AMD-HybridLM-Models ✨
Collection
AMD-HybridLM is a family of post-trained, highly efficient hybrid models, designed to combine performance with speed and memory efficiency. • 21 items • Updated • 5
zebra_4MLA12M2_8bt_SFT_8k_combined
This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the JunxiongWang/sftdatasetv3, the amd/OpenMathInstruct-2_ZebraLlama_2M, the amd/Zebra_Llama_OpenThoughts-114k-math, the amd/OpenR1-Math-220K and the amd/Zebra_Llama_ChatQA2-Long-SFT_long_sft_QA datasets. It achieves the following results on the evaluation set:
- Loss: 1436.5125
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: 6e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1522.4293 | 1.0 | 38873 | 1436.5125 |
Framework versions
- Transformers 4.43.1
- Pytorch 2.7.0a0+git6374332
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for amd/zebra_4MLA12M2_8bt_SFT_8k_combined
Base model
meta-llama/Llama-3.2-1B-Instruct