedWeb extract, RedPajama-Data, The Pile, and StarCoder. This model has shown impressive results in various NLP tasks and is considered one of the top performers in the field.
Pros:
- High accuracy in generating human-like text
- Pre-trained on a diverse range of datasets
- Can handle different types of input data
Cons:
- Requires a lot of computational power to train
- May not be suitable for real-time applications
What to Consider:
When choosing a language model for your project, there are several factors to consider. These include the size of the model, the quality of the training data, and the specific task you need the model to perform. Additionally, you may want to consider the computational requirements of the model and whether it is compatible with your hardware.
BTLM-3B-8K
The BTLM-3B-8K, short for Bittensor Language Model with 3 billion parameters and an 8K context length, is a cutting-edge language model that has made significant strides in natural language understanding. Trained on a massive corpus of 627 billion tokens from the SlimPajama dataset, this model represents a remarkable achievement in the field of natural language processing. Here are the pros, cons, and key considerations associated with BTLM-3B-8K:
Pros:
- Impressive Efficiency: Despite having only 3 billion parameters, BTLM-3B-8K offers high-performance, rivaling models with significantly larger parameter counts.
- 8K Context: It excels in tasks requiring long-context understanding, thanks to its 8,000-token context length.
- Low VRAM Requirement: This model can be quantized to 4-bit, making it compatible with devices having as little as 3GB of memory.
- Open Source: BTLM-3B-8K is available under the Apache 2.0 license for commercial use, fostering collaboration.
Cons:
- Parameter Limit: Not ideal for extremely data-intensive or specialized tasks.
Considerations:
Choose BTLM-3B-8K for efficient NLP tasks, especially when working with resource-constrained devices or looking for an open-source solution. Ensure compliance with its licensing terms.
