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What does this PR do? This PR adds support for Direct Preference Optimization (DPO) training via the existing HuggingFace inline provider. It introduces a new DPO training recipe, config schema updates, dataset integration, and end-to-end testing to support preference-based fine-tuning with TRL. Test Plan Added integration test: tests/integration/post_training/test_post_training.py::TestPostTraining::test_preference_optimize Ran tests on both CPU and CUDA environments --------- Co-authored-by: Ubuntu <ubuntu@ip-172-31-43-83.ec2.internal> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
1.5 KiB
1.5 KiB
inline::huggingface
Description
HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.
Configuration
| Field | Type | Required | Default | Description |
|---|---|---|---|---|
device |
<class 'str'> |
No | cuda | |
distributed_backend |
Literal['fsdp', 'deepspeed' |
No | ||
checkpoint_format |
Literal['full_state', 'huggingface' |
No | huggingface | |
chat_template |
<class 'str'> |
No | < | user |
| {input} | ||||
| < | assistant | > | ||
| {output} | ||||
model_specific_config |
<class 'dict'> |
No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} | |
max_seq_length |
<class 'int'> |
No | 2048 | |
gradient_checkpointing |
<class 'bool'> |
No | False | |
save_total_limit |
<class 'int'> |
No | 3 | |
logging_steps |
<class 'int'> |
No | 10 | |
warmup_ratio |
<class 'float'> |
No | 0.1 | |
weight_decay |
<class 'float'> |
No | 0.01 | |
dataloader_num_workers |
<class 'int'> |
No | 4 | |
dataloader_pin_memory |
<class 'bool'> |
No | True | |
dpo_beta |
<class 'float'> |
No | 0.1 | |
use_reference_model |
<class 'bool'> |
No | True | |
dpo_loss_type |
Literal['sigmoid', 'hinge', 'ipo', 'kto_pair' |
No | sigmoid | |
dpo_output_dir |
<class 'str'> |
No | ./checkpoints/dpo |
Sample Configuration
checkpoint_format: huggingface
distributed_backend: null
device: cpu