---
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: mit
tags:
- axolotl
- generated_from_trainer
model-index:
- name: phi3-deepseek-27k-cleanedplans-longtrain
  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
# model and tokenizer
base_model: microsoft/Phi-3-mini-4k-instruct # change for model
trust_remote_code: true
sequence_len: 2048

strict: false

model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
bf16: auto
pad_to_sequence_len: true
save_safetensors: true


datasets:
  - path: verifiers-for-code/cleaned_deepseek_plans
    type: completion
    field: text
    train_on_split: train

val_set_size: 0.05

# lora
adapter: lora
lora_r: 2048
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
  - embed_tokens
  - lm_head
use_rslora: true

# logging
wandb_project: valeris
wandb_name: phi3-deepseek-27k-cleanedplans-longtrain

output_dir: ./outputs/phi3-deepseek-27k-cleanedplans-longtrain

gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
micro_batch_size: 1
num_epochs: 3
eval_batch_size: 1
warmup_ratio: 0.05
learning_rate: 5e-6
lr_scheduler: cosine
optimizer: adamw_torch

hub_model_id: verifiers-for-code/phi3-deepseek-27k-cleanedplans-longtrain
push_to_hub: true
hub_always_push: true
evals_per_epoch: 8
saves_per_epoch: 4
logging_steps: 1
# eval_table_size: 10
# eval_max_new_tokens: 512

tokens: ["<thinking>", "</thinking>", "<plan>", "</plan>"]

special_tokens:
  pad_token: "<|endoftext|>"

```

</details><br>

# phi3-deepseek-27k-cleanedplans-longtrain

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: 0.3618

## 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: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 242
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5446        | 0.0006 | 1    | 0.4877          |
| 0.4894        | 0.1255 | 203  | 0.4453          |
| 0.4329        | 0.2509 | 406  | 0.3950          |
| 0.4223        | 0.3764 | 609  | 0.3772          |
| 0.3909        | 0.5019 | 812  | 0.3705          |
| 0.3837        | 0.6273 | 1015 | 0.3676          |
| 0.3959        | 0.7528 | 1218 | 0.3658          |
| 0.3516        | 0.8782 | 1421 | 0.3642          |
| 0.3757        | 1.0037 | 1624 | 0.3632          |
| 0.3222        | 1.1292 | 1827 | 0.3627          |
| 0.3095        | 1.2546 | 2030 | 0.3624          |
| 0.3234        | 1.3801 | 2233 | 0.3621          |
| 0.3776        | 1.5056 | 2436 | 0.3620          |
| 0.3471        | 1.6310 | 2639 | 0.3618          |
| 0.343         | 1.7565 | 2842 | 0.3617          |
| 0.3898        | 1.8820 | 3045 | 0.3618          |
| 0.3207        | 2.0074 | 3248 | 0.3618          |
| 0.3486        | 2.1329 | 3451 | 0.3618          |
| 0.3362        | 2.2583 | 3654 | 0.3618          |
| 0.3444        | 2.3838 | 3857 | 0.3618          |
| 0.3717        | 2.5093 | 4060 | 0.3618          |
| 0.3482        | 2.6347 | 4263 | 0.3618          |
| 0.3393        | 2.7602 | 4466 | 0.3617          |
| 0.3121        | 2.8857 | 4669 | 0.3618          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.44.0.dev0
- Pytorch 2.4.0
- Datasets 2.19.1
- Tokenizers 0.19.1