---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 30a236ed-8649-4c2c-987d-2cacf5f85436
  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: NousResearch/CodeLlama-13b-hf
bf16: true
chat_template: llama3
datasets:
- data_files:
  - 761ba631f7869617_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/761ba631f7869617_train_data.json
  type:
    field_input: text
    field_instruction: name
    field_output: label
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: lesso02/30a236ed-8649-4c2c-987d-2cacf5f85436
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 30
micro_batch_size: 4
mlflow_experiment_name: /tmp/761ba631f7869617_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 20
save_strategy: steps
sequence_len: 1024
special_tokens:
  pad_token: </s>
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: 30a236ed-8649-4c2c-987d-2cacf5f85436
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 30a236ed-8649-4c2c-987d-2cacf5f85436
warmup_steps: 5
weight_decay: 0.01
xformers_attention: false

```

</details><br>

# 30a236ed-8649-4c2c-987d-2cacf5f85436

This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7129

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 5
- training_steps: 30

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 7.5749        | 0.0000 | 1    | 3.9099          |
| 7.5191        | 0.0001 | 4    | 3.9077          |
| 6.6592        | 0.0003 | 8    | 3.8825          |
| 6.9418        | 0.0004 | 12   | 3.8428          |
| 7.0457        | 0.0005 | 16   | 3.7901          |
| 6.8632        | 0.0007 | 20   | 3.7472          |
| 7.4406        | 0.0008 | 24   | 3.7231          |
| 7.8922        | 0.0010 | 28   | 3.7129          |


### Framework versions

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