smollm2-135M-8k-lc100k-mix1-ep2
This model is a fine-tuned version of HuggingFaceTB/SmoLLM2-135M on Smol-SmolTalk (the HuggingFaceTB/magpie-ultra-v1.0-top-300K-short-H4, the HuggingFaceTB/OpenHermes-2.5-H4-200k, the HuggingFaceTB/ifeval-like-data-36k-H4, the HuggingFaceTB/everyday-conversations-llama3.1-2k, the HuggingFaceTB/self-oss-instruct-sc2-H4, the HuggingFaceTB/summarization-data-10k-H4, the HuggingFaceTB/smollm-v2-summarization, the HuggingFaceTB/smollm-v2-rewriting-50k-H4, the HuggingFaceTB/explore-instruct-rewrite-H4 and the HuggingFaceTB/LongAlign-16k-ctx-english-H4 datasets). It achieves the following results on the evaluation set:
- Loss: 1.8390
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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2705 | 1.0 | 392 | 1.8649 |
| 1.1867 | 2.0 | 784 | 1.8390 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1