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Model: Weyaxi/Newton-7B Source: Original Platform
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adapter/README.md
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181
adapter/README.md
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---
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license: apache-2.0
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library_name: peft
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tags:
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- axolotl
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- generated_from_trainer
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base_model: openchat/openchat-3.5-0106
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model-index:
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- name: newton-lora
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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/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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base_model: openchat/openchat-3.5-0106
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model_type: MistralForCausalLM
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tokenizer_type: LlamaTokenizer
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is_mistral_derived_model: true
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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: merged_all.json
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type:
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field_instruction: instruction
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field_output: output
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format: "GPT4 Correct User: {instruction}<|end_of_turn|>GPT4 Correct Assistant:"
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no_input_format: "GPT4 Correct User: {instruction}<|end_of_turn|>GPT4 Correct Assistant:"
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.01 # not sure
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output_dir: ./newton
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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: true
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pad_to_sequence_len: true
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lora_r: 128
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lora_alpha: 64
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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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lora_modules_to_save:
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- embed_tokens
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- lm_head
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wandb_project: huggingface
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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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hub_model_id: Weyaxi/newton-lora
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save_safetensors: true
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# change #
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gradient_accumulation_steps: 12
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micro_batch_size: 6
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num_epochs: 2
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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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# change #
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train_on_inputs: false
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group_by_length: false
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bf16: true
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fp16: false
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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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warmup_steps: 10 # not sure
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saves_per_epoch: 2
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evals_per_epoch: 4
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eval_table_size:
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eval_table_max_new_tokens: 128
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debug:
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deepspeed:
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weight_decay: 0.1 # not sure
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fsdp:
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fsdp_config:
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special_tokens:
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bos_token: "<s>"
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eos_token: "</s>"
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unk_token: "<unk>"
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tokens:
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- "<|end_of_turn|>"
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- "<|pad_0|>"
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```
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</details><br>
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# newton-lora
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This model is a fine-tuned version of [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0800
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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: 6
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- eval_batch_size: 6
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- seed: 42
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- gradient_accumulation_steps: 12
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- total_train_batch_size: 72
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.6925 | 0.02 | 1 | 1.3667 |
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| 0.5622 | 0.25 | 16 | 0.3390 |
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| 0.5269 | 0.5 | 32 | 0.1395 |
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| 0.5343 | 0.75 | 48 | 0.1048 |
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| 0.515 | 1.01 | 64 | 0.0904 |
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| 0.3971 | 1.24 | 80 | 0.0854 |
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| 0.3889 | 1.49 | 96 | 0.0820 |
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| 0.3864 | 1.74 | 112 | 0.0800 |
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### Framework versions
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- PEFT 0.7.2.dev0
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- Transformers 4.37.0
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- Pytorch 2.1.2+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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