---
library_name: peft
license: apache-2.0
base_model: unsloth/codellama-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 90d5b725-9dfb-405c-86fa-326350c1c1ee
  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/codellama-7b
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 2ad46d0313986df7_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/2ad46d0313986df7_train_data.json
  type:
    field_instruction: prompt
    field_output: chosen
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/90d5b725-9dfb-405c-86fa-326350c1c1ee
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
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
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1092
micro_batch_size: 4
mlflow_experiment_name: /tmp/2ad46d0313986df7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 100
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.03354781570171966
wandb_entity: null
wandb_mode: online
wandb_name: d47f6846-6c00-4ad8-aca7-52fa04750bcb
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d47f6846-6c00-4ad8-aca7-52fa04750bcb
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 90d5b725-9dfb-405c-86fa-326350c1c1ee

This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6689

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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: 1092

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6172        | 0.0002 | 1    | 2.5837          |
| 1.5458        | 0.0222 | 100  | 1.7610          |
| 1.6923        | 0.0444 | 200  | 1.7392          |
| 1.6922        | 0.0666 | 300  | 1.7222          |
| 1.8           | 0.0889 | 400  | 1.7104          |
| 1.7277        | 0.1111 | 500  | 1.6967          |
| 1.7427        | 0.1333 | 600  | 1.6879          |
| 1.8182        | 0.1555 | 700  | 1.6800          |
| 1.6517        | 0.1777 | 800  | 1.6742          |
| 1.7055        | 0.1999 | 900  | 1.6706          |
| 1.646         | 0.2222 | 1000 | 1.6689          |


### Framework versions

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