---
library_name: peft
license: llama3.1
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cc2561ca-8a57-4a52-a1ae-6aa020e37c55
  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: unsloth/Meta-Llama-3.1-8B
bf16: auto
bnb_config_kwargs:
  bnb_4bit_quant_type: nf4
  bnb_4bit_use_double_quant: true
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - f9972c9034fade77_train_data.json
  ds_type: json
  path: /workspace/input_data/f9972c9034fade77_train_data.json
  type:
    field_input: level
    field_instruction: problem
    field_output: solution
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: '{'''':torch.cuda.current_device()}'
do_eval: true
early_stopping_patience: 1
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: true
hub_model_id: cwaud/cc2561ca-8a57-4a52-a1ae-6aa020e37c55
hub_repo: cwaud
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 70GiB
  1: 70GiB
  2: 70GiB
  3: 70GiB
max_steps: 83
micro_batch_size: 1
mlflow_experiment_name: /tmp/f9972c9034fade77_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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: 50
save_strategy: steps
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: false
train_on_inputs: false
trust_remote_code: true
val_set_size: 50
wandb_entity: rayonlabs-rayon-labs
wandb_mode: online
wandb_name: cc2561ca-8a57-4a52-a1ae-6aa020e37c55
wandb_project: Public_TuningSN
wandb_run: miner_id_24
wandb_runid: cc2561ca-8a57-4a52-a1ae-6aa020e37c55
warmup_raio: 0.03
warmup_ratio: 0.04
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# cc2561ca-8a57-4a52-a1ae-6aa020e37c55

This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6261

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- 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: 3
- training_steps: 83

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6787        | 0.0002 | 1    | 0.9340          |
| 0.6192        | 0.0043 | 25   | 0.6883          |
| 0.6398        | 0.0085 | 50   | 0.6395          |
| 0.5756        | 0.0128 | 75   | 0.6261          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.1+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1