---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: google/gemma-2b-it
model-index:
- name: gemma-2b-hindi-it
  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
# use google/gemma-7b if you have access
base_model: google/gemma-2b-it
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

bnb_config_kwargs:
  # These are default values
  llm_int8_has_fp16_weight: false
  bnb_4bit_quant_type: nf4
  bnb_4bit_use_double_quant: true

# huggingface repo
datasets:
  - path: jayshah5696/samvaad-hi-v1_gemma_format
    type: completion
    field: text
val_set_size: 0.05
dataset_prepared_path: ./LLM-data
output_dir: ./out

adapter: qlora
lora_r: 4
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

wandb_project: gemma_openhathi
wandb_run_id: model_03_qlora
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:


gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

bfloat16: true

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 10
xformers_attention:
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 5
eval_table_size:
# eval_max_new_tokens: 128
metric_for_best_model: "eval_loss"
saves_per_epoch: 20
save_total_limit: 20 
load_best_model_at_end: True

debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

seed: 108

```

</details><br>

# out

This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5293

## 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: 108
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 453
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.0   | 1    | 3.7785          |
| 1.6305        | 0.2   | 965  | 1.6443          |
| 1.5355        | 0.4   | 1930 | 1.5893          |
| 1.5383        | 0.6   | 2895 | 1.5557          |
| 1.5223        | 0.8   | 3860 | 1.5350          |
| 1.5477        | 1.0   | 4825 | 1.5293          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.0