---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 385d0209-5a48-4c55-afeb-fa0021266d80
  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
auto_find_batch_size: true
base_model: microsoft/Phi-3-mini-4k-instruct
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 11bb3328a39885eb_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/11bb3328a39885eb_train_data.json
  type:
    field_instruction: query
    field_output: atom
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
early_stopping_threshold: 0.001
eval_max_new_tokens: 128
eval_steps: 20
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: sn56t0/385d0209-5a48-4c55-afeb-fa0021266d80
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 100
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
micro_batch_size: 32
mlflow_experiment_name: /tmp/11bb3328a39885eb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: null
sample_packing: false
save_steps: 20
saves_per_epoch: 0
seed: 754886094
sequence_len: 512
shuffle: true
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: sn56-miner
wandb_mode: disabled
wandb_name: null
wandb_project: god
wandb_run: 38ik
wandb_runid: null
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 385d0209-5a48-4c55-afeb-fa0021266d80

This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co./microsoft/Phi-3-mini-4k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6789

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 754886094
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 128
- 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: 16
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0075 | 1    | 1.6674          |
| No log        | 0.1509 | 20   | 0.9842          |
| No log        | 0.3019 | 40   | 0.7868          |
| No log        | 0.4528 | 60   | 0.7475          |
| No log        | 0.6038 | 80   | 0.7317          |
| 1.7686        | 0.7547 | 100  | 0.7231          |
| 1.7686        | 0.9057 | 120  | 0.7148          |
| 1.7686        | 1.0566 | 140  | 0.7108          |
| 1.7686        | 1.2075 | 160  | 0.7068          |
| 1.7686        | 1.3585 | 180  | 0.7043          |
| 1.3649        | 1.5094 | 200  | 0.7011          |
| 1.3649        | 1.6604 | 220  | 0.6980          |
| 1.3649        | 1.8113 | 240  | 0.6959          |
| 1.3649        | 1.9623 | 260  | 0.6925          |
| 1.3649        | 2.1132 | 280  | 0.6920          |
| 1.3269        | 2.2642 | 300  | 0.6920          |
| 1.3269        | 2.4151 | 320  | 0.6893          |
| 1.3269        | 2.5660 | 340  | 0.6882          |
| 1.3269        | 2.7170 | 360  | 0.6864          |
| 1.3269        | 2.8679 | 380  | 0.6846          |
| 1.2806        | 3.0189 | 400  | 0.6831          |
| 1.2806        | 3.1698 | 420  | 0.6832          |
| 1.2806        | 3.3208 | 440  | 0.6837          |
| 1.2806        | 3.4717 | 460  | 0.6820          |
| 1.2806        | 3.6226 | 480  | 0.6815          |
| 1.2517        | 3.7736 | 500  | 0.6806          |
| 1.2517        | 3.9245 | 520  | 0.6802          |
| 1.2517        | 4.0755 | 540  | 0.6800          |
| 1.2517        | 4.2264 | 560  | 0.6797          |
| 1.2517        | 4.3774 | 580  | 0.6792          |
| 1.2416        | 4.5283 | 600  | 0.6792          |
| 1.2416        | 4.6792 | 620  | 0.6789          |
| 1.2416        | 4.8302 | 640  | 0.6789          |
| 1.2416        | 4.9811 | 660  | 0.6789          |


### Framework versions

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