Auto-populate and validate params specific to `vision_language_sft` collator in `TrainingConfig`

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-- Setup and validate params for `vision_language_sft` collator. The collator expects VLM SFT dataset to only produce one column: 'conversation_json' (JSON-encoded `Conversation`)! This PR auto-configures misc other params for  `vision_language_sft`

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Towards OPE-355, OPE-1109

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[docs] add security.md

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- Add security.md notice, based on [GitHub example](https://docs.github.com/en/communities/setting-up-your-project-for-healthy-contributions/creating-a-default-community-health-file)

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Upload scripts used in a Weekly Walkthrough

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Adding a script used in a recent walkthrough video for Oumi.
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N/A

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Minor logging improvements in `BaseMapDataset`

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-- Use effective `dataset_name` value
-- Only print `dataset_name`/`dataset_path` if non-empty to reduce clutter.

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Update VisionLanguageConversationFeatureGenerator

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-- Add type hints
-- Fix potential issue with passing multi-image  conversations to processor

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Towards OPE-355

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[tiny] Clean up datasets code

# Description

Remove duplicate code and clean up comments

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Qwen2.5 VL: Replace "from source" install with `transformers>=0.49`

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[Evaluation] Renaming `evaluation_platform` → `evaluation_backend`

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Renaming `evaluation_platform` → `evaluation_backend` for compatibility with PR-1484. Now that we released a new PyPI package (0.1.8) we can start using the new field name in our yaml files (ie., `evaluation_backend`) that is compatible with the latest release.
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Towards OPE-1097

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Added 3 Pixmo vision-language datasets

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Added 3 Pixmo vision-language datasets.
Since some of the image urls return 404 and 429, the integration test was modified to only test a subset of the training set.
For reference, added the yaml files used to test manually using skypilot but these should be deleted.
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Fixes #1401

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Evaluation: Inference optimizations

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Enable re-using the same inference engine across all tasks.

**_Exception_**: In the case of LM Harness: We need to destroy the engine, since LM Harness uses its own inference engine; a 2nd engine will allocate unnecessary resources, potentially increasing the memory footprint.
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Add QwQ full fine-tune and QLoRA configs

# Description

Should be similar to the existing LoRA config, with some modifications to the hyperparameters. Tested on GCP.

## Related issues

Towards #1408

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Mark `BaseMapDataset` as `typing.Sized`

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-- Type annotation change for clarity. Should be no-op otherwise..

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Towards N/A

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Switch eval yaml configs to use evaluation_platform

# Description

`evaluation_platform` was renamed to `evaluation_backend` throughout our codebase in #1484. This causes an error in all our eval configs at main, which use the YAML config from HEAD but the Oumi package from PyPi (which hasn't picked up this rename).

This PR:
- Renames `evaluation_backend` back to `evaluation_platform` to fix the breakage
- Updates `EvaluationTaskParams` so that `evaluation_backend` is not a required field. This will cause a breakage on main, as our configs don't provide this value anymore. Also updates post_init logic in this class to expect one of the two fields to be populated, not none or both.
- Fixes a test

After the next PyPI push, we can revert this change.

Tested that there's no errors both when using the PyPI package and using source.

## Related issues

Towards OPE-1097

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Add QwQ eval/infer configs

# Description

Copies the config from `configs/recipes/deepseek_r1/sft/distill_qwen_32b`, which is a reasoning model based on the same architecture (Qwen 2.5 32B).

Tested on GCP.

## Related issues

Towards #1408

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Add Qwen QwQ Lora config


[Evaluation] Instantiating an inference engine (if needed) when running custom evaluations

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Instantiating an inference engine (if needed) when running custom evaluations
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Towards OPE-1097

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Resolve warning about `--dispatch batches` deprecated param

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-- Use the new field accelerator_config
-- Tested with `configs/recipes/llama3_1/pretraining/8b/gcp_job.yaml` and `configs/recipes/vision/qwen2_vl_2b/sft/gcp_job.yaml`

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Fixes OPE-985

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Update sample GRPO script to validate num_generations

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-- Check if global batch size is divisible by num_generations . Otherwise, training may fail (depending on `trl` version)

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Fixes OPE-993

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Update docker image and build script

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- Fix syntax error with docker build script
- Update to use the latest oumi pip package
- Update build script to include a runtime test

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Fixes #1495

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[Evaluation] Save Utils: Moving, fixes, and unit tests

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Evaluation | Save Utils
- Moving 2 evaluation util files from `oumi/evaluation` to  `oumi/core/evaluation/utils`, since all the evaluation-related code has been moved there.
- Fixing an issue in `save_utils` occurring when overwriting a pre-existing dir.
- Adding unit tests for save_utils.
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Towards OPE-1097

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Minor logging improvements

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Towards OPE-1107

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Update the GPU Tests badge to use results from main

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After manually running our GPU tests, I've updated the test badge to use results only from the `main` branch.

You can preview the badge here: https://github.com/oumi-ai/oumi/actions/workflows/gpu_tests.yaml/badge.svg?branch=main
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N/A

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Add a schedule for our GPU, CPU, and doc tests

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In #1498 I overlooked that subactions would not be listed in the results page for their respective actions. This means that while my CI action ran all tests, they weren't reported for GPU and CPU tests, respectively.

This PR adds a simple workaround, just scheduling GPU, CPU, and doc tests to run once every 8 hours. Additionally, I've enabled the workflow so it can be triggered manually when needed.
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N/A

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# Description

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Support for deprecated input param (` evaluation_platform`) in order to not break any users that have taken a dependency on this.
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Towards OPE-1097

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Add recurring tests to keep our test badges updated.

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This PR adds a new `ci_tests` workflow that triggers both our CPU and GPU tests in parallel on the `main` branch.

Currently our test badges in our README reflect the latest tests run from pull requests. This means that problematic PRs will cause our tests to be listed as failing even when tests pass on `main`.

Follow up work:
- Update our badges to look for only tests run on the `main` branch.

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N/A

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Fix `log_samples` not propagating from `eval_kwargs`

Fix issue where `log_samples` kwarg is not able to be passed to lm_harness

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Fixes # (issue)

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Update documentation formatting for BaseModel

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The docstring for the forward method was malformed.

Before:
![Screenshot 2025-03-01 at 9 53 11 AM](https://github.com/user-attachments/assets/9342741c-4c40-4be7-bf15-9188b57f91ae)

After:
![Screenshot 2025-03-01 at 9 52 59 AM](https://github.com/user-attachments/assets/412d2054-f110-49f0-90c1-c79f5c813440)

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N/A

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Fix chat template issue for nested content parts used for VLMs

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-- Update chat templates to use `content` or `text` field in `ContentItem`, depending on what's available  to make it compatible with Oumi and OpenAI conversations
-- Revert temporary hacks in VLLM inference engines introduced in https://github.com/oumi-ai/oumi/pull/1486

Tested with VLLM inference engines. All chat template related unit tests pass.

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Fixes OPE-1090

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Minor updates to `oumi env`

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-- Include `CUDA_VISIBLE_DEVICES` and `sglang` version into env report.

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Define `GrpoParams` under configs

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-- Include initial set of params including  vLLM-related params.
-- Tested vLLM completions on 1 GPU.  Distributed  multi-GPU setup still needs work.

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Towards OPE-993

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[tiny] Update async_eval.yaml comments to reference correct class

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Fix a bug where overriding remote_params fails via the CLI (oumi infer)

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During inference, `oumi infer` would fail if the user specified any remote_params via the CLI for wrapper engines (engines that inherit from the RemoteInferenceEngine). This is because the merged config specified at inference time had `None` values for required API fields.

Updated the infer method to gracefully fallback to previous values if `None` values are detected at runtime.

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N/A

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Add sample DDP/GCP config for GRPO trainer

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Towards OPE-993

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Update oumi infer to fall back to interactive mode if no input path is specified.

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This is a Quality of Life change for the CLI. If the user doesn't specify interactive inference AND doesn't specify an input path, we'll default to interactive inference.

Additionally, we'll always prevent interactive inference if the user specifies an input file. This is in preparation for removing this flag. We should instead use the presence of the `input_file` as an indicator for whether or not we run interactive inference.

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Add notebook for fine-tuning MiniMath-R1-1.5B

notebooks/Oumi - Distill a Large Model.ipynb

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Add notebook outlining the training and evaluation steps for MiniMath-R1-1.5B
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Create an inference config for Claude Sonnet 3.7

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Runnabled by anyone, anywhere, with the command:

`oumi infer -c oumi://configs/api/anthropic/infer_claude3_7.yaml -i`
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[tiny]Update trl to 0.14

# Description

This is needed for the GRPO trainer., which was added in 0.14.0. We'd ideally like to upgrade to the latest, 0.15.1, which seems to have a decent number of updates to GRPOTrainer. However, 0.15.0 changes the SFTTrainer to only accept HF datasets or a ConstantLengthDataset. Our custom PyTorch datasets thus no longer work, and additional time is needed to do a migration to fix this.

Release history:
- 0.13.0: 12/16/24
- 0.14.0: 1/29/25
- 0.15.0: 2/13/25
- 0.15.1: 2/18/25

TRL release notes: https://github.com/huggingface/trl/releases. At a glance, there doesn't seem to be any other breaking changes for this version update.

Tested by running `configs/recipes/llama3_2/sft/3b_lora/gcp_job.yaml` and `configs/recipes/vision/llama3_2_vision/sft/11b_lora/gcp_job.yaml` as a sanity check.

## Related issues

Towards #1351

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Fix local models to not break the registry.

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Previously we were loading external deps using an arbitrary module name. This had the benefit of preventing of preventing module conflicts, but the downside of python not being able to recognize when a module was already imported.

For custom models, this led to a scenario where a module would be imported twice, causing python to try to register the model 2x with Oumi. This is currently a fatal error, and crashes out.

The new change temporarily appends the directory of the python module to the user's sys path (this is reset when the python session ends). This means local references to the module will now resolve properly. The main downside here is that users must be careful that their *.py files don't conflict with any other packages they need (like `pathlib`, `time`, etc).
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Fixes #1475

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Update the RemoteInferenceEngine to appropriately handle openai format batch prediction endpoints.

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If a user specifies an API endpoint that isn't the base endpoint (e.g. `https://api.openai.com/v1`) batch prediction will fail. Unfortunately, our wrapper engines like the OpenAIInferenceEngine specify paths of the form: `https://api.openai.com/v1/chat/completions`.

Additionally, not every engine supports batch requests (such as gemini). Updated the Gemini engine to simply return an error for `infer_batch`.

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Fixes #1465

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Updated all CLI endpoints to support oumi:// prefix

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Each CLI entrypoint calls the ```cli_utils.fetch_and_resolve_config``` function before proceeding with the existing logic. This resolves the path if it includes ```oumi://``` prefix and otherwise returns the existing path unchanged.
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Fixes #1444

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@taenin
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Enable pre-release install for uv in pyproject.toml

# Description

Unlike pip, uv disallows prelease packages by default, which we need for `omegaconf==2.4.0dev3` for `oumi>=0.1.5`. This PR fixes that in our pyproject.toml.

## Related issues

Fixes #1462

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Remove a temp workaround in `pad_sequences` on the left side

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-- Use built-in param after migration to `pytorch 2.5`

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Fixes OPE-644

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Minor cleanup of oumi fetch

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I've slightly refactored the `oumi fetch` logic to live in `cli_utils` to make testing a bit easier.
I've also removed the `output_dir` argument from the `oumi infer` cli as it was unclear that it was for downloading configs.

I also updated `oumi fetch` to fetch configs if the user omits the `oumi://` prefix.
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Related to #1386

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[Tiny] Update default training params for Qwen2-VL-2B-Instruct

# Description

Short experiments when training `Qwen2-VL-2B-Instruct` with `merve/vqav2-small` hint that decreasing the `max_grad_norm` results in more stable training, and increasing the  `weight decay` reduces overfitting.

We update these two hyper-parameters here.

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Fixes # Towards OPE-946

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[Tiny] Add more warnings for "special" requirements of Qwen2.5-VL

# Description

Adds explicit user warnings on the configuration and readme files regarding `Qwen2.5-VL-3B` and its dependency on the latest dev `transformers` version.

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Fixes # Towards: OPE-988

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Update oumi launch status to show clusters with no running jobs.

# Description

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Previously, running `oumi launch status` would omit running clusters if they had no jobs available.

This change lists running clusters, regardless of their child job status.
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N/A
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Renamed CALM to CoALM

# Description

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CALM was renamed to CoALM.
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Add support for Docmatix dataset to multimodal training script

# Description

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-- The dataset includes multi-image examples, good for testing this feature.
-- Add support for `--subset` param to the script.

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Towards OPE-355

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Require an inference config for oumi infer.

# Description

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Make the config parameter required for oumi infer.

Also updated the `oumi fetch` default directory to `~/.oumi/fetch`. Updated tests accordingly.
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Fixes #1442

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Create a script to calculate memory used during training

# Description

This is a Python script that will estimate the memory needed to run training. This is derived from the memory calculator Google Sheet Jeremy work. In the future, this can be integrated as a subcommand for the CLI, but leaving it as a separate script for now to reduce visibility as it's a WIP. The initial implementation works for GPT2.

## Related issues

Towards OPE-672

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Added fetch command and modified infer command to resolve oumi://

# Description

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A new CLI endpoint ```fetch``` has been added to allows users to directly download configs from the github repo. The same function is then reused to allow the resolution of ```oumi://``` to download a the config file from the github repo and provide the address of this newly downloaded config file for further use by the ```infer``` command.

A test has been added in the ```tests_cli_main.py``` to check for successful registration of the ```fetch``` entry-point.

A new test file has been added named ```tests_cli_fetch.py``` that has three tests that cover the cases of default directory, explicit ```--output-dir``` passing and passing output directory via ```OUMI_DIR``` environment variable.
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Fixes #1374 (issue)

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Set a better default for vllm inference GPU usage.

# Description

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Set the local VLLM inference to use 90% of GPU vRAM.

100% usage causes OOMs when small amounts of vRAM are witheld.
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N/A

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Create `pad_to_max_dim_and_stack()` function in torch_utils

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-- The function pads input tensors to maximum size in each dimension, then stacks them
-- This is similar to `pad_sequences()` except it can pad to more than one dimension, which is needed e.g., for `cross_attention_mask` which has two variable dimensions: `seq_len`, `num_images` for multi-image multimodal examples
-- The integration of this new function into data pipeline is coming in a separate PR.
-- Minor error logging improvements in padding utils

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Towards OPE-355

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WildChat-50M Reproduction

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Adding a project containing the configs necessary to reproduce the UltraChat baseline from the WildChat-50M paper. Resolving https://linear.app/oumi/issue/OPE-1050.

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Enable ability to override list values in config via CLI

# Description

## Problem

Previously, we could not override config values that were within a list field via the CLI, ex. `oumi evaluate -c configs/recipes/smollm/evaluation/135m/eval.yaml --tasks[0].num_samples=1`, getting the error `omegaconf.errors.ConfigTypeError: Cannot merge DictConfig with ListConfig`. This is not a problem with using the wrong CLI syntax, but instead with how we were parsing the CLI overrides.

Currently, we create a config from the args list with `OmegaConf.from_cli(arg_list)`, and then merge it with the YAML config via `OmegaConf.merge(*all_configs)`. The YAML config is an OmegaConf structured config, meaning it's aware of the typing of our Config object, `EvaluationConfig` in this example. The problem is that the config from args list is created before it's merged with the YAML config, so it is not aware of the typing. In the above example with `--tasks[0].num_samples=1`, it's thus not aware that `tasks` is a List, and creates a dict instead: `{"tasks": {"0": {"num_samples": 1}}}`.

## Solution

The solution is simply to call `config.merge_with_dotlist(arg_list)` instead, which merges each argument into the config individually. With this method, the `0` is properly interpreted as a list index instead of a dict key.

I've tested that this does not impact existing behavior. I've also verified that many variants of specifying overrides, which are Pythonic and we expect to work, do work. Here's some example args for the config mentioned in the first example:

```
# Bracket notation
--tasks[0].num_samples=1
# Dot notation
--tasks.0.num_samples=null
# Override existing key in dict
--tasks.0.eval_kwargs.num_fewshot=1
# Add new key in dict
--tasks.0.eval_kwargs.foo=bar
# Merge dict values within the list entirely
# Note this doesn't override the dict, as OmegaConf merges dicts by default
--tasks.0.eval_kwargs="{'foo':'bar', 'baz':2}"

```

NOTE: This PR doesn't support the ability to add new elements in the list. This is currently not supported by OmegaConf: https://github.com/facebookresearch/hydra/issues/1547#issuecomment-1235918733

## Related issues

Fixes OPE-934

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Provide example show to start SGLang server using Docker

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-- Docker should be the recommended way to start SGLang, as Docker pulls pre-tested server configurations and avoids Python dependency conflicts.
-- Tested locally

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Towards OPE-989

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