---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: miner_id_f440f781-9df5-4143-92fd-390f41cfa5f5
  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
base_model: microsoft/Phi-3-mini-128k-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: /workspace/input_data/1f4f410266a6e550_train_data.json
  format: custom
  type:
    system_prompt: ''
    system_format: '{system}'
    field_instruction: topic
    field_input: text
    field_output: target
    no_input_format: '{instruction}'
    format: '{instruction} {input}'
  ds_type: json
  data_files:
  - 1f4f410266a6e550_train_data.json
dataset_prepared_path: null
val_set_size: 0.05
output_dir: miner_id_f440f781-9df5-4143-92fd-390f41cfa5f5
sequence_len: 4056
sample_packing: false
pad_to_sequence_len: true
trust_remote_code: true
adapter: lora
lora_model_dir: null
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out: null
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16: null
tf32: false
gradient_checkpointing: false
early_stopping_patience: null
resume_from_checkpoint: null
local_rank: null
logging_steps: 1
xformers_attention: null
flash_attention: true
s2_attention: null
wandb_project: Gradients-On-Demand
wandb_entity: prongsie
wandb_mode: online
wandb_run: your_name
wandb_runid: default
hub_model_id: tensor24/miner_id_f440f781-9df5-4143-92fd-390f41cfa5f5
hub_repo: tensor24/miner_id_f440f781-9df5-4143-92fd-390f41cfa5f5
hub_strategy: checkpoint
hub_token: null
saves_per_epoch: 4
warmup_steps: 10
evals_per_epoch: 4
eval_table_size: null
eval_max_new_tokens: 128
max_steps: 10
debug: null
deepspeed: null
weight_decay: 0.0
fsdp: null
fsdp_config: null
tokenizer_config: microsoft/Phi-3-mini-128k-instruct
mlflow_experiment_name: /tmp/1f4f410266a6e550_train_data.json

```

</details><br>

# miner_id_f440f781-9df5-4143-92fd-390f41cfa5f5

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: 4.6310

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 37.0272       | 0.0007 | 1    | 9.6565          |
| 39.1861       | 0.0022 | 3    | 9.5608          |
| 38.8873       | 0.0044 | 6    | 8.5297          |
| 22.1419       | 0.0066 | 9    | 4.6310          |


### Framework versions

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