Harness the power of Large Language Models with Azure Machine Learning prompt flow
Unlock the full potential of your AI solutions with our latest blog on prompt flow! Discover how to effectively assess and refine your prompts and flows, leading to production-ready, impactful LLM-infused applications. Don't miss out on these game-changing insights!
Azure Open AI (GPT) with Power Apps - Build a Power App to create Demo or Personal Knowledge Bot
Let's build a power app with open ai to create a personal knowledge assistant and or also to show demo. App allows users to increase productivity by gaining knowledge by asking questions and other features like Summarization, Code generation, Email Content, Blob post content, twitter content.Mistral Large, Mistral AI's flagship LLM, debuts on Azure AI Models-as-a-Service
Microsoft is partnering with Mistral AI to bring its Large Language Models (LLMs) to Azure. Mistral AI’s OSS models, Mixtral-8x7B and Mistral-7B, we added to the Model Catalog last December. We are excited to announce the addition of Mistral AI’s new flagship model, Mistral Large to the Mistral AI collection of models in the Azure AI model catalog today. The Mistral Large model will be available through Models-as-a-Service (MaaS) that offers API-based access and token based billing for LLMs, making it easy to build Generative AI apps. You can provision an API endpoint in a matter of seconds and try out the model in the Azure AI Studio playground or use it with popular LLM app development tools like Azure AI prompt flow and LangChain. The APIs support two layers of safety – first, the model has built-in support for a “safe prompt” parameter and second, Azure AI content safety that screens for harmful content generated by the model, enabling developers to build safe and trustworthy applications.
Scalable and Efficient Fine-Tuning of LLM on Azure ML
https://github.com/james-tn/llm-fine-tuning/tree/main/opensource_llm/single_step Co-Author: Mohamad AL jazaery Why Scalable and Efficient Fine-Tuning Matters Faster Iterations, Shorter Time-to-Value: In today’s competitive AI landscape, time is of the essence. The faster you can fine-tune a model, the quicker you can validate ideas, test hypotheses, and bring solutions to market. High-profile GPU machines are costly: High-performance GPUs and compute clusters don’t come cheap, and their availability is often limited. Efficient fine-tuning techniques, such as model sharding and distributed training, maximize the utilization of these precious resources—ensuring that you get the most out of your infrastructure investment. Choosing the Right Azure ML GPU Compute for the Job: NC or ND? Not all GPU computes are created equal, and choosing the right sku can make or break your training efficiency. ND Series: Ideal for distributed training across multiple nodes, thanks to its Infiniband (IB) connectivity that ensures high-speed communication between nodes like pretraining LLM or finetuning very large model ~70B params. NC Series: Small and medium workload where no heavy interaction between nodes needed like LLM inferencing or mid-size LLM finetuning. Azure GPU Machine Options by Scenario: Scenario Common model size Training Approach Recommended Azure Compute Small-scale fine-tuning < 3B parameters Parameter-efficient tuning NCas_T4_v3 (Tesla T4, 16 GB) Medium-scale fine-tuning 1–5B parameters Full or parameter-efficient NCs_v3 (Tesla V100, 16 GB) Distributed training for medium models 5–10B parameters Full fine-tuning ND_v2 (Tesla V100 NVLINK, 32 GB, InfiniBand) Large-scale fine-tuning (single machine) 10–30B parameters Full or parameter-efficient NC_A100_v4 (A100, 40 GB) Distributed training for very large models 20–70B parameters Full fine-tuning NDasrA100_v4 (A100, 80 GB, HDR InfiniBand) Very large models training (single machine) up to 70B parameters Full or parameter-efficient NCads_H100_v5 (H100 NVL, 94 GB) Massive-scale distributed training > 70B parameters Full fine-tuning ND-H100-v5 (H100, 80 GB, scale-out InfiniBand) Distributed Efficient Training: A Quick Guide When scaling fine-tuning tasks, choosing the right distributed training method is key: DDP (Data Parallelism): Works well when the entire model fits on a single GPU. It replicates the model across multiple GPUs and splits the data for parallel processing. Check experiment 1 in the following section. Model Parallelism: A game-changer for massive models that don’t fit on a single GPU. It shards not only the data but also the model parameters and optimizer states across multiple GPUs, enabling efficient training of models like LLaMA-70B on GPUs with low memory GPUs. Both FSDP and DeepSpeed as libraries excel at implementing advanced forms of model parallelism and memory optimization. Memory Optimization Techniques Gradient Checkpointing: Reduces memory by recomputing activations during the backward pass, trading memory for additional computation. Mixed Precision Training: Reduces memory usage by using FP16 or BF16 instead of FP32, accelerating training while maintaining numerical stability. Supported by both frameworks. Quantization (DeepSpeed Exclusive): Uses INT8 precision for weights and activations, dramatically reducing memory and compute requirements. Offloading (DeepSpeed Exclusive): Offloads optimizer states and model parameters to CPU or NVMe, freeing up GPU memory for computation. Our Experiments: Pushing the Limits of Scalability Experiment 1: Distributed Training on Multiple Nodes using DDP We conducted an experiment to fine-tune the Llama-3.1-8B model using LoRA (Low-Rank Adaptation) on Azure ML NDv2-V100 nodes. The goal was to evaluate the efficiency of fine-tuning across different numbers of nodes (1, 2, and 3) and observe the impact on training time and throughput. Azure ML Job YAML Definition $schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json type: command code: ./ # Path to your training script and related files inputs: model_dir: path: azureml://registries/azureml/models/mistralai-Mistral-7B-v01/versions/19 command: > accelerate launch --num_processes 16 # gpu per machine * num of machines --num_machines 2 --machine_rank $NODE_RANK --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT compute: azureml:ndv2-cluster resources: instance_count: 2 # Number of nodes for distributed training distribution: type: pytorch process_count_per_instance: 1 # Number of processes per node Results: As you increased the number of nodes from one to three, the throughput increased proportionally. This indicates that the system scaled efficiently with the addition of more nodes, maintaining a close-to-linear improvement in throughput. Experiment 2: Model Parallelism using FSDP Fine-tuning a 70B-parameter model on GPUs with only 16GB of memory might sound impossible, but we made it happen using FSDP (Full Sharded Data Parallelism) on Azure ML using a cluster of multiple NDv2-V100 nodes. By distributing not only the data but also the model parameters and optimizer states across multiple nodes, we unlocked the power of full sharding. $schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json type: command code: ./ # Path to your training script and related files inputs: model_dir: path: azureml://registries/azureml-meta/models/Llama-3.3-70B-Instruct/versions/4 command: > accelerate launch --config_file "configs/fsdp_config.yaml" --num_processes 32 --num_machines 4 --machine_rank $NODE_RANK --main_process_ip $MASTER_ADDR --main_process_port $MASTER_PORT train.py compute: azureml:ndv2-cluster resources: instance_count: 4 # Number of nodes for distributed training distribution: type: pytorch process_count_per_instance: 1 # Number of processes per node Key Takeaways: Memory Efficiency: Full sharding enabled us to fine-tune the LLaMA-70B model on V100 GPUs despite their limited memory. Connectivity Matters: The Infiniband (IB) connectivity of ND nodes played a critical role in ensuring smooth communication across GPUs, making this feat possible. Conclusion Scalable and efficient fine-tuning is the key to unlocking the true potential of Large Language Models. By leveraging distributed training techniques, such as FSDP and DDP, and optimizing compute resources on Azure ML, researchers and practitioners can overcome the challenges of training massive models—reducing costs, accelerating time-to-value, and driving AI innovation. Access the code and start experimenting here! Future work: The second part will focus on real-world pipeline setups, including end-to-end model training, hyperparameter optimization, and testing. The third part will dive into deploying trained models for practical use. Future posts may explore best practices for specific fine-tuning scenarios and techniques.Falcon LLMs in Azure Machine Learning
We are thrilled to announce that the Falcon family of state-of-the-art language models, created by the Technology Innovation Institute (TII) and hosted on Hugging Face hub, is now available in Model Catalog on the Azure Machine Learning platform. This exciting addition is a result of the partnership between Microsoft and Hugging Face. With their flagship library, Transformers, and Models hub with over 200,000 open-source models, Hugging Face has become the go-to source for state-of-the-art pre-trained models and tools in the NLP space.
