---
library_name: transformers
license: mit
base_model: microsoft/Phi-3.5-mini-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Phi-3.5-mini-instruct-Code50000-Test
  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-3.5-mini-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
chat_template: phi_3

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: flydust/CodeGen_50000_Test
    type: chat_template
    field_messages: conversations
    # The key in the message turn that contains the role. Default is "role".
    message_field_role: from
    # The key in the message turn that contains the content. Default is "content".
    message_field_content: value
    # Optional[Dict[str, List]]. Roles mapping for the messages.
    roles:
      user: ["human", "user"]
      assistant: ["gpt", "assistant", "ai"]
      system: ["system"]


dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: axolotl_out/Phi-3.5-mini-instruct-Code50000-Test

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Phi-3.5-mini-instruct-Code50000-Test
wandb_log_model:
hub_model_id: flydust/Phi-3.5-mini-instruct-Code50000-Test

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
# Disable flash attention
flash_attention: true
# sdp_attention: falses
# eager_attention: true

warmup_ratio: 0.1
evals_per_epoch: 10
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```

</details><br>

# Phi-3.5-mini-instruct-Code50000-Test

This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1712

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 21
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4415        | 0.0093 | 1    | 0.4636          |
| 0.2376        | 0.1019 | 11   | 0.2367          |
| 0.2014        | 0.2037 | 22   | 0.2002          |
| 0.1824        | 0.3056 | 33   | 0.1895          |
| 0.1728        | 0.4074 | 44   | 0.1817          |
| 0.1764        | 0.5093 | 55   | 0.1786          |
| 0.1822        | 0.6111 | 66   | 0.1766          |
| 0.1661        | 0.7130 | 77   | 0.1750          |
| 0.171         | 0.8148 | 88   | 0.1740          |
| 0.1577        | 0.9167 | 99   | 0.1741          |
| 0.1615        | 1.0162 | 110  | 0.1722          |
| 0.1551        | 1.1181 | 121  | 0.1720          |
| 0.1676        | 1.2199 | 132  | 0.1724          |
| 0.1583        | 1.3218 | 143  | 0.1714          |
| 0.164         | 1.4236 | 154  | 0.1713          |
| 0.1581        | 1.5255 | 165  | 0.1717          |
| 0.1496        | 1.6273 | 176  | 0.1707          |
| 0.1563        | 1.7292 | 187  | 0.1710          |
| 0.1518        | 1.8310 | 198  | 0.1707          |
| 0.1687        | 1.9329 | 209  | 0.1712          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 2.20.0
- Tokenizers 0.19.1