---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 256f9e7b-2983-48ef-8be0-c98dfc6f9fee
  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: microsoft/Phi-3-mini-4k-instruct
bf16: true
chat_template: llama3
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
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: ardaspear/256f9e7b-2983-48ef-8be0-c98dfc6f9fee
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_modules_to_save:
- lm_head
lora_r: 64
lora_target_linear: true
loraplus_lr_ratio: 8
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 600
micro_batch_size: 8
mlflow_experiment_name: /tmp/11bb3328a39885eb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-05
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
peft_use_rslora: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 150
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: 7ce72720-2645-47e1-9d12-b5a347cab783
wandb_project: Gradients-On-Five
wandb_run: your_name
wandb_runid: 7ce72720-2645-47e1-9d12-b5a347cab783
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# 256f9e7b-2983-48ef-8be0-c98dfc6f9fee

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: 3.9896

## 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: 8
- eval_batch_size: 4
- 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=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 600

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0019 | 1    | 4.4778          |
| 8.2119        | 0.0943 | 50   | 7.4195          |
| 6.9458        | 0.1887 | 100  | 14.9005         |
| 6.2023        | 0.2830 | 150  | 11.4503         |
| 3.7041        | 0.3774 | 200  | 10.5616         |
| 3.5489        | 0.4717 | 250  | 13.1156         |
| 4.6485        | 0.5660 | 300  | 15.8167         |
| 1.8853        | 0.6604 | 350  | 12.1802         |
| 1.821         | 0.7547 | 400  | 10.7766         |
| 1.0218        | 0.8491 | 450  | 20.4883         |
| 1.6077        | 0.9434 | 500  | 4.6230          |
| 24.8611       | 1.0377 | 550  | 3.9979          |
| 24.3324       | 1.1321 | 600  | 3.9896          |


### Framework versions

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