| | --- |
| | language: |
| | - en |
| | license: apache-2.0 |
| | library_name: transformers |
| | pipeline_tag: text-generation |
| | tags: |
| | - nlp |
| | - agent |
| | --- |
| | |
| | # ALFWorld-MPO |
| |
|
| | This model is a fine-tuned version of Llama-3.1-8B-Instruct on the [alfworld-metaplan-preference-pairs](https://huggingface.co/datasets/xwm/Meta_Plan_Optimization/blob/main/alfworld_metaplan_preference_pairs.json) dataset as described in [MPO: Boosting LLM Agents with Meta Plan Optimization](https://hf.co/papers/2503.02682). |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.8390 |
| | - Rewards/chosen: -0.5836 |
| | - Rewards/rejected: -1.2646 |
| | - Rewards/accuracies: 0.6318 |
| | - Rewards/margins: 0.6810 |
| | - Logps/chosen: -12.9009 |
| | - Logps/rejected: -19.8890 |
| | - Logits/chosen: -0.3349 |
| | - Logits/rejected: -0.3405 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 1e-05 |
| | - train_batch_size: 2 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - distributed_type: multi-GPU |
| | - num_devices: 4 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 32 |
| | - total_eval_batch_size: 4 |
| | - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_ratio: 0.03 |
| | - num_epochs: 3.0 |
| | |
| | ### Training results |
| | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.46.1 |
| | - Pytorch 2.5.1+cu124 |
| | - Datasets 3.1.0 |
| | - Tokenizers 0.20.3 |
| | |
| | ## Code |
| | |
| | [https://github.com/WeiminXiong/MPO](https://github.com/WeiminXiong/MPO) |
