---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: Qwen/Qwen1.5-7B
model-index:
- name: qwen_1.5_odia_7b
  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
# Qwen/Qwen1.5-7B

base_model: Qwen/Qwen1.5-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

# is_qwen_derived_model: true
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: OdiaGenAIdata/culturax-odia
    type: completion
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out-qwen-7b-odia
hub_model_id: sam2ai/qwen_1.5_odia_7b

sequence_len: 2048  # supports up to 8192
sample_packing: false
pad_to_sequence_len:

adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: Qwen-completion-7b-odia
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 10
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: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# qwen_1.5_odia_7b

This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan

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

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.8734        | 0.0   | 1      | nan             |
| 0.377         | 0.25  | 5463   | nan             |
| 0.3727        | 0.5   | 10926  | nan             |
| 0.3894        | 0.75  | 16389  | nan             |
| 0.3933        | 1.0   | 21852  | nan             |
| 0.3542        | 1.25  | 27315  | nan             |
| 0.3567        | 1.5   | 32778  | nan             |
| 0.3557        | 1.75  | 38241  | nan             |
| 0.3414        | 2.0   | 43704  | nan             |
| 0.3261        | 2.25  | 49167  | nan             |
| 0.3813        | 2.5   | 54630  | nan             |
| 0.3607        | 2.75  | 60093  | nan             |
| 0.3348        | 3.0   | 65556  | nan             |
| 0.3464        | 3.25  | 71019  | nan             |
| 0.3545        | 3.5   | 76482  | nan             |
| 0.2719        | 3.75  | 81945  | nan             |
| 0.3158        | 4.0   | 87408  | nan             |
| 0.3119        | 4.25  | 92871  | nan             |
| 0.3311        | 4.5   | 98334  | nan             |
| 0.3335        | 4.75  | 103797 | nan             |
| 0.3399        | 5.0   | 109260 | nan             |
| 0.3247        | 5.25  | 114723 | nan             |
| 0.3166        | 5.5   | 120186 | nan             |
| 0.3366        | 5.75  | 125649 | nan             |
| 0.3478        | 6.0   | 131112 | nan             |
| 0.2852        | 6.25  | 136575 | nan             |
| 0.2852        | 6.5   | 142038 | nan             |
| 0.2601        | 6.75  | 147501 | nan             |
| 0.2734        | 7.0   | 152964 | nan             |
| 0.2983        | 7.25  | 158427 | nan             |
| 0.2068        | 7.5   | 163890 | nan             |
| 0.2355        | 7.75  | 169353 | nan             |
| 0.2836        | 8.0   | 174816 | nan             |
| 0.2263        | 8.25  | 180279 | nan             |
| 0.2953        | 8.5   | 185742 | nan             |
| 0.257         | 8.75  | 191205 | nan             |
| 0.2484        | 9.0   | 196668 | nan             |
| 0.2477        | 9.25  | 202131 | nan             |
| 0.2641        | 9.5   | 207594 | nan             |
| 0.2569        | 9.75  | 213057 | nan             |
| 0.2846        | 10.0  | 218520 | nan             |


### Framework versions

- PEFT 0.8.2
- Transformers 4.37.0
- Pytorch 2.0.1+gita61a294
- Datasets 2.16.1
- Tokenizers 0.15.0