---
license: mit
base_model: microsoft/phi-2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: phi2-filter2
results: []
---
[
](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
axolotl version: `0.3.0`
```yaml
base_model: microsoft/phi-2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false
hub_model_id: satpalsr/phi2-filter2
hf_use_auth_token: true
datasets:
- path: satpalsr/translation-filter
type: completion
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./phi-sft-out2
sequence_len: 2048
sample_packing: false # currently unsupported
pad_to_sequence_len:
adapter:
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
- embd
- lm_head
wandb_project: phi2transfilter
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 4
optimizer: paged_adamw_8bit
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false
warmup_steps: 100
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true
special_tokens:
pad_token: "<|endoftext|>"
```
# phi2-filter2
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1944
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5676 | 0.01 | 1 | 2.5391 |
| 2.4364 | 0.25 | 29 | 2.4042 |
| 1.9523 | 0.5 | 58 | 1.8580 |
| 1.1137 | 0.75 | 87 | 0.9535 |
| 0.5107 | 1.0 | 116 | 0.4195 |
| 0.4588 | 1.25 | 145 | 0.2877 |
| 0.2876 | 1.5 | 174 | 0.2462 |
| 0.2959 | 1.75 | 203 | 0.2264 |
| 0.2197 | 2.0 | 232 | 0.2114 |
| 0.3045 | 2.25 | 261 | 0.2052 |
| 0.2726 | 2.5 | 290 | 0.2022 |
| 0.3046 | 2.75 | 319 | 0.1975 |
| 0.3316 | 3.0 | 348 | 0.1954 |
| 0.2223 | 3.25 | 377 | 0.1950 |
| 0.2609 | 3.5 | 406 | 0.1946 |
| 0.2739 | 3.75 | 435 | 0.1945 |
| 0.2703 | 4.0 | 464 | 0.1944 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0