Create README.md

README.md

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+---
+license: other
+base_model: Meta-Llama-3.1-8B-Instruct
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: all_abla_numina_oly_orca
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# all_abla_numina_oly_orca
+
+This model is a fine-tuned version of [/home/test/testdata/models/Meta-Llama-3.1-8B-Instruct](https://huggingface.co//home/test/testdata/models/Meta-Llama-3.1-8B-Instruct) on the codefeedback-o1, the magicoder-o1, the magicoder-oss-o1, the mathinstruct-MATH-o1, the mathinstruct-augmented-o1, the numina-cn-k12-o1, the numina-not-cn-k12-o1, the reasoning-001-o1 and the ultramedical_mc_o1 datasets.
+It achieves the following results on the evaluation set:
+- Loss: 0.2059
+- Accuracy: 0.9286
+
+## 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: 4
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 32
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 256
+- 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: 3.0
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 0.2107        | 0.5574 | 500  | 0.2142          | 0.9208   |
+| 0.1561        | 1.1148 | 1000 | 0.2085          | 0.9239   |
+| 0.1547        | 1.6722 | 1500 | 0.1994          | 0.9265   |
+| 0.1092        | 2.2297 | 2000 | 0.2073          | 0.9278   |
+| 0.1073        | 2.7871 | 2500 | 0.2064          | 0.9284   |
+
+
+### Framework versions
+
+- Transformers 4.43.4
+- Pytorch 2.4.0
+- Datasets 2.20.0
+- Tokenizers 0.19.1