LLM for ARC
Collection
4 items
•
Updated
engineer1-heavy-barc-llama3.1-8b-ins-fft-transduction_lr1e-5_epoch3
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/transduction_heavy_100k_jsonl, the barc0/transduction_heavy_suggestfunction_100k_jsonl, the barc0/transduction_rearc_dataset_400k, the barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems and the barc0/transduction_angmented_100k_gpt4o-mini_generated_problems datasets. It achieves the following results on the evaluation set:
- Loss: 0.0219
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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.0378 | 1.0 | 3729 | 0.0330 |
| 0.0234 | 2.0 | 7458 | 0.0227 |
| 0.0116 | 3.0 | 11187 | 0.0219 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
- Downloads last month
- 7,519
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for barc0/Llama-3.1-ARC-Potpourri-Transduction-8B
Base model
meta-llama/Llama-3.1-8B
Finetuned
meta-llama/Llama-3.1-8B-Instruct