---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: L3.1-Pneuma-8B
  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: `0.5.0`
```yaml
base_model: meta-llama/Llama-3.1-8B-Instruct

load_in_8bit: false
load_in_4bit: false
strict: false

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Sandevistan_cleaned.jsonl
    type: customllama3_stan
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/out

fix_untrained_tokens: true

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

wandb_project: Pneuma
wandb_entity: 
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 8
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0000078
max_grad_norm: 1

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

gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
eval_sample_packing: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

hub_model_id: Replete-AI/L3.1-Pneuma-8B
hub_strategy: every_save

warmup_steps: 0
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 3
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|begin_of_text|>"
  eos_token: "<|end_of_text|>"
  pad_token: "<|end_of_text|>"
tokens:

```

</details><br>

# L3.1-Pneuma-8B

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the [Sandevistan](https://huggingface.co/datasets/Replete-AI/Sandevistan) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4357

## 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: 7.8e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0731        | 0.0023 | 1    | 2.7679          |
| 0.6458        | 0.3338 | 143  | 2.4576          |
| 0.6504        | 0.6675 | 286  | 2.4407          |
| 1.112         | 1.0019 | 429  | 2.4358          |
| 0.6014        | 1.3357 | 572  | 2.4358          |
| 0.6194        | 1.6694 | 715  | 2.4357          |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3