Chapter 1. Red Hat Enterprise Linux AI 1.4 release notes RHEL AI provides organizations with a process to develop enterprise applications on open source Large Language Models (LLMs). 1.1. About this release Red Hat Enterprise Linux AI version 1.4 includes various features for Large Language Model (LLM) fine-tuning on the Red Hat and IBM produced Granite model. A customized model using the RHEL AI workflow consisted of the following: Install and launch a RHEL 9.4 instance with the InstructLab tooling. Host information in a Git repository and interact with a Git-based taxonomy of the knowledge you want a model to learn. Run the end-to-end workflow of synthetic data generation (SDG), multi-phase training, and benchmark evaluation. Serve and chat with the newly fine-tuned LLM. 1.2. Features and Enhancements Red Hat Enterprise Linux AI version 1.4 includes various features for Large Language Model (LLM) fine-tuning. 1.2.1. Installing Red Hat Enterprise Linux AI is installable as a bootable image. This image contains various tooling for interacting with RHEL AI. The image includes: Red Hat Enterprise Linux 9.4, Python version 3.11 and InstructLab tools for model fine-tuning. For more information about installing Red Hat Enterprise Linux AI, see Installation overview and the "Installation feature tracker" 1.2.2. Building your RHEL AI environment After installing Red Hat Enterprise Linux AI, you can set up your RHEL AI environment with the InstructLab tools. 1.2.2.1. Initializing InstructLab You can initialize and set up your RHEL AI environment by running the ilab config init command. This command creates the necessary configurations for interacting with RHEL AI and fine-tuning models. It also creates proper directories for your data files. For more information about initializing InstructLab, see the Initialize InstructLab documentation. 1.2.2.2. Downloading Large Language Models You can download various Large Language Models (LLMs) provided by Red Hat to your RHEL AI machine or instance. You can download these models from a Red Hat registry after creating and logging in to your Red Hat registry account. For more information about the supported RHEL AI LLMs, see the Downloading models documentation and the "Large Language Models (LLMs) technology preview status". 1.2.2.2.1. Uploading models to an S3 bucket Red Hat Enterprise Linux AI version 1.4 now allows you to upload models and checkpoints to an AWS S3 bucket. For more information on model uploading, see the Uploading your models to a registry 1.2.2.3. Serving and chatting with models Red Hat Enterprise Linux AI version 1.4 allows you to run a vLLM inference server on various LLMs. The vLLM tool is a memory-efficient inference and serving engine library for LLMs that is included in the RHEL AI image. For more information about serving and chatting with models, see Serving and chatting with the models documentation. 1.2.3. Creating skills and knowledge YAML files On Red Hat Enterprise Linux AI, you can customize your taxonomy tree using custom YAML files so a model can learn domain-specific information. You host your knowledge data in a Git repository and fine-tune a model with that data. For detailed documentation on how to create a knowledge markdown and YAML file, see Customizing your taxonomy tree . 1.2.4. Generating a custom LLM using RHEL AI You can use Red Hat Enterprise Linux AI to customize a granite starter LLM with your domain specific skills and knowledge. RHEL AI includes the LAB enhanced method of Synthetic Data Generation (SDG) and multi-phase training. 1.2.4.1. Synthetic Data Generation (SDG) Red Hat Enterprise Linux AI includes the LAB enhanced method of synthetic data generation (SDG). You can use the qna.yaml files with your own knowledge data to create hundreds of artifical datasets in the SDG process. For more information about running the SDG process, see Generating a new dataset with Synthetic data generation (SDG) . 1.2.4.1.1. Running Synthetic Data Generation (SDG) in the background RHEL AI version 1.4 introduces process management for SDG. This allows you to run SDG in the background of the same terminal you are using. You can interact with and attach to these processes while its running. 1.2.4.2. Training a model with your data Red Hat Enterprise Linux AI includes the LAB enhanced method of multi-phase training: A fine-tuning strategy where datasets are trained and evaluated in multiple phases to create the best possible model. For more details on multi-phase training, see Training your data on the model . 1.2.4.3. Benchmark evaluation Red Hat Enterprise Linux AI includes the ability to run benchmark evaluations on the newly trained models. On your trained model, you can evaluate how well the model knows the knowledge or skills you added with the MMLU_BRANCH or MT_BENCH_BRANCH benchmark. For more details on benchmark evaluation, see Evaluating your new model . 1.3. Red Hat Enterprise Linux AI feature tracker Some features in this release are currently in Technology Preview. These experimental features are not intended for production use. For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope . In the following tables, features are marked with the following statuses: Not Available Technology Preview General Availability Deprecated Removed 1.3.1. Installation feature tracker Table 1.1. Installation features Feature 1.1 1.2 1.3 1.4 Installing on bare metal Generally available Generally available Generally available Generally available Installing on AWS Generally available Generally available Generally available Generally available Installing on IBM Cloud Generally available Generally available Generally available Generally available Installing on GCP Not available Technology preview Generally available Generally available Installing on Azure Not available Generally available Generally available Generally available 1.3.2. Platform support feature tracker Table 1.2. End-to-end InstructLab workflow Feature 1.1 1.2 1.3 1.4 Bare metal Generally available Generally available Generally available Generally available AWS Generally available Generally available Generally available Generally available IBM Cloud Not available Generally available Generally available Generally available Google Cloud Platform Not available Technology preview Generally available Generally available Azure Not available Generally available Generally available Generally available Table 1.3. Inference serving LLMs Feature 1.1 1.2 1.3 1.4 Bare metal Generally available Generally available Generally available Generally available AWS Generally available Generally available Generally available Generally available IBM Cloud Generally available Generally available Generally available Generally available Google Cloud Platform (GCP) Not available Technology preview Generally available Generally available Azure Not available Generally available Generally available Generally available Table 1.4. Cloud Marketplace support Feature 1.1 1.2 1.3 1.4 AWS Not available Not available Generally available Generally available Azure Not available Not available Generally available Generally available 1.4. Large Language Models feature status 1.4.1. RHEL AI version 1.4 hardware vendor LLM support Table 1.5. LLM support on hardware vendors Feature NVIDIA granite-7b-starter Deprecated granite-7b-redhat-lab Deprecated granite-8b-starter Generally available granite-8b-redhat-lab Generally available granite-3.1-8b-starter-v1 Generally available granite-3.1-8b-lab-v1 Generally available granite-8b-code-instruct Technology preview granite-8b-code-base Technology preview mixtral-8x7B-instruct-v0-1 Generally available prometheus-8x7b-v2.0 Generally available 1.5. Known Issues AMD-smi is not usable upon installation After installing Red Hat Enterprise Linux AI using the ISO image or upgrading to a system using the bootc-amd-rhel9 container, the amd-smi tool does not work by default. To enable amd-smi , add the proper ROCm version to your user PATH variable with the following command: USD export PATH="USDPATH:/opt/rocm-6.1.2/bin" Incorrect auto-detection on some NVIDIA A100 systems RHEL AI sometimes auto-detects the incorrect system profile on machines with A100 accelerators. You can select the correct profile by re-initializing and passing the correct system profile. USD ilab config init --profile <path-to-system-profile> Upgrading to a z-stream on AMD Bare metal and NVIDIA AWS systems On RHEL AI, there is an issue in the upgrade process if you are upgrading to a AMD bare metal or NVIDIA AWS system. To successfully update to a RHEL AI z-stream on these systems, run the following command. Bare metal with AMD accelerators USD sudo bootc switch registry.redhat.io/rhelai1/bootc-amd-rhel9:1.3 AWS with NVIDIA accelerators USD sudo bootc switch registry.redhat.io/rhelai1/bootc-nvidia-rhel9:1.3 Fabric manager does not always starts with NVIDIA accelerators After installing Red Hat Enterprise Linux AI on NVIDIA systems, you may see the following error when serving or training a model. INFO 2024-11-26 22:18:04,244 instructlab.model.serve_backend:56: Using model '/var/home/cloud-user/.local/share/instructlab/phased/phase2/checkpoints/hf_format/samples_29117' with -1 gpu-lay ers and 4096 max context size. INFO 2024-11-26 22:18:04,244 instructlab.model.serve_backend:88: '--gpus' flag used alongside '--tensor-parallel-size' in the vllm_args section of the config file. Using value of the --gpus File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 105, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/usr/lib64/python3.11/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 192, in build_async_engine_client_from_engine_args raise RuntimeError( RuntimeError: Engine process failed to start To resolve this issue, you need the run the following commands: USD sudo systemctl stop nvidia-persistenced.service USD sudo systemctl start nvidia-fabricmanager.service USD sudo systemctl start nvidia-persistenced.service UI AMD technology preview installations Red Hat Enterprise Linux AI version 1.4 currently does not support graphical based installation with the technology previewed AMD ISOs. Ensure that the text parameter in your kickstart file is configured for non-interactive installs. You can also pass inst.text in your shell during interactive installation to avoid an install time crash. SDG can fail on 4xL40s For SDG to run on 4xL40s, you need to run SDG with the --num-cpus flag and set to the value of 4 . USD ilab data generate --num-cpus 4 MMLU and MMLU_BRANCH on the granite-8b-starter-v1 model When evaluating a model built from the granite-8b-starter-v1 LLM, there might an error where vLLM does not start when running the MMLU and MMLU_BRANCH benchmarks. If vLLM does not start, add the following parameter to the serve section of your config.yaml file: serve: vllm: vllm_args: [--dtype bfloat16] Kdump over nfs Red Hat Enterprise Linux AI version 1.4 does not support kdump over nfs without configuration. To use this feature, run the following commands: mkdir -p /var/lib/kdump/dracut.conf.d echo "dracutmodules=''" > /var/lib/kdump/dracut.conf.d/99-kdump.conf echo "omit_dracutmodules=''" >> /var/lib/kdump/dracut.conf.d/99-kdump.conf echo "dracut_args --confdir /var/lib/kdump/dracut.conf.d --install /usr/lib/passwd --install /usr/lib/group" >> /etc/kdump.conf systemctl restart kdump 1.6. Asynchronous z-stream updates Security, bug fix, and enhancement updates for RHEL AI 1.4 are released as asynchronous z-stream updates. This section will continue to be updated over time to provide notes on enhancements and bug fixes for future asynchronous z-stream releases of RHEL AI 1.4. Versioned asynchronous releases, for example with the form RHEL AI 1.4.z, will be detailed in subsections. 1.6.1. Red Hat Enterprise Linux AI 1.4.1 bug fixes Issued: 25 February 2025 Red Hat Enterprise Linux AI release 1.4.1 is now available. This release includes bug fixes and product enhancements. 1.6.1.1. Upgrade To update your RHEL AI system to the most recent z-stream version, you must be logged in to the Red Hat registry and run the following command: USD sudo bootc upgrade --apply For more information on upgrading your RHEL AI system, see the Updating Red Hat Enterprise Linux AI documentation. 1.6.2. Red Hat Enterprise Linux AI 1.4.2 features and bug fixes Issued: 4 March 2025 Red Hat Enterprise Linux AI release 1.4.2 is now available. This release includes bug fixes and product enhancements. 1.6.2.1. Features RHEL AI version 1.4.2, and further 1.4.z releases, now supports Intel Gaudi3 accelerators. You can download the Red Hat Enterprise Linux AI image on the Download Red Hat Enterprise Linux AI page and deploy RHEL AI on a machine with Gaudi3 accelerators. 1.6.2.2. Known Issues Inference fails on Intel Gaudi3 for multi-accelerators The 1.4.2 Intel Gaudi3 is missing a parameter in the InstructLab wrapper. This causes the inference to fail on machines with Gaudi3 accelerators. You can run the following procedure to resolve this issue. Copy the /usr/bin/ilab file to your home directoy and edit the ilab file. USD cp /usr/bin/ilab <path-to-home-directory> USD vim ~/ilab Your file will look like the following that now includes the `--env" "PT_HPU_ENABLE_LAZY_COLLECTIVES=true parameter. PODMAN_COMMAND=("podman" "run" "--rm" "-it" "--device" "/dev/infiniband" "--device" "/dev/accel" "--security-opt" "label=disable" "--net" "host" "--shm-size" "10G" "--pids-limit" "-1" "-v" "USDHOME:USDHOME" "USD{ADDITIONAL_MOUNT_OPTIONS[@]}" "--env" "HF_TOKEN" "--env" "HOME" "--env" "NCCL_DEBUG" "--env" "VLLM_LOGGING_LEVEL" "--env" "PT_HPU_ENABLE_LAZY_COLLECTIVES=true" "--entrypoint" "USDENTRYPOINT" "USD{IMAGE_NAME}") RHEL AI serving does not allow for more that 16 concurrent requests on Gaudi accelerators On RHEL AI version 1.4.2 for machines with Gaudi accelerators, you cannot run more that 16 concurrent requests when running the ilab model serve command. 1.6.2.3. Upgrade To update your RHEL AI system to the most recent z-stream version, you must be logged in to the Red Hat registry and run the following command: USD sudo bootc upgrade --apply For more information on upgrading your RHEL AI system, see the Updating Red Hat Enterprise Linux AI documentation.

Commands:
export PATH="USDPATH:/opt/rocm-6.1.2/bin"
ilab config init --profile <path-to-system-profile>
sudo bootc switch registry.redhat.io/rhelai1/bootc-amd-rhel9:1.3
sudo bootc switch registry.redhat.io/rhelai1/bootc-nvidia-rhel9:1.3
INFO 2024-11-26 22:18:04,244 instructlab.model.serve_backend:56: Using model '/var/home/cloud-user/.local/share/instructlab/phased/phase2/checkpoints/hf_format/samples_29117' with -1 gpu-lay ers and 4096 max context size. INFO 2024-11-26 22:18:04,244 instructlab.model.serve_backend:88: '--gpus' flag used alongside '--tensor-parallel-size' in the vllm_args section of the config file. Using value of the --gpus File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 105, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/usr/lib64/python3.11/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 192, in build_async_engine_client_from_engine_args raise RuntimeError( RuntimeError: Engine process failed to start
sudo systemctl stop nvidia-persistenced.service sudo systemctl start nvidia-fabricmanager.service sudo systemctl start nvidia-persistenced.service
ilab data generate --num-cpus 4
serve: vllm: vllm_args: [--dtype bfloat16]
mkdir -p /var/lib/kdump/dracut.conf.d echo "dracutmodules=''" > /var/lib/kdump/dracut.conf.d/99-kdump.conf echo "omit_dracutmodules=''" >> /var/lib/kdump/dracut.conf.d/99-kdump.conf echo "dracut_args --confdir /var/lib/kdump/dracut.conf.d --install /usr/lib/passwd --install /usr/lib/group" >> /etc/kdump.conf systemctl restart kdump
sudo bootc upgrade --apply
cp /usr/bin/ilab <path-to-home-directory> vim ~/ilab
PODMAN_COMMAND=("podman" "run" "--rm" "-it" "--device" "/dev/infiniband" "--device" "/dev/accel" "--security-opt" "label=disable" "--net" "host" "--shm-size" "10G" "--pids-limit" "-1" "-v" "USDHOME:USDHOME" "USD{ADDITIONAL_MOUNT_OPTIONS[@]}" "--env" "HF_TOKEN" "--env" "HOME" "--env" "NCCL_DEBUG" "--env" "VLLM_LOGGING_LEVEL" "--env" "PT_HPU_ENABLE_LAZY_COLLECTIVES=true" "--entrypoint" "USDENTRYPOINT" "USD{IMAGE_NAME}")
sudo bootc upgrade --apply