End of training

README.md

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+---
+license: llama3
+library_name: peft
+tags:
+- axolotl
+- generated_from_trainer
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+model-index:
+- name: math-llama-3-8b-instruct
+  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. -->
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.0`
+```yaml
+adapter: qlora
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+base_model_config: meta-llama/Meta-Llama-3-8B-Instruct
+datasets:
+- path: vicgalle/alpaca-gpt4
+  type: alpaca
+flash_attention: true
+gradient_accumulation_steps: 4
+gradient_checkpointing: true
+hf_use_auth_token: true
+hub_model_id: ibivibiv/math-llama-3-8b-instruct
+learning_rate: 0.0002
+load_in_4bit: true
+logging_steps: 1
+lora_alpha: 16
+lora_dropout: 0.05
+lora_r: 32
+lora_target_linear: true
+lr_scheduler: cosine
+micro_batch_size: 2
+model_type: AutoModelForCausalLM
+num_epochs: 3
+optimizer: paged_adamw_32bit
+output_dir: /job/out
+sample_packing: true
+save_safetensors: true
+sequence_len: 4096
+special_tokens:
+  pad_token: <|end_of_text|>
+tokenizer_type: AutoTokenizer
+wandb_project: TuneStudio
+wandb_run_id: mathllama
+wandb_watch: 'true'
+warmup_steps: 10
+
+```
+
+</details><br>
+
+# math-llama-3-8b-instruct
+
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset.
+
+## 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: 0.0002
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- total_eval_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 3
+
+### Training results
+
+
+
+### Framework versions
+
+- PEFT 0.10.0
+- Transformers 4.40.2
+- Pytorch 2.1.2+cu118
+- Datasets 2.19.1
+- Tokenizers 0.19.1