Evaluation Notebooks: MT Bench

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Evaluation Notebooks: Add support for MT Bench

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Fixes OPE-730

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Fix missing import precommits when you create a fresh install of Oumi

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When running a vanilla install of oumi, I get a precommit error if I don't install GCP bits.

My initial install was:
```
pip install -e ".[dev]"
```

We simply need to ignore these deps if they're not present as they're in the optional GCP package.

Complete repro steps:

```
conda create -n oumi_clean_test python=3.11 -y
conda activate oumi_clean_test
pip install -e ".[dev]"
(MAKE A CHANGE TO A FILE)
git add -A
git commit -m "Some change."

/Users/matthewpersons/Repos/oumi/src/oumi/inference/gcp_inference_engine.py
  /Users/matthewpersons/Repos/oumi/src/oumi/inference/gcp_inference_engine.py:20:18 - error: Import "google.auth" could not be resolved (reportMissingImports)
  /Users/matthewpersons/Repos/oumi/src/oumi/inference/gcp_inference_engine.py:21:18 - error: Import "google.auth.transport.requests" could not be resolved (reportMissingImports)
  /Users/matthewpersons/Repos/oumi/src/oumi/inference/gcp_inference_engine.py:22:18 - error: Import "google.oauth2" could not be resolved (reportMissingImports)
```

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Update inference documentation

notebooks/Oumi - Finetuning Tutorial.ipynb

notebooks/Oumi - Multinode Inference on Polaris.ipynb

notebooks/Oumi - Running Jobs Remotely.ipynb

notebooks/Oumi - Using vLLM Engine for Inference.ipynb

# Description:

- Update inference documentation
- Fix and update the two inference notebooks and the tour notebook
- Use SmolLM2 instead of GPT2 in our tour notebook
- Rename config created by notebook so it'll be covered by our gitignore, and update gitignore to also include files with `tutorial` in the name.

Tested that the three notebooks mentioned above work E2E.

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Update VLM configs to use `oumi distributed torchrun`

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-- Update VLM configs to start using`oumi distributed torchrun`
-- Fix handling of  `accelerate launch` sub-command.
-- Minor changes in  `distributed_run.py`: Update command docstring, log rank, add docstring for ports.

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Towards OPE-691

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Freeze torchdata>=0.8.0,<0.10.0

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Apparently, `torchdata.datapipes` is removed in `0.10.0` , which causes test breaks.

```
/home/runner/work/oumi/oumi/src/oumi/builders/oumi_data.py
  /home/runner/work/oumi/oumi/src/oumi/builders/oumi_data.py:5:6 - error: Import "torchdata.datapipes.iter" could not be resolved (reportMissingImports)
  /home/runner/work/oumi/oumi/src/oumi/builders/oumi_data.py:10:6 - error: Import "torchdata.datapipes.map.util.converter" could not be resolved (reportMissingImports)
2 errors, 0 warnings, 0 informations

```

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Alpaca Eval 2.0 notebook

notebooks/Oumi - Evaluation with AlpacaEval 2.0.ipynb

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Alpaca Eval 2.0 notebook

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Towards OPE-424

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Move multimodal configs from `configs/examples/vision` to `configs/recipes/vision`

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-- Also update paths in README.md

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Fixes OPE-716

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Improve error messaging for the remote inference engine.

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Pass the latest HTTP error as part of our exception messaging.

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Fixes OPE-719

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[tiny] fix remote inference bug for messages with image urls

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- Fix issue converting image turns to openai format

**Before**
Turn `image_url` key is rendered as a `set`, which is not json serializable, and not what the openai api expects

```json
{'type': 'image_url', 'image_url': {'https://upload.wikimedia.org/wikipedia/commons/1/15/Cat_August_2010-4.jpg'}}]
```

**After**
Turn content is rendered as a `dict`

```json
[{'type': 'image_url', 'image_url': {'url': 'https://upload.wikimedia.org/wikipedia/commons/1/15/Cat_August_2010-4.jpg'}}]
```

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Cap TRL library version to <0.12.2

In case anyone runs into this error:

TypeError: Trainer.__init__() got an unexpected keyword argument 'processing_class'

TRL's latest update breaks us:
https://github.com/huggingface/trl/releases

The fix is to prevent TRL from upgrading:
"trl>=0.9.0,<0.12.2",

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Update documentation landing page

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- Update documentation landing page to match the updated GitHub readme changes
- Convert RST sphinx index to markdown for easier transfer from the GitHub readme

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Towards OPE-708

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Adding support for Alpaca Eval

# Description:
Adding support for Alpaca Eval

## Related issues
Fixes # N/A

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## Reviewers
Will, Oussama


Update the list of supported VLM-s in README

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-- Move Llama3.2 Vision to the top
-- Replace BLIP with Phi3 (there is some regression affecting BLIP, so removed it temporarily)

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Update recipes list in readme

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- Update readme recipes list
- Many of these resources need to be updated / refresh, and we're missing a few others. For now, this PR just updates the existing list of examples / recipes / notebooks

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Towards OPE-708

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Refresh documentation pages

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- Misc updates / fixes in our documentation

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Update readme sections (part 2)

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- Update remaining readme sections, and remaining feedback from part 1 PR.
- Still WIP: table with supported models & notebook (in a follow-up)

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Update main readme (part 1)

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- Update & refresh main readme

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Tweak writer batch size computation in `BaseMapDataset`

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-- It's a follow-up to https://github.com/oumi-ai/oumi/pull/868 with some improvements.

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Towards OPE-569

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Rewrite CONTRIBUTING.md with new content, fixes and nits

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Update dev_setup.md with minor clarification for use of SSH in GitHub


Update base config to_yaml signature

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- Small change to allow using `Path` and `StringIO` as inputs to the `BaseConfig.to_yaml`. This is just to avoid linting errors as the underlying function supports it

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Update inference engine docs.

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Small changes to give a better description of our inference engines.

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Initial port of zigzag attention

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-- Based on `oelachqar/dist_attention`,  which is based on  https://github.com/jzhang38/EasyContext, https://github.com/zhuzilin/ring-flash-attention/
-- Includes minor updates to resolve pre-submit and `PyRight` errors.
-- The code is not enabled yet.

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Towards OPE-333, OPE-334

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Change config dir structure  for LLAVA and blip2 models

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-- For consistency with other models, and to make it easier to add larger model sizes

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Towards OPE-688

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Enable LM-harness eval for multimodal, and add eval config for Llama 3.2 Vision 11B

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-- Update `src/oumi/evaluate.py` to invoke `lm-eval` with multimodal specific params
-- Eval for Llama 3.2 11B model, using MMMU benchmark
-- GCP Launcher configs for 1 node with 4 A100 GPUs.
-- Re-organized directory structure for Llama 3.2 11B model for train/eval/infer configs

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Towards OPE-649

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Update  Llama 405B qlora config to use `adamw_torch_fused` optimizer

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-- Replace `sgd`  with `adamw_torch_fused`, as training loss doesn't seem to decrease with `sgd`
-- `sgd` was introduced in https://github.com/oumi-ai/oumi/pull/838 . Reverting the change.
-- Tested on GCP: 1 node with 8 `A100-80GB`
-- Also, reverting max sequence length from `1028` back to `2048`.  It works OK e.g.,  https://wandb.ai/lema-train-test/oumi-train/runs/7tzsaddn

with  `adamw_torch_fused`. Loss decreases:
https://wandb.ai/lema-train-test/oumi-train/runs/5w46zd2l
https://wandb.ai/lema-train-test/oumi-train/runs/wwd2e3kg

Before (`sgd`):
https://wandb.ai/lema-train-test/oumi-train/runs/pwz7gasu
https://wandb.ai/lema-train-test/oumi-train/runs/1pne1jch

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Towards OPE-671

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Fix our config parsing test

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After our recent restructuring of our test folder, our config parsing test was silently running on 0 configs.
Updated the test to fail in that case. Updated the test to properly point to the repo root again.

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Fixes for quickstart and launch commands.

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This PR comes in two parts:

1) Update the quickstart to take less than 10 minutes e2e. Training and eval are not really useful for this demo, but a user can see the jobs start, run, and generate results.

We also now show configs inline on the quickstart page
![image](https://github.com/user-attachments/assets/45a09319-0d48-4434-a25d-595665dcebe9)

![image](https://github.com/user-attachments/assets/26574cfe-e00c-423d-87be-3defa52a17f0)

2) Update several CLI commands to clear the previous line when printing. This cleans up miscellaneous issues when polling jobs.

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# Description:

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- Update RemoteInferenceEngine to support guided decoding
- This leverages the OpenAI structured output api `https://platform.openai.com/docs/guides/structured-outputs`

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Closes OPE-685

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[tiny] fix openai remote engine auth bug

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- Bug generating the auth headers for remote inference engine when using `api_key_env_varname`

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Add `@override` annotations to misc inference engines methods where missing

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-- also add `pyright: ignore[reportMissingImports]` to `vlm.GuidedDecodingParams`

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Towards OPE-370, OPE-627

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Add support for remote vllm inference engine

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- This PR adds a new `RemoteVLLMInferenceEngine` class that enables running inference against remote VLLM servers.
- Compared to `RemoteInferenceEngine`, it supports the additional parameters that can be used by `vllm`, e.g. structured decoding

#### Usage
```python
from oumi.inference import RemoteVLLMInferenceEngine
from oumi.core.configs import ModelParams, GenerationParams

engine = RemoteVLLMInferenceEngine(
    ModelParams(
        model_name="your-model",
        remote_params={
            "api_base": "http://your-vllm-server",
        }
    )
)
```

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[tiny] fix typo in docs/quickstart

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- fix typo in docs/quickstart

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[tiny] pass dataset_path to constructor

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- Fix a tiny bug constructing datasets: the `dataset_path` argument was not passed to the dataset constructor

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Documentation fix for training.

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Fix a typo in local dataset loading.

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Add a debug command for dumping environment information.

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This PR adds the CLI command `oumi env` which dumps information about the user's environment.

Sample output (from my mac):
```
----------Oumi environment information:----------

Oumi version: 0.1a1.dev252+g84436cbc.d20241112
Python version: 3.11.9
Platform: macOS-14.7.1-arm64-arm-64bit

Installed dependencies:
PACKAGE             VERSION
accelerate          1.0.0
aiohttp             3.10.9
bitsandbytes        0.42.0
datasets            3.0.0
diffusers           <not installed>
einops              0.8.0
jsonlines           4.0.0
liger-kernel        <not installed>
lm-eval             0.4.5
numpy               1.26.4
nvidia-ml-py        12.560.30
omegaconf           2.3.0
open_clip_torch     <not installed>
pandas              2.2.3
peft                0.12.0
pexpect             4.9.0
pillow              10.4.0
pydantic            2.9.2
responses           0.25.3
skypilot            0.6.1
tensorboard         2.17.1
timm                <not installed>
torch               2.4.0
torchdata           0.8.0
tqdm                4.66.5
transformers        4.45.2
trl                 0.11.0
typer               0.12.5
vllm                <not installed>
wandb               0.17.7

PyTorch information:
CUDA available: False
```

Sample on GCP:
```
----------Oumi environment information:----------

Oumi version: 0.1a1.dev255+gb8d24207.d20241112
Python version: 3.10.13
Platform: Linux-5.10.0-26-cloud-amd64-x86_64-with-glibc2.31

Installed dependencies:
PACKAGE             VERSION
accelerate          1.1.1
aiohttp             3.10.9
bitsandbytes        <not installed>
datasets            3.1.0
diffusers           <not installed>
einops              0.8.0
jsonlines           4.0.0
liger-kernel        0.4.0
lm-eval             0.4.5
numpy               1.25.2
nvidia-ml-py        11.495.46
omegaconf           2.3.0
open_clip_torch     <not installed>
pandas              2.0.3
peft                0.13.2
pexpect             4.8.0
pillow              10.4.0
pydantic            2.9.2
responses           0.25.3
skypilot            0.7.0
tensorboard         2.18.0
timm                <not installed>
torch               2.4.0
torchdata           0.9.0
tqdm                4.67.0
transformers        4.45.2
trl                 0.11.4
typer               0.9.0
vllm                0.6.3.post1
wandb               0.18.6

PyTorch information:
CUDA available: True
CUDA version: 12.1
Number of GPUs: 1
GPU type: NVIDIA A100-SXM4-40GB
CUDNN version: 90.1.0
```

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Fixes OPE-615

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Additional improvements to 405B

# Description:

- For Polaris training, download the model weights from Eagle to worker disk, reducing the total time needed to load the model from 3 hours to ~20 minutes.
- Update 405B training params to reduce memory consumption/increase nodes
- Move `bnb_4bit_quant_storage` to base config
- Refactor `llama_tune.sh` since we only need to swap out the config file
- Remove model copying from `llama_tune.sh` for 8B/70B. These aren't really necessary since the models are relatively small, and the model only has to be downloaded once
- Exclude downloading the `original/*` subdir for HF Llama models, since they're redundant copies of the weights. Excluding this reduces the amount of downloaded files by 2-3x and speeds up download time.

## Related issues

Towards OPE-671

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Request user feedback after training.

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Fixes OPE-680

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Add options for specifying log levels in the CLI.

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This PR adds the `--log-level` / `-log` argument to all CLI commands.

Unfortunately there is no good solution in Typer currently for sharing options across all commands: https://github.com/fastapi/typer/issues/153

I took this approach as it feels the most natural for the user.

Sample help command:
![image](https://github.com/user-attachments/assets/4f0989dd-8d22-429d-ab19-cb3cae8c03d8)

Sample invalid value:
![image](https://github.com/user-attachments/assets/ad0ce387-d7ea-4015-a983-25ca0fedd1aa)

Sample valid value:
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Fixes OPE-669

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Switch from 8-bit AdamW to AdamW for FSDP Lora/QLora

# Description:

We get an error with saving the model with 8-bit AdamW when doing FSDP Lora/QLora training:

```
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 2918, in _maybe_log_save_evaluate
(llama405b-qlora-fsdp, pid=17187) [rank0]:     self._save_checkpoint(model, trial, metrics=metrics)
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 3012, in _save_checkpoint
(llama405b-qlora-fsdp, pid=17187) [rank0]:     self._save_optimizer_and_scheduler(output_dir)
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 3150, in _save_optimizer_and_scheduler
(llama405b-qlora-fsdp, pid=17187) [rank0]:     save_fsdp_optimizer(
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/accelerate/utils/fsdp_utils.py", line 186, in save_fsdp_optimizer
(llama405b-qlora-fsdp, pid=17187) [rank0]:     optim_state = FSDP.optim_state_dict(model, optimizer)
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py", line 1873, in optim_state_dict
(llama405b-qlora-fsdp, pid=17187) [rank0]:     return FullyShardedDataParallel._optim_state_dict_impl(
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py", line 1284, in _optim_state_dict_impl
(llama405b-qlora-fsdp, pid=17187) [rank0]:     return _optim_state_dict(
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
(llama405b-qlora-fsdp, pid=17187) [rank0]:     return func(*args, **kwargs)
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py", line 1975, in _optim_state_dict
(llama405b-qlora-fsdp, pid=17187) [rank0]:     fsdp_osd_state = convert_fn(
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py", line 1798, in _convert_state_with_orig_params
(llama405b-qlora-fsdp, pid=17187) [rank0]:     _gather_all_orig_param_state(
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py", line 1692, in _gather_all_orig_param_state
(llama405b-qlora-fsdp, pid=17187) [rank0]:     output_states = _allgather_orig_param_states(
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py", line 1522, in _allgather_orig_param_states
(llama405b-qlora-fsdp, pid=17187) [rank0]:     dtype, state_buffers = _convert_all_state_info(
(llama405b-qlora-fsdp, pid=17187) [rank0]:   File "/opt/conda/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py", line 1381, in _convert_all_state_info
(llama405b-qlora-fsdp, pid=17187) [rank0]:     assert dtype == info.dtype
(llama405b-qlora-fsdp, pid=17187) [rank0]: AssertionError
```

This PR also adds `responses` to our pyproject since it's required by `tests/unit/utils/test_image_utils.py`.

## Related issues

Towards OPE-671

## Before submitting
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Add Llama405B Qlora/Lora/FFT configs

# Description:

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- Add initial Llama405B Qlora/Lora/FFT configs. Lora/FFT are still untested due to difficulty scheduling on Polaris. In Polaris, we update an env variable to point to Eagle as the HF cache dir, since the model is too big to fit in the user's `/home` dir.
- Update params for Llama 8B/70B
- Rename SFT to FFT in more places
- Update Polaris cluster to use YYYYMMDD instead of DDMMYYYY timestamp

Tested 405B Qlora training on GCP and Polaris. Everything runs slow given model size

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Towards OPE-671

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Update our documentation to include modules for directories that aren't packages.

# Description:

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This PR comes in several parts.

1. Updated the jinja template for apidoc to generate documentation for directories that are namespaces but not packages if they do not contain any subpackages. This is suitable for leaf directories in our code base.

2. Rerun `make docs-rebuild`. We should consider not tracking any generated docs under `/docs`. I'll consider addressing this later.

3. Fix several broken links in docs / notebooks.

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Misc documentation updates for installation, quickstart, and tutorials.

# Description:

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This is an initial first pass on our installation / quickstart documentation. I've run most commands and fixed those with typos.

I also updated our suggested install path to `local` as we don't have pip integration with our latest bits yet.

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Update quickstart to use the new CLI arg format.

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Update quickstart to use the new `-arg value` format for overriding values.

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Correct names of few env vars in `llama_tune.sh`

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--Update  names of few env vars in llama_tune.sh

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Towards OPE-362

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Fix a small typo in quickstart

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Update an old config reference.

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Update pretest.yaml

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Only run unit tests during presubmit runs.

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Update installation.md

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Add instructions for installing via SSH.

New UI:
<img width="964" alt="image" src="https://github.com/user-attachments/assets/4e0bcea8-653b-4d84-9841-efb0fda4e347">

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Add extra logging in Polaris client

# Description:

-- Add extra error logging in Polaris client.
-- Include time durations.
--  Should not affect happy path.

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Towards OPE-670

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Disable uv in polaris launcher

# Description:

-- Disable uv in polaris launcher temporarily. `uv pip install` doesn't seem effective leading to `Error while finding module specification for 'oumi.train' (ModuleNotFoundError: No module named 'oumi')`

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Towards OPE-670

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## Before submitting
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- [x] Did you link the issue(s) related to this PR in the section above?
- [ ] Did you add / update tests where needed?

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Migrate configs to use Oumi instead of Accelerate configs

# Description:

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Following #803, we now migrate our configs to use Oumi `FSDPParams` instead of a separate Accelerate config file, unifying everything under `TrainingConfig`. There should be no functional change from this PR.

This PR also does misc. cleanup in our configs.

I tested the following subset of configs to ensure the total training time is unchanged:
- `configs/examples/fineweb_ablation_pretraining/fsdp/gcp_job.yaml`
- `configs/examples/vision/llama3_2_vision/gcp_job.yaml`
- `configs/recipes/llama3_1/sft/8b_lora/fsdp_gcp_job.yaml`
- `configs/recipes/phi3/dpo/fsdp_sky_job.yaml`

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Fixes OPE-362

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

## Reviewers

At least one review from a member of `oumi-ai/oumi-staff` is required.

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