---
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-70B
model-index:
- name: output/llama3-70b
  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: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3-70B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: awilliamson/qbank_conversations
    type: chat_template
    chat_template: llama3
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      system:
        - system
      user:
        - user
      assistant:
        - assistant
chat_template: llama3
adapter: qlora
lora_r: 32
lora_alpha: 16
lora_modules_to_save: [embed_tokens, lm_head]
lora_dropout: 0.05
lora_target_linear: true

dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./output/llama3-70b

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true

wandb_project: llama-70b
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 1e-5

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: 15
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
save_total_limit: 10
save_steps:
debug:
weight_decay: 0.00
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
special_tokens:
  pad_token: "<|end_of_text|>"
```

</details><br>

# output/llama3-70b

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3901

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: 15
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.3783        | 0.0388 | 1    | 2.8294          |
| 1.2438        | 0.1942 | 5    | 1.4718          |
| 1.1973        | 0.3883 | 10   | 1.4697          |
| 1.0995        | 0.5825 | 15   | 1.4572          |
| 1.181         | 0.7767 | 20   | 1.4470          |
| 1.1298        | 0.9709 | 25   | 1.4350          |
| 0.9058        | 1.1650 | 30   | 1.4232          |
| 0.8712        | 1.3592 | 35   | 1.4126          |
| 0.8735        | 1.5534 | 40   | 1.4051          |
| 0.8975        | 1.7476 | 45   | 1.4024          |
| 0.929         | 1.9417 | 50   | 1.3951          |
| 0.9181        | 2.1359 | 55   | 1.3923          |
| 0.9171        | 2.3301 | 60   | 1.3917          |
| 0.9111        | 2.5243 | 65   | 1.3907          |
| 0.9676        | 2.7184 | 70   | 1.3904          |
| 0.8497        | 2.9126 | 75   | 1.3901          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1