End of training
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README.md
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---
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base_model: meta-llama/Meta-Llama-3-70B
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library_name: peft
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license: llama3
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tags:
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- axolotl
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- generated_from_trainer
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model-index:
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- name: Allama370b
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<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)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.1`
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```yaml
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base_model: meta-llama/Meta-Llama-3-70B
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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load_in_8bit: false
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load_in_4bit: true
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strict: false
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datasets:
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- path: afrias5/FinUpTagsNoTestNoExNew
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type: alpaca
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field: text
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dataset_prepared_path: Allama3dataset
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val_set_size: 0
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output_dir: models/Allama370b #change
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# lora_model_dir: models/llama370b
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# auto_resume_from_checkpoints: true
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sequence_len: 4096
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sample_packing: true
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pad_to_sequence_len: true
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eval_sample_packing: False
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adapter: lora
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lora_r: 4
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_linear: true
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lora_fan_in_fan_out:
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lora_modules_to_save:
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- embed_tokens
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- lm_head
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wandb_project: 'llama3run'
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wandb_entity:
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wandb_watch:
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wandb_run_id:
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wandb_name: 'A70b' #change
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 1
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num_epochs: 4
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optimizer: adamw_torch
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lr_scheduler: cosine
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learning_rate: 0.0002
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train_on_inputs: false
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group_by_length: false
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bf16: true
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fp16:
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tf32: false
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hub_model_id: afrias5/Allama370b
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gradient_checkpointing: true
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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s2_attention:
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warmup_steps: 10
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# eval_steps: 300
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saves_per_epoch: 1
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save_total_limit: 12
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debug:
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deepspeed: deepspeed_configs/zero3_bf16.json
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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special_tokens:
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pad_token: <|end_of_text|>
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```
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</details><br>
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/afrias5/llama3run/runs/0xrylxx1)
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# Allama370b
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) on the None dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 2
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 8
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- total_eval_batch_size: 2
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 4
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### Training results
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### Framework versions
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- PEFT 0.11.1
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- Transformers 4.42.4
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- Pytorch 2.2.2+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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