Add Conversation-Level Analysis

# Description
This PR adds dual-level conversation analysis - enabling both message-level and conversation-level analysis of datasets.

Main changes:
* SampleAnalyzer has a analyze_sample method (instead of analyze_message) which computes both message level and conversation level metrics and returns tuple[list[MessageAnalysisResult], ConversationAnalysisResult]
* LengthAnalyzer implements SampleAnalyzer in a way that conversation level metrics are the aggregate of message level metrics for all of the metrics (char, word, sentence) except for token. For token, it tokenizes the conversation directly using the datast.tokenize
* dataset is passed to the LengthAnalyzer so it can use it for tokenziation
* we can remove the tokenizer in a follow up PR since we are going to use the dataset directly
* All of the tests are updated accordingly
* The check for sample_count has been moved to analyze_config

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Fixes OPE-1455

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Update dataset planner to use dependency injection

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[tiny] Pin exact omegaconf version

# Description

See attached Linear issue for details. tl;dr installing oumi with uv often won't work because it is stricter than pip and won't download prerelease packages (i.e. omegaconf) with our current version specifications. Pinning an exact omegaconf version should fix this.

## Related issues

Fixes OPE-1476

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Update README.md with v0.3 release

Adding v0.3 release to GH news


Temporarily disable python 3.13 support to unblock pip release

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[bug] use number of slurm visible gpus instead of hardcoded


notebooks/Oumi - Quantization Tutorial.ipynb

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- [tiny] remove deprecated param from quantization notebook

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[Quantization] tutorial quantization (part 8)

# Description

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add quantization tutorial, including quantization tool and inference demo with quantized models.

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Towards: OPE-1425

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[docs] Add quantization docs to index, minor cleanups


[docs] Skip :noindex duplicate warnings

# Description

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- When generating the docs, we get a lot of warnings that are not particularly useful and clutter the output
- This PR adds a log filter to skip logging them.

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Add notebook for bulk inference

notebooks/Oumi - Bulk Inference of LLM APIs.ipynb

# Description

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Fixes OPE-1413

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Judge API V2 | Lower inference temperatures

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Lower judgment inference temperatures to 0
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Towards OPE-1327

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Fix pyNVML not supported error

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Some devices cause pynvml to raise a not supported error, right now we log these as exceptions and return None, but instead we should log them with debug and exclude them from returning. This way we return everything we can without raising exceptions for something out of our control.
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Fixes #1877

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# Description

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- Update hf vision dataset class to also load local datasets
- If provided a local path, load from disk using `_load_local_dataset`, else load remote dataset

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[tiny] Enable rich logging by default

# Description

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- Enable rich logging by default
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Update README.md to add news of adding Qwen 3 Recipe

# Description
Add a line in the News about the new Qwen 3 Recipe

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Add Qwen3 235B inference configuration

# Description

Add Qwen3-235B-A22B-2507 inference config using Together.ai

Model on Together.AI: Qwen/Qwen3-235B-A22B-Instruct-2507-tput

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remove webinar ad, add webinar recording link

# Description

Remove links to July webinar signup page, and add link to YouTube recording


Update mlflow dependency to >=3.1.4

## Summary
- Updates the mlflow dependency in pyproject.toml from `>=2.21.2,<2.22.0` to `>=3.1.4`
- This is a minor dependency update to allow for newer versions of mlflow


[tiny] Install torchdata from source if python >= 3.13

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- torchdata does not provide wheels for python >= 3.13.
- Proceed as usual with python < 3.13, but install from source if python >= 3.13

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[tiny] update type annotations for python3.9 support

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- New syntax (using | ) is only support in python 3.10+
- Switch to using `Union` to unblock 3.9 support

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[Quantization] add bnb quantizer (part 5)

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add more quantizers: gguf and bnb

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Add github workflow with install test matrix

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- Add a workflow to test oumi installation for various environments (Python, cuda versions, torch)

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Add  Dataset Analyzer

# Description
Adds dataset analysis functionality with unit tests for the DatasetAnalyzer class and compute_sample_level_analysis utility function

New Files
* src/oumi/core/analyze/dataset_analyzer.py: High-level orchestrator for dataset analysis
* tests/unit/core/analyze/test_dataset_analyzer.py: tests for DatasetAnalyzer
Enhanced Files
* src/oumi/utils/analysis_utils.py: Added compute_sample_level_analysis function for message-level analysis
* tests/unit/utils/test_analysis_utils.py: Added unit tests

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[Quantization] CLI integration (part 4)

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cli integration

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Adjust settings.json (type checking & auto-format)

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*Changes*
- Avoid setting the type checker to accomodate users using cursor
- Set formatting by default

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[vision] Add vision dpo dataset class

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**Changes**
- Add a base vision DPO dataset class, similar to the language DPO dataset class
  - Note that both DPO datasets (text, and now vision-language) are still experimental, and do not use the `Conversation` format.
- Add a first vision DPO dataset, to consume `jsonl` files.
  - Add an example data file
- Fix a type annotation issue in the DPO text dataset

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Judge API V2 | Documentation & Notebook

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Current changes:
- Updated Judge API documentation from Judge V1 to Judge V2
- Dropped 2 notebooks (these are for Judge V1)
- Added a notebook for Judge V2 (Simple Judge) and updated references
- Removed experimental reg key (`OUMI_EXPERIMENTAL_JUDGE_V2`). Judge V2 can now be used without this

Future changes ToDo:
- Rename Judge V2 to Judge
- Delete legacy judge code and configs

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[Quantization] add awq quantizer (part 3)

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 add awq quantizer

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Fix a pyright error for synthesis

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Add a pyright exemption for an optional synthesis import.
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[bug] Fix a bug where inference engines would wait for one politeness policy cycle after all requests were finished.

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This PR updates our concurrency logic to use a "Polite" semaphore. From a producer-consumer standpoint, the semaphore is producing "start times", which are consumed on subscription. The semaphore will then force the holder to wait if necessary given the politeness policy. When the subscriber releases the semaphore, their release time is appended to the queue.
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[Quantization] adding base class (part 2)

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adding base class

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Towards: OPE-1425

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Update docs with information on adaptive inference

# Description

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Adaptive inference changes have been merged in, this PR updates the docs to help explain to users what it is and how it works. It also provides updates on our "save and resume" functionality in inference.
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Fixes OPE-1411

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docs: add troubleshooting note for Conda Terms of Service error in de…

# Description
This commit adds a troubleshooting note to the development setup documentation to help users resolve the "CondaToSNonInteractiveError: Terms of Service have not been accepted for the following channels..." error.
This issue can block new users from creating or activating the necessary Conda environment for Oumi development.

The note provides copy-pasteable commands for accepting the Terms of Service or removing problematic channels, addressing possible installation blockers that may not be familiar to all contributors. This should help speed up onboarding and reduce confusion during the initial installation process.

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Refine sentence to clarify that Oumi cannot be installed on Intel Macs

# Description
Updating the [#Requirements](https://oumi.ai/docs/en/latest/get_started/installation.html#requirements) section on the Installation page.

The original sentence used the word "there," which could be ambiguous about the location where Oumi cannot be installed. This change replaces "there" with "on those machines" to explicitly specify that installation is not possible on Intel Macs due to PyTorch dropping support for that platform. This improves clarity for readers and reduces potential confusion.

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Judge API V2 | Built-In Judges

# Description

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Introducing 4 generic built-in judges (applicable to any task type, i.e. their inputs are `user request` and `model response`) and 3 built-in judges specifically for Doc Q&A (our signature use case) with inputs: `context`, `question`, and `answer`.

**_Generic Judges_**
- Format Compliance (JSON/XML/HTML/.., keys required, custom constraints)
- Instruction Following
- Topic Adherence
- Truthfulness

**_DocQA Judges_**
- Groundedness
- Relevance
- Completeness

_Notes_
- **_Why did we select these 4 generic judges?_** These should cover the most popular task types identified by [PromptEvals](https://arxiv.org/pdf/2504.14738). Note that there are a few more use cases that are popular, but are either hard to generalize (e.g., stylistic constraints), or should be indirectly covered by these 3 (length constraints and language adherence can be covered by the "Format Compliance" judge).
- **_Why not use generic judges for the 3 DocQA use cases?_** We potentially could, but I strongly believe these would be very inefficient, since we could not explicitly define and refer to the "context". Note that the assesment of these 3 judges can be very different if no context is explicitly considered. In addition, some basic testing I did for generalizing these ("if there is a context in the request, then only consider this for ...") did NOT prove to be very efficient. Since DocQA is one of our signature use cases and these specific 3 dimensions are NOT well-captured by generic judges, I opted for specialized ones (@oelachqar let me know if this is OK).
- **_How well do these judges work? Have we done extensive testing?_** Nope. These are just a starting point with minimum investment and testing from my side (I spend a day on these). If (i) these are deemed high priority and (ii) @brragorn has bandwidth to invest in refining/improving these, we can iterate and incrementally make them more efficient and powerful (without changing the inputs/outputs, which would break users).

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Towards OPE-1327

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[4] Add support for Transform Attributes

# Description

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To be able to output datasets in a specific format (i.e. training, eval, etc.), we need to be able to support "Transformed" Attributes, which are effectively attributes of various types (str, list[str], dict[str, str], and Conversation) which can be "filled in" using the other attributes in the sample.

This PR adds the class necessary to handle these attributes, but does not integrate it into the synthesis flow.
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Fixes OPE-1385

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[0] Add attribute formatter for samples

# Description

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The AttributeFormatter allows us to apply more advanced formatting logic using attribute values from samples in a dataset (think columns).

For the below examples, `attribute_id` is basically `column_name` in a CSV-style dataset.

Samples are formatted with varying logic:
{attribute_id} for the value of a given column
{attribute_id} for the name of a permutable attribute column
{attribute_id.description} for the description of a permutable attribute column
{attribute_id.value} for the specific value of a permutable attribute for that sample
{attribute_id.value.description} for the description of that value

To properly format strings of this type with samples, we need to construct the necessary information into objects (_AttributeInfo and _AttributeValueInfo), which will allow `resolve_placeholders` to do the rest of the work for us.
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Towards OPE-1355

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Followup for comments from #1817

# Description

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SFT:
- Removed unnecessary `HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download Qwen/Qwen3-4B` (for model sizes 8b and below)
- Increased disk size for Qwen3 8b

Evaluation:
- Decrease number of GPU requested from 4 to 1 for model sizes 14b and below
- Tested 14b to ensure it runs

Addresses comments from #1817

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Fixes OPE-1346

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misc. cleanup

# Description

- Remove batch_size from inference configs that don't use it (anything but native)

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Add `slurm_init.sh` script and quickstart Slurm job

# Description

The script is similar to `sky_init.sh`. Example output of slurm init script:
```
------------------------------------------
Slurm job ID: 801
Slurm job name: grpo-verl-gsm8k
Slurm task PID: 1832035
Job start time: Sat Jul 12 01:54:26 UTC 2025
Slurm job nodelist: oumi-compute[002,001]
Current dir: /home/wizeng/oumi_launcher/20250711_215400585499

Head node: oumi-compute002
Master address: 172.26.135.6
Number of nodes: 2
Number of tasks per node: 1
Number of CPUs per node: 8
Number of GPUs per node: 4
CUDA_VISIBLE_DEVICES: 0,1,2,3
------------------------------------------
```

## Related issues

Towards OPE-1373

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# Description

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Added more Qwen3 recipes for training, evaluation, and inference. I have tested all SFT configs for a few training steps to make sure they don't OOM. For inference and eval configs, I tested 3 out of 5 configs (usually 0.6b, 4b, 14b) to see that they run as expected. To test each config, I launched a new cluster and ran training for a few steps eg:

```bash
oumi launch up -c configs/recipes/qwen3/sft/4b_full/gcp_job.yaml --cluster qwen3-4b-full
oumi launch down --cluster qwen3-4b-full
```

Before checking in the PR, I added back `oumi://` prefixes.

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Fixes OPE-1346

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feat: add analyze config classes and exports

# Description
This is the first PR in a series of smaller, reviewable pull requests that break down the larger analyze functionality. The complete changes can be found in the full feature branch: [main...ryan-arman-anlyze_v0](https://github.com/oumi-ai/oumi/compare/main...ryan-arman-anlyze_v0)

This PR introduces the foundational configuration classes needed for the dataset analysis functionality.

Changes:
Added: src/oumi/core/configs/analyze_config.py
- DatasetAnalyzeConfig - Main configuration class for dataset analysis
- SampleAnalyzeConfig - Configuration for individual analyzer plugins

Updated: src/oumi/core/configs/__init__.py
- Added imports for the new config classes
- Added exports to __all__ list for public API

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Towards OPE-1407

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Add verl Countdown Slurm config

# Description

- Add Slurm Countdown for config that mirrors the GCP config
- Swap from deprecated `log_prob_micro_batch_size` param to `log_prob_micro_batch_size_per_gpu`. With the former, there's a bug where if `log_prob_micro_batch_size` is less than the number of GPUs, training will hang until it OOMs.

## Related issues

Fixes OPE-1371

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Add experimental option to use the rich logging handler

# Description

#### Changes
- Our CLI needs a refresh visually. One of the lowest effort improvements is to enable the rich logging handler
- This is currently experimental, and disabled by default
- Users can enable it by setting `export OUMI_ENABLE_RICH_LOGGING="1"`
- When oumi's log level is set to `DEBUG`, we additionally log a lot more information (more detailed stack traces, locals, etc)

#### OUMI_ENABLE_RICH_LOGGING="1"

#### OUMI_ENABLE_RICH_LOGGING="0"

## Related issues

Fixes # (issue)

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[tiny] Update train config comment header

# Description

Since we have many types of training configs now, this PR makes the comment more generic. This PR is just a large find+replace.

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Add support for document ingestion during data synthesis

# Description

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Integrates document ingestion into the dataset planning process, allowing users to specify attributes in their dataset plan which will be filled with documents and/or document segments, which can then be usable during generation.
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Fixes OPE-1297

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Judge API V2 | Enable prompt variable replacement by YAML

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Allow placeholders / variables (format: `{variable}`) in prompts (both `prompt_template` and `system_instruction` fields) to be replaced by the `template_variables` dict that is defined in YAML.
Why we need this? See [here](https://oumi-ai.slack.com/archives/C081RNZUBCZ/p1752149810332809?thread_ts=1752148996.735389&cid=C081RNZUBCZ)
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Towards OPE-1327

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Update mflow support in oumi trainer

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- Update mflow support in the oumi trainer:
  - Log metrics
  - Re-use existing run if it exists. if not, create and manage a new run

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Added utm_source parameters

# Description

Added UTM source parameters to webinar Lu.ma links. This helps us track which are the most successful advertising mediums.


qwen 2.5 config updates

# Description

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Qwen 2.5 and Qwen 2.5 VL config updates: faster, more efficient training, supports long context. Optimized for single node, 8xH100 80GB GPU training.

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Webinar announcement and other news

# Description

Notice for our July 24 webinar at top of README

Updated news items for June-July


Add support for Example Sources in Synthesis

# Description

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Integrates ExampleSources into the dataset planner, allowing the planner to enumerate through them when creating the dataset.
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Fixes OPE-1357

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Fixed issue with final conversations not consistently being saved

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Conversations were not being saved in the core public `infer()` method, resulting in some scenarios where only some of the conversations were being written in the case where inference was stopped and restarted, rather than ALL conversations.
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Towards OPE-1307

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Fixed various tutorial notebooks

notebooks/Oumi - Evaluation with Oumi.ipynb

notebooks/Oumi - Finetuning Tutorial.ipynb

notebooks/Oumi - Using vLLM Engine for Inference.ipynb

# Description

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1. vLLM inference notebook: Adjust the VLLMInferenceEngine config such that it can load on 4 A100-40GB GPUs
2. Evaluation with Oumi notebook: Load models in torch_dtype_str="bfloat16"
3. alpaca_evals.py: Remove import alpaca.evaluate. evaluate is actually a function in alpaca_eval/main.py, not a submodule
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Fixes OPE-1348
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Update our contributing guidelines.

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Minor update to CONTRIBUTING.md
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# Description

Removed collator in finetuning tutorial notebook as temp fix for missing 'labels' ValueError bug. Intended as a temporary fix for [OPE-1342](https://linear.app/oumi/issue/OPE-1342).

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Judge API V2 | Fix judge config from repo path

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Minor Fix: judge config being loaded from repo path
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Towards OPE-1327

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Update launch.md

Fix subversion handling in launch.md by adding required quotes

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