---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Llama-3-8B-flan
  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/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)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: /vast/work/public/ml-datasets/flan/cot_submix_data.jsonl
    type:
      system_prompt: "You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability."
      field_system: system
      field_instruction: inputs
      field_output: targets
  - path: /vast/work/public/ml-datasets/flan/niv2_submix_data.jsonl
    type:
      system_prompt: "You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability."
      field_system: system
      field_instruction: inputs
      field_output: targets
  - path: /vast/work/public/ml-datasets/flan/dialog_submix_data.jsonl
    type:
      system_prompt: "You are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability."
      field_system: system
      field_instruction: inputs
      field_output: targets
dataset_prepared_path: /scratch/bf996/axolotl/datasets/flan-mix
val_set_size: 0.001
output_dir: /scratch/bf996/axolotl/outputs/llama3-8b-flan-v2.0
chat_template: llama3
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: lm-evals
wandb_entity:
wandb_watch:
wandb_name: Llama-3-8B-flan
wandb_log_model:
hub_model_id: penfever/Llama-3-8B-flan

shuffle_merged_datasets: true

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
max_steps: 10000

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
save_strategy: steps
save_steps: 500
save_total_limit: 5
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/nyu-dice-lab/lm-evals/runs/3cv1xhof)
# Llama-3-8B-flan

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co./meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 10000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 2.0576        | 0.0000 | 1     | nan             |
| 1.172         | 0.1090 | 2500  | nan             |
| 1.194         | 0.2181 | 5000  | nan             |
| 1.1629        | 0.3271 | 7500  | nan             |
| 1.0608        | 0.4362 | 10000 | nan             |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1