Harness the full potential of Large Language Models tailored precisely to your business needs. At 10Clouds, we specialize in transforming open-source LLMs, particularly the LLAMA family, into powerful, domain-specific AI solutions.
Expert analysis of your specific use case to determine the optimal LLAMA variant (7B, 13B, or 70B) and custom architecture modifications to enhance model capabilities for your domain.
High-quality, domain-specific datasets, data cleaning, preprocessing, augmentation techniques, and synthetic data generation for low-resource domains.
State-of-the-art fine-tuning techniques (LoRA, QLoRA, P-Tuning v2), hyperparameter optimization for maximum performance, and efficient training strategies to minimize computational resources.
Our team of AI specialists has successfully fine-tuned over 50 LLMs across diverse industries.
We employ advanced methods like LoRA, QLoRA, and instruction fine-tuning to optimize model performance.
From selecting the right base model to crafting bespoke datasets, we tailor every step to your unique requirements.
On average, our fine-tuned models show a 40% improvement in task-specific accuracy compared to base models.
Challenge: A legal technology startup needed to automate the analysis of complex legal contracts, extracting key clauses and identifying potential risks.
Solution: We fine-tuned LLAMA 13B using a carefully curated dataset of 50,000 annotated legal documents, focusing on contract-specific language and structure.
Results:
Challenge: A leading e-commerce platform sought to improve their AI-driven customer support system to handle a wide range of product-related queries more accurately.
Solution: We fine-tuned LLAMA 70B on a massive dataset of 5 million+ customer interactions, incorporating product catalogs and support guidelines.
Results:
Let's discuss how our LLM fine-tuning expertise can drive innovation and efficiency in your business. Contact us for a free consultation and personalized solution proposal.
BOOK A CONSULTATIONOpenAI's large language model frequently employed for human-like text generation, conversation simulation, and data analysis.
PyTorch is a machine learning platform grounded on the Torch library, frequently utilized for tasks including computer vision and natural language processing.
Tools for creating and managing visual content, enhancing the capabilities of intelligent AI agents in multimedia applications.
At 10Clouds, we use several fine-tuning methodologies to make sure your AI models work their best. Fine-tuning local models means adjusting a pre-trained model to do specific tasks by training it on a new dataset. This helps the model perform better for your needs.
One technique we use is Retrieval-Augmented Generation (RAG). RAG combines the strengths of retrieval-based models and generative models. It fetches relevant information from a large dataset and generates accurate responses. This makes the model perform better by giving it more context and relevant data.
Preparing the dataset for LLM fine tuning is a crucial step in achieving optimal performance.
Select a dataset specific to the target task for LLM fine tuning.
Ensure the fine-tuning dataset includes a diverse amount of labeled data to cover various scenarios.
Follow the guide to fine-tuning to use high-quality datasets for fine-tuning.
When you train a model, use a well-structured dataset to fine-tune the original LLM.
This process, known as full fine-tuning, adapts pre-trained models to specific tasks.
For a model for sequence classification, include relevant text data in your dataset.
Fine-tuning is often used for multiple tasks, so ensure your dataset is comprehensive.
Platforms like Hugging Face can provide pre-processed datasets ready to be used for fine-tuning.
By following these steps, you can unlock the full potential of LLMs and create highly specialized models.
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LLM fine-tuning is the process of training a language model on specific examples of prompts and desired responses to improve its performance and relevance in a particular domain.
Fine-tuning allows you to adapt these powerful models into specialized tools capable of handling domain-specific tasks. By fine-tuning the model, you can achieve higher accuracy and relevancy in your specific applications. This process is essential for training large language models to meet the unique needs of your business.
Fine-tuning may lead to higher quality results than prompt engineering alone, cost savings through shorter prompts, the ability to reach equivalent accuracy with a smaller model, lower latency at inference time, and the ability to show an LLM more examples than can fit in a single context window.
Fine-tuning has a wide range of applications, from improving customer service chatbots to enhancing medical report processing. First, training language models. Fine-tuning language models for specific tasks, such as text generation or sentiment analysis. Then we have models for specific tasks. Adapting models to handle specialized tasks, such as legal document processing or financial report analysis. There's also advanced fine-tuning. Using advanced techniques to fine-tune models for complex applications, such as multi-task learning or domain adaptation.
The training data for full LLM fine-tuning should consist of prompt and response pairs. Having high-quality data is essential to improving performance.
Our services include fine-tuning LLMs, application-specific training, maintaining ethical use of the models, on-premise training, regular model updates and maintenance, and training and consultation services.
As a rule of thumb, you should expect to see linear improvements in your fine-tuned model's quality with each doubling of the dataset size. For every linear increase in the error rate in your training data, you may encounter a roughly quadratic increase in your fine-tuned model's error rate.
Yes, we offer technical support to solve any issues our clients may encounter.
Instruction fine-tuning trains a pre-trained language model to follow specific commands. Unlike traditional fine-tuning, which adapts a model to a dataset, this method teaches the model to understand and execute instructions. It uses a dataset with examples of commands and desired outputs. Using pre-trained models from platforms like Hugging Face, this method improves performance on tasks needing specific instructions. It's useful for LLM applications that handle multiple tasks efficiently.
To ensure quality in fine-tuning a model for domain-specific tasks, we follow key steps. We start with a high-quality pre-trained language model and a domain-specific dataset. The training dataset should be comprehensive and include relevant text data. We use best practices like regular evaluation and validation to monitor performance. Fine-tuning adapts the model for specific tasks such as natural language generation or sequence classification. By following a structured process, we transform pre-trained LLMs into specialized tools, ensuring they are accurate, robust, and adaptable to real-world applications.
