---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: med-lora/Llama2-Medtext-txt-lora-epochs-2-lr-0001
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<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)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Llama-2-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: utrgvseniorproject/medtext-txt
    type: completion
dataset_prepared_path: /home/ethensanchez01/med-llm/last_run_prepared
val_set_size: 0.05
output_dir: ./med-lora/Llama2-Medtext-txt-lora-epochs-2-lr-0001

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

wandb_project: Llama2-Medtext-Lora
wandb_entity: utrgvmedai
wandb_watch:
wandb_name: Llama2-Medtext-txt-lora-epochs-2-lr-0001
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0001

train_on_inputs: True # make sure you have this on True
group_by_length: false
bf16: true
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true

warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
eval_sample_packing:
save_steps: 800
debug:
deepspeed: /home/ethensanchez01/src/axolotl/deepspeed_configs/zero2.json
weight_decay: 0.001
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# med-lora/Llama2-Medtext-txt-lora-epochs-2-lr-0001

This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4128

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.532         | 0.19  | 1    | 1.4208          |
| 1.5994        | 0.38  | 2    | 1.4210          |
| 1.6281        | 0.76  | 4    | 1.4198          |
| 1.6221        | 1.05  | 6    | 1.4168          |
| 1.5891        | 1.43  | 8    | 1.4136          |
| 1.582         | 1.81  | 10   | 1.4128          |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.0