license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- generated_from_trainer
model-index:
- name: mistral_fine_out
results: []
See axolotl config
axolotl version: 0.3.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: out/train_alpaca.jsonl
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: 4
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
The fine tuning script used for launch was from https://github.com/totallylegitco/healthinsurance-llm w/ run_remote.sh and an INPUT_MODEL=mistral
TotallyLegitCo/fighthealthinsurance_model_v0.3
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the syntehtic-appeal dataset. It achieves the following results on the evaluation set:
- Loss: 1.3954
Model description
Generate health insurance appeals. Early work.
Intended uses & limitations
Generate health insurance appeals. This is early work and may not be suitable for production.
Training and evaluation data
The syntehtic appeal dataset was used for training and evaluation. Given how the dataset was produced there is likely cross-contamination of the training and eval datasets so loss values are likely understated.
This model is intended to match the Mistral-7B-Instruct style with <s>[INST]Instructions[/INT] present (as well as system specific instructions within an extra <<SYS><</SYS>.
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0506 | 0.0 | 1 | 2.4510 |
| 0.8601 | 0.2 | 58 | 1.1493 |
| 0.8635 | 0.4 | 116 | 1.1356 |
| 0.869 | 0.61 | 174 | 1.1174 |
| 0.7764 | 0.81 | 232 | 1.1173 |
| 0.7803 | 1.01 | 290 | 1.1124 |
| 0.6902 | 1.2 | 348 | 1.1570 |
| 0.6774 | 1.4 | 406 | 1.1591 |
| 0.6859 | 1.6 | 464 | 1.1651 |
| 0.725 | 1.81 | 522 | 1.1677 |
| 0.6525 | 2.01 | 580 | 1.1686 |
| 0.5069 | 2.2 | 638 | 1.2688 |
| 0.4702 | 2.4 | 696 | 1.2767 |
| 0.4888 | 2.6 | 754 | 1.2852 |
| 0.5197 | 2.8 | 812 | 1.2881 |
| 0.4734 | 3.01 | 870 | 1.2851 |
| 0.3586 | 3.2 | 928 | 1.3856 |
| 0.3889 | 3.4 | 986 | 1.3929 |
| 0.3526 | 3.6 | 1044 | 1.3959 |
| 0.3832 | 3.8 | 1102 | 1.3954 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0