---
library_name: peft
base_model: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 47160379-0888-4d5a-b10a-23597f497138
  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: MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - dceb95c67dfd689a_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/dceb95c67dfd689a_train_data.json
  type:
    field_input: ingredients
    field_instruction: title
    field_output: directions
    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: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: leixa/47160379-0888-4d5a-b10a-23597f497138
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
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_steps: 400
micro_batch_size: 8
mlflow_experiment_name: /tmp/dceb95c67dfd689a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: leixa-personal
wandb_mode: online
wandb_name: abe5ea54-7e04-4354-bf82-c8ee9feb6e09
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: abe5ea54-7e04-4354-bf82-c8ee9feb6e09
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# 47160379-0888-4d5a-b10a-23597f497138

This model is a fine-tuned version of [MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4](https://huggingface.co/MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8088

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 400

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0001 | 1    | 1.0482          |
| 0.849         | 0.0023 | 34   | 0.8385          |
| 0.8132        | 0.0046 | 68   | 0.8286          |
| 0.8264        | 0.0069 | 102  | 0.8240          |
| 0.8151        | 0.0092 | 136  | 0.8195          |
| 0.7621        | 0.0115 | 170  | 0.8164          |
| 0.8271        | 0.0137 | 204  | 0.8147          |
| 0.787         | 0.0160 | 238  | 0.8127          |
| 0.8079        | 0.0183 | 272  | 0.8107          |
| 0.7842        | 0.0206 | 306  | 0.8097          |
| 0.825         | 0.0229 | 340  | 0.8090          |
| 0.8118        | 0.0252 | 374  | 0.8088          |


### Framework versions

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