---
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: NousResearch/Llama-2-7b-hf
model-index:
- name: MathLlama-7b
  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: `0.3.0`
```yaml
base_model: NousResearch/Llama-2-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
hub_model_id: MathLlama-7b

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: zorooo/Eval_Math_Derivatives
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./qlora-out-2

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: axolotl_run_1_math_llama
wandb_entity:
wandb_watch:
wandb_name: math_llama_run2
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 5
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
```

</details><br>

# MathLlama-7b

This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1580

## 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: 0.0002
- 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: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.952         | 0.04  | 1    | 0.9490          |
| 0.9351        | 0.27  | 7    | 0.9474          |
| 0.9431        | 0.54  | 14   | 0.9181          |
| 0.8078        | 0.82  | 21   | 0.7671          |
| 0.5693        | 1.06  | 28   | 0.5249          |
| 0.309         | 1.33  | 35   | 0.3288          |
| 0.2752        | 1.6   | 42   | 0.2607          |
| 0.2406        | 1.87  | 49   | 0.2267          |
| 0.2241        | 2.12  | 56   | 0.2068          |
| 0.2212        | 2.39  | 63   | 0.1932          |
| 0.1991        | 2.66  | 70   | 0.1842          |
| 0.173         | 2.93  | 77   | 0.1738          |
| 0.162         | 3.18  | 84   | 0.1711          |
| 0.1357        | 3.46  | 91   | 0.1681          |
| 0.15          | 3.73  | 98   | 0.1664          |
| 0.1553        | 4.0   | 105  | 0.1610          |
| 0.1263        | 4.25  | 112  | 0.1613          |
| 0.132         | 4.52  | 119  | 0.1580          |


### Framework versions

- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0