---
library_name: peft
license: llama3.1
base_model: VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d7dacba5-abc9-44d3-92a0-9deec81dc181
  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
adapter: lora
base_model: VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 6d961e5ee0b627ef_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/6d961e5ee0b627ef_train_data.json
  type:
    field_instruction: text
    field_output: all_events
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device: cuda
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config:
  max_steps: 50
  weight_decay: 0.01
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: false
hub_model_id: ivangrapher/d7dacba5-abc9-44d3-92a0-9deec81dc181
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/6d961e5ee0b627ef_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 70
sequence_len: 2048
special_tokens:
  pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: d7dacba5-abc9-44d3-92a0-9deec81dc181
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d7dacba5-abc9-44d3-92a0-9deec81dc181
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# d7dacba5-abc9-44d3-92a0-9deec81dc181

This model is a fine-tuned version of [VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct](https://huggingface.co./VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5149

## 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: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 25

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0043 | 1    | 9.8786          |
| 9.1996        | 0.0128 | 3    | 9.8052          |
| 9.7841        | 0.0256 | 6    | 8.2528          |
| 5.9899        | 0.0384 | 9    | 3.2230          |
| 1.8292        | 0.0512 | 12   | 0.8131          |
| 0.7333        | 0.064  | 15   | 0.6106          |
| 0.5552        | 0.0768 | 18   | 0.5230          |
| 0.4576        | 0.0896 | 21   | 0.5373          |
| 0.6688        | 0.1024 | 24   | 0.5149          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1