End of training
Browse files- README.md +139 -0
- generation_config.json +8 -0
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: JackFram/llama-68m
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tags:
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- axolotl
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- generated_from_trainer
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datasets:
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- argilla/databricks-dolly-15k-curated-en
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model-index:
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- name: llama-68m
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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/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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axolotl version: `0.6.0`
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```yaml
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base_model: JackFram/llama-68m
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batch_size: 64
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bf16: true
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chat_template: tokenizer_default_fallback_alpaca
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datasets:
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- format: custom
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path: argilla/databricks-dolly-15k-curated-en
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type:
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field_input: original-instruction
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field_instruction: original-instruction
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field_output: original-response
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format: '{instruction} {input}'
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no_input_format: '{instruction}'
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system_format: '{system}'
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system_prompt: ''
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device_map: auto
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eval_sample_packing: false
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eval_steps: 50
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flash_attention: true
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gradient_checkpointing: true
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group_by_length: true
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hub_model_id: SystemAdmin123/llama-68m
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hub_strategy: checkpoint
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learning_rate: 0.0002
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logging_steps: 10
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lr_scheduler: cosine
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max_steps: 5000
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micro_batch_size: 32
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model_type: AutoModelForCausalLM
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num_epochs: 100
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optimizer: adamw_bnb_8bit
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output_dir: /root/.sn56/axolotl/tmp/llama-68m
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pad_to_sequence_len: true
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resize_token_embeddings_to_32x: false
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sample_packing: true
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save_steps: 50
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save_total_limit: 2
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sequence_len: 2048
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special_tokens:
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pad_token: </s>
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tokenizer_type: LlamaTokenizerFast
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torch_dtype: bf16
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trust_remote_code: true
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val_set_size: 0.1
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wandb_entity: ''
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wandb_mode: online
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wandb_name: JackFram/llama-68m-argilla/databricks-dolly-15k-curated-en
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wandb_project: Gradients-On-Demand
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wandb_run: your_name
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wandb_runid: default
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warmup_ratio: 0.05
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```
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</details><br>
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# llama-68m
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This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) on the argilla/databricks-dolly-15k-curated-en dataset.
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It achieves the following results on the evaluation set:
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- Loss: 4.0103
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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: 32
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- eval_batch_size: 32
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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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- total_train_batch_size: 64
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- total_eval_batch_size: 64
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- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 30
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- training_steps: 600
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-------:|:----:|:---------------:|
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| No log | 0.0769 | 1 | 3.9168 |
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| 2.5978 | 3.8462 | 50 | 2.8149 |
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| 2.0808 | 7.6923 | 100 | 2.9664 |
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| 1.6294 | 11.5385 | 150 | 3.2337 |
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| 1.2699 | 15.3846 | 200 | 3.5217 |
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| 1.0092 | 19.2308 | 250 | 3.7262 |
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| 0.8392 | 23.0769 | 300 | 3.8683 |
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| 0.7428 | 26.9231 | 350 | 3.9435 |
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| 0.6952 | 30.7692 | 400 | 3.9860 |
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| 0.6762 | 34.6154 | 450 | 3.9990 |
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| 0.6739 | 38.4615 | 500 | 4.0167 |
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| 0.6691 | 42.3077 | 550 | 4.0208 |
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| 0.6667 | 46.1538 | 600 | 4.0103 |
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### Framework versions
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- Transformers 4.48.1
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- Pytorch 2.5.1+cu124
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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generation_config.json
ADDED
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"do_sample": true,
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"eos_token_id": 2,
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"pad_token_id": 1,
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"transformers_version": "4.48.1"
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}
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