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phi-2-layla-v1

Text Generation
Transformers
Safetensors
English
phi
custom_code
text-generation-inference
Inference Endpoints
Model card Files Community
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phi-2-layla-v1
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---
tags:
- generated_from_trainer
model-index:
- name: out
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: /home/layla/src/text-generation-webui/models/phi-2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: /home/layla/src/Layla-datasets/datasets_formatted/base/dailydialog.topicalchat.teatime.openhermes.jsonl
    ds_type: json # see other options below
    type: sharegpt
    conversation: vicuna_v1.1

# datasets:
#   - path: /home/layla/src/Layla-datasets/datasets_formatted/airoboros_alpaca.jsonl
#     type: alpaca

dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./out

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0000005

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: True
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.05
eval_steps: 0.1
eval_sample_packing: true
save_steps: 300
debug:
deepspeed: /home/layla/src/Layla-datasets/axolotl/configs/deepspeed/zero2.json # multi-gpu only
weight_decay: 0.0
fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true
special_tokens:
  bos_token: "<|endoftext|>"
  eos_token: "<|endoftext|>"
  unk_token: "<|endoftext|>"
  pad_token: "<|endoftext|>"
```

</details><br>

# out

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8072

## 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-07
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 5
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- total_eval_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 17
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9616        | 0.0   | 1    | 1.0031          |
| 0.9489        | 0.1   | 372  | 0.8825          |
| 0.987         | 0.2   | 744  | 0.8487          |
| 0.818         | 0.3   | 1116 | 0.8313          |
| 0.8389        | 0.4   | 1488 | 0.8212          |
| 0.9015        | 0.5   | 1860 | 0.8146          |
| 0.8237        | 0.6   | 2232 | 0.8108          |
| 0.7562        | 0.7   | 2604 | 0.8088          |
| 0.8776        | 0.8   | 2976 | 0.8078          |
| 0.8703        | 0.9   | 3348 | 0.8072          |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.0
- Datasets 2.17.1
- Tokenizers 0.15.0