---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf-flash
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a98d23dd-889c-48b6-a02d-ec7eec821dd5
  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-flash
bf16: true
chat_template: llama3
datasets:
- data_files:
  - af2776e3f8d7bb4e_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/af2776e3f8d7bb4e_train_data.json
  type:
    field_instruction: Name
    field_output: Descriptor
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: lesso10/a98d23dd-889c-48b6-a02d-ec7eec821dd5
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
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_steps: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/af2776e3f8d7bb4e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 512
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: 8942483c-1cfc-4e12-8246-93c0d39139ac
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8942483c-1cfc-4e12-8246-93c0d39139ac
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# a98d23dd-889c-48b6-a02d-ec7eec821dd5

This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf-flash](https://huggingface.co/NousResearch/CodeLlama-13b-hf-flash) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6205

## 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: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB 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 |
|:-------------:|:------:|:----:|:---------------:|
| 7.2779        | 0.0006 | 1    | 1.9802          |
| 7.1895        | 0.0031 | 5    | 1.9733          |
| 7.2103        | 0.0062 | 10   | 1.8486          |
| 6.7976        | 0.0093 | 15   | 1.6854          |
| 6.1401        | 0.0124 | 20   | 1.6285          |
| 6.5449        | 0.0155 | 25   | 1.6205          |


### Framework versions

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