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) |