axolotl version: 0.4.1
base_model: mistralai/Mistral-7B-Instruct-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- data_files: out/train.jsonl
path: out/
ds_type: json
type:
alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./mistral_fine_out
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
auto_resume_from_checkpoint: true
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
eval_steps: 0.05
eval_table_size:
eval_table_max_new_tokens: 128
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
model_config:
sliding_window: 4096
mistral_fine_out
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 on a synthetic appeals dataset. See the health insurance fine tuning repo for details. An earlier version of this dataset is availabile.
It achieves the following results on the evaluation set:
- Loss: 0.7984
Model description
Generate health insurance appeals. Early work.
Intended uses & limitations
It is intended to be used as part of the fight health insurance web app who's repo is at https://github.com/totallylegitco/fighthealthinsurance
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0397 | 0.0004 | 1 | 1.1590 |
| 0.6084 | 0.1002 | 230 | 0.7272 |
| 0.5195 | 0.2003 | 460 | 0.7141 |
| 0.4713 | 0.3005 | 690 | 0.7090 |
| 0.3973 | 0.4007 | 920 | 0.7097 |
| 0.3306 | 0.5009 | 1150 | 0.7145 |
| 0.3507 | 0.6010 | 1380 | 0.7136 |
| 0.3125 | 0.7012 | 1610 | 0.7200 |
| 0.3055 | 0.8014 | 1840 | 0.7227 |
| 0.2027 | 0.9016 | 2070 | 0.7301 |
| 0.2632 | 1.0017 | 2300 | 0.7471 |
| 0.2077 | 1.0851 | 2530 | 0.7662 |
| 0.0992 | 1.1853 | 2760 | 0.7744 |
| 0.236 | 1.2855 | 2990 | 0.7844 |
| 0.1572 | 1.3857 | 3220 | 0.7915 |
| 0.192 | 1.4858 | 3450 | 0.7921 |
| 0.1812 | 1.5860 | 3680 | 0.7968 |
| 0.1973 | 1.6862 | 3910 | 0.7979 |
| 0.1422 | 1.7864 | 4140 | 0.7982 |
| 0.1315 | 1.8865 | 4370 | 0.7984 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1