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--- |
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library_name: transformers |
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base_model: Dans-DiscountModels/Meta-Llama-3.1-8B-ChatML |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: l3.1-8b-dans-instruct |
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results: [] |
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license: apache-2.0 |
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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: `0.4.1` |
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```yaml |
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base_model: Dans-DiscountModels/Meta-Llama-3.1-8B-ChatML |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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trust_remote_code: |
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# wandb configuration |
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wandb_project: l3.1-8b-dans-instruct |
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wandb_watch: |
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wandb_run_id: |
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wandb_log_model: |
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# where to save the finished model to |
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output_dir: ./l3.1-8b-dans-instruct |
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# dataset settings (local or huggingface repo) |
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datasets: |
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- path: PocketDoc/Dans-MemoryCore-CoreCurriculum-Small |
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type: sharegpt |
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conversation: chatml |
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- path: AquaV/Energetic-Materials-Sharegpt |
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type: sharegpt |
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conversation: chatml |
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- path: AquaV/Chemical-Biological-Safety-Applications-Sharegpt |
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type: sharegpt |
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conversation: chatml |
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- path: PocketDoc/Dans-Mathmaxx |
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type: sharegpt |
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conversation: chatml |
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- path: PocketDoc/Dans-Benchmaxx |
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type: sharegpt |
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conversation: chatml |
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- path: PocketDoc/Dans-Codemaxx |
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type: sharegpt |
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conversation: chatml |
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- path: PocketDoc/Dans-Taskmaxx |
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type: sharegpt |
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conversation: chatml |
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- path: PocketDoc/Dans-ASCIIMaxx-Wordart |
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type: sharegpt |
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conversation: chatml |
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- path: PocketDoc/Dans-Prosemaxx |
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type: sharegpt |
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conversation: chatml |
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- path: PocketDoc/Dans-Toolmaxx |
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type: sharegpt |
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conversation: chatml |
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chat_template: chatml |
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plugins: |
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- axolotl.integrations.liger.LigerPlugin |
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liger_rope: true |
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liger_rms_norm: true |
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liger_swiglu: true |
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liger_fused_linear_cross_entropy: true |
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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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dataset_prepared_path: ./l3.1-8b-dans-instruct-data |
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val_set_size: 0.03 |
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lora_model_dir: |
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sequence_len: 8192 |
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# use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true' |
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sample_packing: true |
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eval_sample_packing: true |
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# you can set these packing optimizations AFTER starting a training at least once. |
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# The trainer will provide recommended values for these values. |
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pad_to_sequence_len: true |
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#rope_scaling: |
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#type: # linear | dynamic |
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#factor: # float (2 for 2x) |
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adapter: # blank for full finetune |
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lora_r: 64 |
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lora_alpha: 64 |
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lora_dropout: 0.2 |
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lora_target_linear: True |
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lora_target_modules: |
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- q_proj |
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- v_proj |
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- k_proj |
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- o_proj |
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- gate_proj |
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- down_proj |
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- up_proj |
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lora_modules_to_save: |
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- embed_tokens |
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- lm_head |
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lora_fan_in_fan_out: |
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gradient_accumulation_steps: 32 |
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micro_batch_size: 1 |
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num_epochs: 3 |
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optimizer: adamw_torch |
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lr_scheduler: cosine |
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learning_rate: 0.0000015 |
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cosine_min_lr_ratio: |
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train_on_inputs: false |
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group_by_length: true |
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bf16: true |
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fp16: false |
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tf32: false |
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gradient_checkpointing: unsloth |
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early_stopping_patience: |
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resume_from_checkpoint: |
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auto_resume_from_checkpoints: false |
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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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warmup_steps: 15 |
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eval_steps: 25 |
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# save_steps: 100 |
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saves_per_epoch: 3 |
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debug: false |
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deepspeed: |
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weight_decay: 0.1 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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pad_token: <|finetune_right_pad_id|> |
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eos_token: <|im_end|> |
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``` |
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</details><br> |
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# l3.1-8b-dans-instruct |
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This model is a fine-tuned version of [Dans-DiscountModels/Meta-Llama-3.1-8B-ChatML](https://huggingface.co/Dans-DiscountModels/Meta-Llama-3.1-8B-ChatML) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7432 |
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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: 1.5e-06 |
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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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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 32 |
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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: 15 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.0783 | 0.0077 | 1 | 1.0298 | |
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| 0.8528 | 0.1931 | 25 | 0.8603 | |
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| 0.7776 | 0.3862 | 50 | 0.7925 | |
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| 0.7089 | 0.5793 | 75 | 0.7697 | |
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| 0.6868 | 0.7724 | 100 | 0.7584 | |
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| 0.7158 | 0.9655 | 125 | 0.7524 | |
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| 0.6938 | 1.1566 | 150 | 0.7488 | |
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| 0.733 | 1.3499 | 175 | 0.7464 | |
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| 0.7956 | 1.5433 | 200 | 0.7450 | |
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| 0.6886 | 1.7366 | 225 | 0.7442 | |
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| 0.9065 | 1.9299 | 250 | 0.7437 | |
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| 0.7851 | 2.1210 | 275 | 0.7434 | |
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| 0.7256 | 2.3142 | 300 | 0.7433 | |
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| 0.7832 | 2.5074 | 325 | 0.7432 | |
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| 0.7317 | 2.7006 | 350 | 0.7432 | |
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| 0.7112 | 2.8937 | 375 | 0.7432 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
