162 lines
4.3 KiB
Markdown
162 lines
4.3 KiB
Markdown
---
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license: apache-2.0
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base_model: mistralai/Mistral-7B-Instruct-v0.3
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tags:
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- generated_from_trainer
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model-index:
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- name: mistral_fine_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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[<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: mistralai/Mistral-7B-Instruct-v0.3
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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: false
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strict: false
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datasets:
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- data_files: out/train.jsonl
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path: out/
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ds_type: json
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type:
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alpaca
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dataset_prepared_path:
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val_set_size: 0.05
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output_dir: ./mistral_fine_out
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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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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_run_id:
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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: 2
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.000005
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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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auto_resume_from_checkpoint: true
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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
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eval_steps: 0.05
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eval_table_size:
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eval_table_max_new_tokens: 128
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save_steps:
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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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bos_token: "<s>"
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eos_token: "</s>"
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unk_token: "<unk>"
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model_config:
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sliding_window: 4096
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```
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</details><br>
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# mistral_fine_out
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on a synthetic appeals dataset.
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See the [health insurance fine tuning repo](https://github.com/totallylegitco/healthinsurance-llm) for details.
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An earlier [version of this dataset is availabile](https://huggingface.co/datasets/TotallyLegitCo/synthetic-appeals).
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It achieves the following results on the evaluation set:
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- Loss: 0.7984
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## Model description
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Generate health insurance appeals. Early work.
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## Intended uses & limitations
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It is intended to be used as part of the [fight health insurance web app](https://www.fighthealthinsurance.com/) [who's repo is at https://github.com/totallylegitco/fighthealthinsurance](https://github.com/totallylegitco/fighthealthinsurance)
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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: 5e-06
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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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- gradient_accumulation_steps: 4
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- total_train_batch_size: 8
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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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| 1.0397 | 0.0004 | 1 | 1.1590 |
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| 0.6084 | 0.1002 | 230 | 0.7272 |
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| 0.5195 | 0.2003 | 460 | 0.7141 |
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| 0.4713 | 0.3005 | 690 | 0.7090 |
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| 0.3973 | 0.4007 | 920 | 0.7097 |
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| 0.3306 | 0.5009 | 1150 | 0.7145 |
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| 0.3507 | 0.6010 | 1380 | 0.7136 |
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| 0.3125 | 0.7012 | 1610 | 0.7200 |
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| 0.3055 | 0.8014 | 1840 | 0.7227 |
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| 0.2027 | 0.9016 | 2070 | 0.7301 |
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| 0.2632 | 1.0017 | 2300 | 0.7471 |
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| 0.2077 | 1.0851 | 2530 | 0.7662 |
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| 0.0992 | 1.1853 | 2760 | 0.7744 |
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| 0.236 | 1.2855 | 2990 | 0.7844 |
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| 0.1572 | 1.3857 | 3220 | 0.7915 |
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| 0.192 | 1.4858 | 3450 | 0.7921 |
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| 0.1812 | 1.5860 | 3680 | 0.7968 |
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| 0.1973 | 1.6862 | 3910 | 0.7979 |
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| 0.1422 | 1.7864 | 4140 | 0.7982 |
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| 0.1315 | 1.8865 | 4370 | 0.7984 |
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### Framework versions
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- Transformers 4.42.3
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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