[docs] Update index page

# Description

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[Notebook] Evaluation with Oumi

notebooks/Oumi - Evaluation with Oumi.ipynb

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Notebook: Evaluate with Oumi (LM Harness & AlpacaEval 2.0) for 3 models (LLaMA 1B, 3B, 8B)
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Fixes # OPE-913

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gpt2: move `include_performance_metrics` param from script to yaml

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Move configs to experimental

# Description

After we fix them, we can move them back to their original locations.

## Related issues

Towards OPE-929
Towards OPE-818

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Simplify inference engine API

# Description

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- The goal of this PR is to simplify the inference engine API.
  - Users can now set their generation, remote and model params in the engine constructor
  - Users can optionally override `engine.infer(inputs, config=config)`, but by default sending a config is now optional
- Fix a regression in the unit tests that was making unnecessary network calls to check if the model is a vlm. This reduced the runtime by 50%
- Simplify remote engines, which now only have to set the `base_url` instead of adding a constructor.

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Fix most job configs

notebooks/Oumi - Using vLLM Engine for Inference.ipynb

# Description

This PR fixes almost all Oumi job configs to be able to run. Those that require non-trivial fixes will be moved to experimental in a follow-up PR, and will be resolved at a later time.

Fixes made to config files (not comprehensive):
- Delete Mistral config. It was OOMing, and there was no specific reason at the time of creation to use Mistral over another model we support like Llama
- Add tokenizer pad tokens
- Comment out GCS bucket mounting. This way the config runs out of the box, and users can opt into mounting a bucket if desired.
- Switch some eval jobs from 4 GPUs to 1 GPU, given the bug with data-parallel LM Harness mentioned in OPE-931

Fixes made to platform (requires a version update to fix):
- Delete unneded parameter in `src/oumi/datasets/sft/chatqa.py`

I've manually tested all job configs to ensure they work (with the shortcut of re-using a VM with the same accelerator setup to test multiple configs). The only broken configs are Zephyr and FSDP DPO Phi3.

## Related issues

Towards OPE-818
Towards OPE-931

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[BugFix] Throw a runtime error for quantized models & inference=VLLM

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Throw a runtime error when instantiating a {judge, evaluation, inference} config that includes a "BitsAndBytes" quantized model and a VLLM inference engine. I am aware this does NOT cover all possible use cases (users may use an inference engine directly without a config) but my hope is that it covers most cases.
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Exclude `multi_gpu` tests from GitHub GPU tests

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Towards OPE-698, OPE-909

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Add wrappers for remote inference engines

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- Add default remote params for engines that have one (e.g. openai, anthropic, parasail, etc)
- Add new inference engine classes for OpenAI, Together, DeepSeek.
- Update InferenceEngineType and builder function to include all supported engines

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Closes OPE-486

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[docs] Add inference snippet for together.ai and DeepSeek APIs

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- Add inference snippet for together.ai and DeepSeek APIs

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Closes OPE-774, OPE-458

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[Evaluation] Bug: serialization

# Description

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Details at https://linear.app/oumi/issue/OPE-924

**_Current experience_**

Display message:
```
[2025-01-22 14:15:40,787][oumi][rank0][pid:10457][MainThread][WARNING]][serialization_utils.py:33] Non-serializable value `functools.partial(<function format_cot_example at 0x360f99e40>, including_answer=True)` of type `<class 'functools.partial'>`.
```

Saved file:
```
      "fewshot_config": {
        "sampler": "first_n",
        "doc_to_text": "functools.partial(<function format_cot_example at 0x360f99e40>, including_answer=True)",
        "doc_to_target": ""
      },
```
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GCP tests launcher script changes

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-- Define `multi_gpu` test marker, and update Launcher script to  use it
-- Switch to editable install

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Towards OPE-698, OPE-909

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Add newline before `pformat(train_config)`

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-- This results in slightly-better formatting in logs, and often done for other `pformat()`-s in oumi codabse

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Update tests to make them runnable on GCP

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-- Add GCP Launcher script for running tests.
-- Minimal changes to making tests pass on GCP multi-GPU machine.
-- New utils to get locations of `testdata`, `notebooks`  (`get_oumi_root_directory().parent...`  leads to `miniconda3` location for `oumi` package `~/miniconda3/lib/python3.10/site-packages/oumi` vs `/home/gcpuser/sky_workdir/`)
-- Tested on GCP `A100:4`, `A100:1`, locally, and on Github
-- Minor improvements.

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Native inference: Don't set `min_p`, `temperature` in `GenerationConfig` if sampling is disabled

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-- This leads to verbose warnings
-- There is a chance it may contribute to observed non-determinism in native inference integration tests

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Fixes OPE-921

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Define `single_gpu` test marker

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-- Define `single_gpu` test marker  to allow skipping such tests on multi-GPU machines if needed.

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Configs Train/Infer/Eval and Llama 3.3v (70b)

# Description
Adds configurations to train/infer/eval 3.3. Llama 70b (text only) models.

- We mirror the hyper-parameters we used in the 3.1 version of Llama 70b model.

 - Noticeable exception which should not affect the underlying compute demands: `max_grad_norm` was set to null (instead of defaulting to "1") to better much, torchtune recipes. Also, the default `sharding_strategy` was explicitly set to "FULL_SHARD" to increase readability/transparency.

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Fixes OPE-851

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[docs] Add more details and cross-references related to customization

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-- Add cross-references for "CNN Classifier" notebook, Numpy dataset, and misc customization documentation

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[Cherrypick for launch] Evaluate: return dict of results

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Evaluate: return dict of results
(!) Not supported for evaluate async @taenin

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# Description

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Documentation: Judge | Custom Model page
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[docs] Resolve PyRight issues in NpzDataset in custom dataset example

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Towards OPE-639

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Bugfix in CnnClassifier example

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-- Fixed few cases when `kernel_size` param is ignored

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Towards

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[tiny] Add error message to bare exceptions

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-  Add error message to bare exceptions

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[WIP] Define `default_dataset` property for all pretraining datasets

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Towards OPE-897

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Support `dataset_name_override`  dataset_kwarg

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-- Update `oumi_data.py` to be consistent with `data.py` for `dataset_name`
-- If `dataset_name_override` is defined in `dataset_kwargs` then use that value over `dataset_params.dataset_name`. This also removes the potential `duplicate kwarg` error for `dataset_name` key

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[tiny] Raise explicit error for models not supporting infer

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- Currently we get an obscure error, this PR raises a more descriptive exception for models not supporting infer

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[docs] fix misc broken links

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-  Fix misc broken links

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# Description

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[docs] Add initial page how to define datasets for custom data types

# Description

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-- Add a stub page, and an example how to define custom dataset to load from `.npz` files
-- More changes will follow

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Add `CnnClassifier` custom model

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-- Adding CNN example (in addition to `MlpEncoder`) to show Oumi can be used for non-text models
-- The example may be referenced from docs and/or notebooks later.

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Training vlm llama 90b

# Description

Adds a basic configuration tested to be trainable with Oumi and Llama-3.2-90B-Vision-Instruct.

- Many parameters were chosen to reduce the memory footprint almost maximally. (E.g., using an 8-bit adam, batch_size=1, etc.).

**Note:**
1. Even setting `gradient_accumulation_steps` to `2` will result in OOM. Activating `cpu_offload` or deactivating prefetches (`forward_prefetch`, `backward_prefetch`) will not enable to increase the _effective batch size_.
2. Surprisingly,  activating `enable_gradient_checkpointing` results in faster training (logged in OPE-906).

Towards: OPE-688

Relates and can also be the basis for investigations per OPE-906 and OPE-905.

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OUMI Trainer: Improve handling of `num_training_epochs` and update `_get_total_training_steps()`

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-- Update stopping condition to use `params.max_steps` exclusively  if specified
-- Update `_get_total_training_steps()` to cover more cases. Previously it could return a negative number if `max_steps` isn't set, leading to completely wrong LR schedule which doesn't converge.

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Towards OPE-904, OPE-811

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Update readme

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- Update readme

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OUMI Trainer: Support non-text models

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-- Mark `tokenizer` as `Optional`
-- Only count tokens if tokenizer is not None, and `input_ids` are present

The changes are needed to support non-textual models

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Documentation: Judge |  Built-In page

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# Description

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Update GitHub actions markers expression

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-- Exclude `e2e_eternal`

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Define `e2e_eternal` test marker

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-- One level above `e2e` in size and slowness

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Removed unnecessary cloud.md which is already covered in depth in another section.

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Update train.yaml

Add comment and spacing clarifying comments

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Documentation: Judge | Judge (main) page

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Final (i.e., 95% ready) version for Judge's main documentation page

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Exclude unit/integration tests explicitly marked as `e2e` from GitHub runs

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-- Gives us a quick way to exclude tests (vs `skip()`)

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Update style_guide.md

Removed numbering, as the heading sizes make this clear for such a short document. Added link.
Minor rewrites

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Update contributing.md

Added emoji
Added links
Minor verbiage edits
Updated copyright date to 2025

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Update quickstart.md Removed H1 emoji

Until we determine / add emoji to all H1 (single "#") at the top of each doc, better to leave this one out. It sticks out in the left nav as being the only one with a lead emoji for the moment.

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Update training_methods.md

Re-order to have JSON before Python for consistency

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Update train.md

Made some minor corrections

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[docs] Improve docs for custom multimodal SFT datasets

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-- Make the example more realistic by providing an example how to specify raw HF dataset, and use image bytes instead of image path (more common)
-- Change custom dataset name for consistency with other examples on the page
-- Minor descriptions update.

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Towards OPE-639

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[docs] Update the list of commands on Core Concepts page

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-- Add `env` and `distributed` and add descriptions
-- Format transformers library as `transformers`

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[docs] Add a description to the oumi env command.

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Add a short description to our `oumi env` command.
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[docs] Put `-m oumi train` on single line

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updated quickstart with clearer instructions about setting up cloud

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Rewrote the Launching Jobs section of the Quickstart to make it clearer.

* Specified that this is for launching cloud jobs
* Added links to GCP and Skypilot documentation and some getting started commands
* Added clearly marked warnings about the risks of running cloud jobs (e.g., cost, data loss)

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add max_memory_per_gpu and device_map params to lm eval harness

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LM evaluation harness accepts device_map and max_memory_per_gpu kwargs, and these are very helpful when you're trying to evaluate large models. This PR passes through those kwargs to LM evaluation harness.

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## Before submitting

- [ ] This PR only changes documentation. (You can ignore the following checks in that case)
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- [ ] Did you add / update tests where needed?

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Add a comment to Omegaconf yaml files with config info

# Description

This comment identifies the config class that will load the YAML file, for self-documentation purposes. It also renames two files from `*_gcp_job_eval.yaml` to `*_gcp_job.yaml` for consistency, and updates some pip installs to use uv.

## Related issues

Fixes OPE-894

## Before submitting

- [x] This PR only changes documentation. (You can ignore the following checks in that case)
- [ ] Did you read the [contributor guideline](https://github.com/oumi-ai/oumi/blob/main/CONTRIBUTING.md) Pull Request guidelines?
- [ ] Did you link the issue(s) related to this PR in the section above?
- [ ] Did you add / update tests where needed?