| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: JackFram/llama-68m |
| | tags: |
| | - axolotl |
| | - generated_from_trainer |
| | datasets: |
| | - argilla/databricks-dolly-15k-curated-en |
| | model-index: |
| | - name: llama-68m |
| | 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/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) |
| | <details><summary>See axolotl config</summary> |
| |
|
| | axolotl version: `0.6.0` |
| | ```yaml |
| | base_model: JackFram/llama-68m |
| | batch_size: 128 |
| | bf16: true |
| | chat_template: tokenizer_default_fallback_alpaca |
| | datasets: |
| | - format: custom |
| | path: argilla/databricks-dolly-15k-curated-en |
| | type: |
| | field_input: original-instruction |
| | field_instruction: original-instruction |
| | field_output: original-response |
| | format: '{instruction} {input}' |
| | no_input_format: '{instruction}' |
| | system_format: '{system}' |
| | system_prompt: '' |
| | device_map: auto |
| | eval_sample_packing: false |
| | eval_steps: 200 |
| | flash_attention: true |
| | gradient_checkpointing: true |
| | group_by_length: true |
| | hub_model_id: SystemAdmin123/llama-68m |
| | hub_strategy: checkpoint |
| | learning_rate: 0.0002 |
| | logging_steps: 10 |
| | lr_scheduler: cosine |
| | max_steps: 10000 |
| | micro_batch_size: 32 |
| | model_type: AutoModelForCausalLM |
| | num_epochs: 100 |
| | optimizer: adamw_bnb_8bit |
| | output_dir: /root/.sn56/axolotl/tmp/llama-68m |
| | pad_to_sequence_len: true |
| | resize_token_embeddings_to_32x: false |
| | sample_packing: true |
| | save_steps: 200 |
| | save_total_limit: 1 |
| | sequence_len: 2048 |
| | special_tokens: |
| | pad_token: </s> |
| | tokenizer_type: LlamaTokenizerFast |
| | torch_dtype: bf16 |
| | training_args_kwargs: |
| | hub_private_repo: true |
| | trust_remote_code: true |
| | val_set_size: 0.1 |
| | wandb_entity: '' |
| | wandb_mode: online |
| | wandb_name: JackFram/llama-68m-argilla/databricks-dolly-15k-curated-en |
| | wandb_project: Gradients-On-Demand |
| | wandb_run: your_name |
| | wandb_runid: default |
| | warmup_ratio: 0.05 |
| | |
| | ``` |
| |
|
| | </details><br> |
| |
|
| | # llama-68m |
| |
|
| | 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. |
| |
|
| | ## 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: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - distributed_type: multi-GPU |
| | - num_devices: 4 |
| | - total_train_batch_size: 128 |
| | - total_eval_batch_size: 128 |
| | - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_steps: 5 |
| | - training_steps: 100 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:------:|:----:|:---------------:| |
| | | No log | 0.1667 | 1 | 3.9323 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.48.1 |
| | - Pytorch 2.5.1+cu124 |
| | - Datasets 3.2.0 |
| | - Tokenizers 0.21.0 |
| | |
