---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3414d43e-5776-46fb-9c1c-0c745e4249c5
  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
absolute_data_files: false
adapter: lora
base_model: microsoft/Phi-3-mini-128k-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
  - a8acb8fbf624e566_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_input: input
    field_instruction: instruct
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
dpo:
  beta: 0.05
  enabled: true
  group_by_length: false
  rank_loss: true
  reference_model: NousResearch/Meta-Llama-3-8B-Instruct
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.9
group_by_length: false
hub_model_id: sergioalves/3414d43e-5776-46fb-9c1c-0c745e4249c5
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-05
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/a8acb8fbf624e566_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
saves_per_epoch: 1
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: null
wandb_mode: online
wandb_name: 73900726-a2c5-4a6a-a029-478b5221794b
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 73900726-a2c5-4a6a-a029-478b5221794b
warmup_steps: 10
weight_decay: 0.05
xformers_attention: false

```

</details><br>

# 3414d43e-5776-46fb-9c1c-0c745e4249c5

This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5445

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 100

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.5126        | 0.0000 | 1    | 2.9043          |
| 6.3891        | 0.0020 | 50   | 2.6179          |
| 3.8619        | 0.0039 | 100  | 2.5445          |


### Framework versions

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