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Magpie-Align
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Llama-3-8B-Self-Instruct-100K

PyTorch
Safetensors
llama
axolotl
Generated from Trainer
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  1. README.md +145 -0
  2. pytorch_model-00001-of-00004.bin +2 -2
  3. pytorch_model-00002-of-00004.bin +3 -0
  4. pytorch_model-00003-of-00004.bin +3 -0
  5. pytorch_model-00004-of-00004.bin +3 -0
  6. pytorch_model.bin.index.json +298 -0
README.md ADDED
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+ ---
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+ license: llama3
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+ base_model: meta-llama/Meta-Llama-3-8B
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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: Llama-3-8B-Self-Instruct-100K
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+ results: []
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+ ---
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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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+
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+ [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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+ <details><summary>See axolotl config</summary>
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+
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+ axolotl version: `0.4.1`
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+ ```yaml
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+ base_model: meta-llama/Meta-Llama-3-8B
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+ model_type: LlamaForCausalLM
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+ tokenizer_type: AutoTokenizer
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+ chat_template: llama3
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+
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+ load_in_8bit: false
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+ load_in_4bit: false
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+ strict: false
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+
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+ datasets:
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+ - path: Magpie-Align/Llama3-8B-Instruct-Self-Instruct
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+ type: sharegpt
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+ conversation: llama3
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+ dataset_prepared_path: last_run_prepared
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+ val_set_size: 0.001
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+ output_dir: axolotl_out/Llama-3-8B-self-instruct-100K
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+
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+ sequence_len: 8192
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+ sample_packing: true
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+ eval_sample_packing: false
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+ pad_to_sequence_len: true
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+
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+ wandb_project: SynDa
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+ wandb_entity:
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+ wandb_watch:
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+ wandb_name: Llama-3-8B-Self-Instruct
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+ wandb_log_model:
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+ hub_model_id: Magpie-Align/Llama-3-8B-Self-Instruct-100K
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+
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+ gradient_accumulation_steps: 8
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+ micro_batch_size: 1
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+ num_epochs: 2
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+ optimizer: paged_adamw_8bit
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+ lr_scheduler: cosine
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+ learning_rate: 2e-5
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+
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+ train_on_inputs: false
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+ group_by_length: false
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+ bf16: auto
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+ fp16:
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+ tf32: false
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+
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+ gradient_checkpointing: true
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+ gradient_checkpointing_kwargs:
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+ use_reentrant: false
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+ early_stopping_patience:
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+ resume_from_checkpoint:
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+ logging_steps: 1
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+ xformers_attention:
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+ flash_attention: true
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+
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+ warmup_ratio: 0.1
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+ evals_per_epoch: 5
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+ eval_table_size:
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+ saves_per_epoch: 1
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+ debug:
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+ deepspeed:
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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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+ ```
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+
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+ </details><br>
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+
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+ # Llama-3-8B-Self-Instruct-100K
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+
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+ This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6245
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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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: 4
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 32
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+ - total_eval_batch_size: 4
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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: 2
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:----:|:---------------:|
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+ | 1.3442 | 0.0190 | 1 | 2.3110 |
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+ | 0.9581 | 0.2095 | 11 | 1.1476 |
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+ | 0.8258 | 0.4190 | 22 | 0.9256 |
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+ | 0.717 | 0.6286 | 33 | 0.7341 |
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+ | 0.6746 | 0.8381 | 44 | 0.6497 |
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+ | 0.5601 | 1.0333 | 55 | 0.6268 |
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+ | 0.5571 | 1.2429 | 66 | 0.6285 |
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+ | 0.538 | 1.4524 | 77 | 0.6258 |
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+ | 0.548 | 1.6619 | 88 | 0.6251 |
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+ | 0.5467 | 1.8714 | 99 | 0.6245 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.43.3
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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