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should probably proofread and complete it, then remove this comment. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: ravithejads/telugumodels
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: ravithejads/telugu_alpaca
    type: alpaca
  - path: ravithejads/telugu_gpteacher
    type: alpaca
  - path: ravithejads/telugu_gpteacher_ft
    type: alpaca
  - path: ravithejads/telugu_alpaca_ft
    type: alpaca
dataset_prepared_path:
val_set_size: 0.1
output_dir: ./telugu-100

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: finetuning
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model: "checkpoint"

gradient_accumulation_steps: 8
micro_batch_size: 16
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000075

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

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

warmup_steps: 10
evals_steps: 250
eval_sample_packing: False
eval_table_size:
eval_table_max_new_tokens:
save_steps: 50
debug:
deepspeed:
weight_decay: 0.001
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```

</details><br>

# telugu-100

This model is a fine-tuned version of [ravithejads/telugumodels](https://huggingface.co/ravithejads/telugumodels) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7362

## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7487        | 1.01  | 66   | 0.7978          |
| 0.7067        | 1.97  | 130  | 0.7362          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.37.0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "ravithejads/telugumodels", "model-index": [{"name": "telugu-100", "results": []}]}
peft safetensors llama generated_from_trainer base_model:ravithejads/telugumodels license:mit region:us