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strickvl
/
isafpr-gemma-qlora-templatefree

PEFT
Safetensors
gemma
axolotl
Generated from Trainer
4-bit precision
bitsandbytes
Model card Files Community
isafpr-gemma-qlora-templatefree
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---
license: gemma
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: google/gemma-2b
model-index:
- name: isafpr-gemma-qlora-templatefree
  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: `0.4.1`
```yaml
# use google/gemma-7b if you have access
base_model: google/gemma-2b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: data/templatefree_isaf_press_releases_ft_train.jsonl
    type: input_output
val_set_size: 0.1
output_dir: ./outputs/gemma/qlora-out-templatefree
hub_model_id: strickvl/isafpr-gemma-qlora-templatefree

adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
 - embed_tokens
 - lm_head

sequence_len: 1024
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: isaf_pr_ft
wandb_entity: strickvl
wandb_watch:
wandb_name:
wandb_log_model:


gradient_accumulation_steps: 3
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"

```

</details><br>

# isafpr-gemma-qlora-templatefree

This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0379

## 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
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 3
- total_train_batch_size: 12
- 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: 64
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.3995        | 0.0054 | 1    | 2.3804          |
| 0.1051        | 0.2527 | 47   | 0.0906          |
| 0.0444        | 0.5054 | 94   | 0.0617          |
| 0.0292        | 0.7581 | 141  | 0.0490          |
| 0.1049        | 1.0108 | 188  | 0.0475          |
| 0.03          | 1.2419 | 235  | 0.0435          |
| 0.0219        | 1.4946 | 282  | 0.0411          |
| 0.0286        | 1.7473 | 329  | 0.0413          |
| 0.0403        | 2.0    | 376  | 0.0383          |
| 0.0274        | 2.2330 | 423  | 0.0386          |
| 0.0178        | 2.4857 | 470  | 0.0384          |
| 0.0272        | 2.7384 | 517  | 0.0378          |
| 0.0409        | 2.9910 | 564  | 0.0371          |
| 0.013         | 3.2240 | 611  | 0.0378          |
| 0.0177        | 3.4767 | 658  | 0.0380          |
| 0.018         | 3.7294 | 705  | 0.0379          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1