finetuning llm - MonsterAPI Blog

finetuning llm

A collection of 11 posts

How to Fine-tune a Large Language Model

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How to Fine-tune a Large Language Model

A comprehensive guide with fundamental concepts on how to fine-tune a large language model in 2024.

Finetuning LLMs with MonsterTuner

How to Fine-tune Open Source AI Models like LlaMa, Mistral, SDXL

Fine-tuning is a technique in machine learning used to adapt a pre-trained model to a new and more specific task. It's like taking a highly trained athlete and giving them specialized coaching to excel in a particular sport. In this blog, we will explore the various methods to build our

Finetuning Llama-3 with MonsterGPT

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Finetuning Llama-3 with MonsterGPT

Llama-3 currently holds the top position among open-source large language models (LLMs). On the Chatbot Arena Leaderboard, it leads the open-source category by a significant margin, with no comparable rivals. The performance gap between Llama-3 and GPT-4 is surprisingly narrow, and with its next release Llama-3 400B it is expected

Introduction to LLMs - What Are LLMs, How Do LLMs Work?

Introduction to LLMs - What Are LLMs, How Do LLMs Work?

Overview of Large Language Models Large language models (LLMs) have revolutionized how AI models work. Large Language Models are similar to human intelligence. They are used for several things, including academia, tech, entertainment, and the research community. LLMs are trained on extensive data sets, they utilize intricate architectures like stack

How to fine-tune a Large Language Model (LLM) and deploy it on MonsterAPI

How to fine-tune a Large Language Model (LLM) and deploy it on MonsterAPI

Learn how to deploy an LLM finetuned with MonsterAPI’s no-code LLM finetuning using our new Deploy service with just a few clicks. Using this service anyone can deploy an LLM with or without a LoRA Adapter with just a few clicks or commands. See this blog to learn how

Outperforming SOTA LLMs for Less than the Cost of a Coffee with Monster Tuner

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Outperforming SOTA LLMs for Less than the Cost of a Coffee with Monster Tuner

Finetuning Mistral 7B LLM using Monster Tuner to outperform SOTA LLMs like Falcon and Zephyr.

Finetuning LLaMA 70B with No-Code: Results, Methods, and Implications

Finetuning LLaMA 70B with No-Code: Results, Methods, and Implications

In this blog post, we will demonstrate how you can effortlessly fine-tune the LLaMA 2 - 70B model at a fraction of the cost of $57.75 with just a few clicks, utilising the No-Code LLM-Finetuner from Monster API. Download the Fine-Tuned Model weights from Hugging Face Background LLaMA 2

MonsterAPI is now Integrated with Llama Index!

MonsterAPI is now Integrated with Llama Index!

In the ever-evolving realm of artificial intelligence, simplicity in accessing Language Models (LLMs) is essential. Developers and businesses continuously seek ways to harness the immense potential of these models to enhance their AI-driven applications. The combination of MonsterAPI with llama_index provides an invaluable solution to businesses for questioning meaningful

A guide on how to finetune llama 2 llm

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How to finetune Llama 2 LLM

LLaMA 2 is an impressive family of pretrained and fine-tuned Large Language Models (LLMs) released by Meta AI ranging from 7B to an astounding 70B parameters (7B, 13B, 70B). As successors to the already impressive LLaMA 1, these newer versions are more refined, offering a host of enhancements that have

MonsterAPI no-code LLM finetuner

A guide on how to Finetune Large Language Models (LLMs) in 2024

Large Language Models (LLMs) have quickly become a sensation in the field of Natural Language Processing (NLP). The emergence of ChatGPT has further amplified the interest in LLMs as they possess the remarkable ability to generate text that closely resembles human language, offer prompt answers to queries, and actively engage

Introducing no-code LLM FineTuning with Monster API

Introducing no-code LLM FineTuning with Monster API

FineTuning pre-trained models is an essential practice in natural language processing (NLP) domain to optimize their performance for specific tasks. At Monster API, we understand the challenges developers face when fine-tuning models, especially when it comes to complex setups, memory limitations, high GPU costs, and the absence of standardized practices.