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--- |
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license: other |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: intervitens/internlm2-limarp-chat-20b |
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model-index: |
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- name: outputs/qlora-out |
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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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Compute power from g4rg. Big Thanks. |
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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: `0.4.0` |
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```yaml |
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mlflow_tracking_uri: http://127.0.0.1:2340 |
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mlflow_experiment_name: Default |
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base_model: intervitens/internlm2-limarp-chat-20b |
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model_type: AutoModelForCausalLM |
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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: ResplendentAI/Alpaca_NSFW_Shuffled |
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type: alpaca |
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- path: diffnamehard/toxic-dpo-v0.1-NoWarning-alpaca |
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type: alpaca |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.1 |
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output_dir: ./outputs/qlora-out |
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adapter: qlora |
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lora_model_dir: |
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sequence_len: 8192 |
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sample_packing: false |
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pad_to_sequence_len: true |
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lora_r: 32 |
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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_target_modules: |
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- gate_proj |
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- down_proj |
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- up_proj |
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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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wandb_project: |
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wandb_entity: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 2 |
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num_epochs: 4 |
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optimizer: adamw_bnb_8bit |
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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: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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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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loss_watchdog_threshold: 5.0 |
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loss_watchdog_patience: 3 |
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warmup_steps: 10 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_max_new_tokens: 128 |
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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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``` |
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</details><br> |
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# outputs/qlora-out |
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This model is a fine-tuned version of [intervitens/internlm2-limarp-chat-20b](https://huggingface.co/intervitens/internlm2-limarp-chat-20b) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9896 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 7 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 56 |
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- total_eval_batch_size: 14 |
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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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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.4668 | 0.0476 | 1 | 1.4615 | |
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| 1.3541 | 0.2857 | 6 | 1.4253 | |
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| 1.2057 | 0.5714 | 12 | 1.2120 | |
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| 1.0818 | 0.8571 | 18 | 1.1259 | |
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| 1.0835 | 1.1429 | 24 | 1.0750 | |
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| 1.0503 | 1.4286 | 30 | 1.0451 | |
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| 1.0031 | 1.7143 | 36 | 1.0288 | |
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| 0.9728 | 2.0 | 42 | 1.0137 | |
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| 0.8879 | 2.2857 | 48 | 1.0082 | |
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| 0.8981 | 2.5714 | 54 | 0.9956 | |
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| 0.8613 | 2.8571 | 60 | 0.9926 | |
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| 0.8608 | 3.1429 | 66 | 0.9903 | |
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| 0.7841 | 3.4286 | 72 | 0.9903 | |
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| 0.9237 | 3.7143 | 78 | 0.9899 | |
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| 0.868 | 4.0 | 84 | 0.9896 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
