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## Overview
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Protocol for batch processing API operations.
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The Batches API enables efficient processing of multiple requests in a single operation,
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particularly useful for processing large datasets, batch evaluation workflows, and
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cost-effective inference at scale.
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particularly useful for processing large datasets, batch evaluation workflows, and
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cost-effective inference at scale.
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Note: This API is currently under active development and may undergo changes.
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The API is designed to allow use of openai client libraries for seamless integration.
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This API provides the following extensions:
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- idempotent batch creation
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Note: This API is currently under active development and may undergo changes.
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This section contains documentation for all available providers for the **batches** API.
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@ -10,4 +10,5 @@ This section contains documentation for all available providers for the **files*
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:maxdepth: 1
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inline_localfs
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remote_s3
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```
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docs/source/providers/files/remote_s3.md
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33
docs/source/providers/files/remote_s3.md
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# remote::s3
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## Description
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AWS S3-based file storage provider for scalable cloud file management with metadata persistence.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `bucket_name` | `<class 'str'>` | No | | S3 bucket name to store files |
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| `region` | `<class 'str'>` | No | us-east-1 | AWS region where the bucket is located |
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| `aws_access_key_id` | `str \| None` | No | | AWS access key ID (optional if using IAM roles) |
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| `aws_secret_access_key` | `str \| None` | No | | AWS secret access key (optional if using IAM roles) |
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| `endpoint_url` | `str \| None` | No | | Custom S3 endpoint URL (for MinIO, LocalStack, etc.) |
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| `auto_create_bucket` | `<class 'bool'>` | No | False | Automatically create the S3 bucket if it doesn't exist |
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| `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | SQL store configuration for file metadata |
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## Sample Configuration
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```yaml
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bucket_name: ${env.S3_BUCKET_NAME}
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region: ${env.AWS_REGION:=us-east-1}
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aws_access_key_id: ${env.AWS_ACCESS_KEY_ID:=}
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aws_secret_access_key: ${env.AWS_SECRET_ACCESS_KEY:=}
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endpoint_url: ${env.S3_ENDPOINT_URL:=}
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auto_create_bucket: ${env.S3_AUTO_CREATE_BUCKET:=false}
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metadata_store:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/s3_files_metadata.db
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```
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```{toctree}
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:maxdepth: 1
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inline_huggingface
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inline_torchtune
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inline_huggingface-cpu
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inline_huggingface-gpu
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inline_torchtune-cpu
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inline_torchtune-gpu
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remote_nvidia
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```
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# inline::huggingface-cpu
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## Description
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HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `device` | `<class 'str'>` | No | cuda | |
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| `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No | | |
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| `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface | |
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| `chat_template` | `<class 'str'>` | No | <|user|>
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{input}
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<|assistant|>
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{output} | |
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| `model_specific_config` | `<class 'dict'>` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} | |
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| `max_seq_length` | `<class 'int'>` | No | 2048 | |
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| `gradient_checkpointing` | `<class 'bool'>` | No | False | |
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| `save_total_limit` | `<class 'int'>` | No | 3 | |
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| `logging_steps` | `<class 'int'>` | No | 10 | |
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| `warmup_ratio` | `<class 'float'>` | No | 0.1 | |
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| `weight_decay` | `<class 'float'>` | No | 0.01 | |
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| `dataloader_num_workers` | `<class 'int'>` | No | 4 | |
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| `dataloader_pin_memory` | `<class 'bool'>` | No | True | |
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| `dpo_beta` | `<class 'float'>` | No | 0.1 | |
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| `use_reference_model` | `<class 'bool'>` | No | True | |
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| `dpo_loss_type` | `Literal['sigmoid', 'hinge', 'ipo', 'kto_pair'` | No | sigmoid | |
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| `dpo_output_dir` | `<class 'str'>` | No | | |
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## Sample Configuration
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```yaml
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checkpoint_format: huggingface
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distributed_backend: null
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device: cpu
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dpo_output_dir: ~/.llama/dummy/dpo_output
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```
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# inline::huggingface-gpu
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## Description
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HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `device` | `<class 'str'>` | No | cuda | |
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| `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No | | |
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| `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface | |
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| `chat_template` | `<class 'str'>` | No | <|user|>
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{input}
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<|assistant|>
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{output} | |
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| `model_specific_config` | `<class 'dict'>` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} | |
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| `max_seq_length` | `<class 'int'>` | No | 2048 | |
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| `gradient_checkpointing` | `<class 'bool'>` | No | False | |
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| `save_total_limit` | `<class 'int'>` | No | 3 | |
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| `logging_steps` | `<class 'int'>` | No | 10 | |
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| `warmup_ratio` | `<class 'float'>` | No | 0.1 | |
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| `weight_decay` | `<class 'float'>` | No | 0.01 | |
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| `dataloader_num_workers` | `<class 'int'>` | No | 4 | |
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| `dataloader_pin_memory` | `<class 'bool'>` | No | True | |
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| `dpo_beta` | `<class 'float'>` | No | 0.1 | |
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| `use_reference_model` | `<class 'bool'>` | No | True | |
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| `dpo_loss_type` | `Literal['sigmoid', 'hinge', 'ipo', 'kto_pair'` | No | sigmoid | |
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| `dpo_output_dir` | `<class 'str'>` | No | | |
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## Sample Configuration
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```yaml
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checkpoint_format: huggingface
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distributed_backend: null
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device: cpu
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dpo_output_dir: ~/.llama/dummy/dpo_output
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```
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docs/source/providers/post_training/inline_torchtune-cpu.md
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20
docs/source/providers/post_training/inline_torchtune-cpu.md
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# inline::torchtune-cpu
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## Description
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TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `torch_seed` | `int \| None` | No | | |
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| `checkpoint_format` | `Literal['meta', 'huggingface'` | No | meta | |
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## Sample Configuration
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```yaml
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checkpoint_format: meta
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```
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docs/source/providers/post_training/inline_torchtune-gpu.md
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20
docs/source/providers/post_training/inline_torchtune-gpu.md
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# inline::torchtune-gpu
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## Description
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TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `torch_seed` | `int \| None` | No | | |
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| `checkpoint_format` | `Literal['meta', 'huggingface'` | No | meta | |
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## Sample Configuration
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```yaml
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checkpoint_format: meta
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```
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