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pyg3old_qlora_adapter

PEFT
mistral
Generated from Trainer
4-bit precision
bitsandbytes
Model card Files Community
pyg3old_qlora_adapter
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---
base_model: mistralai/Mistral-Nemo-Base-2407
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: home/austin/disk2/axolotl_storage/pyg3_qlora_2e-4
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: mistralai/Mistral-Nemo-Base-2407
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

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

loraplus_lr_ratio: 2  

chat_template: chatml

datasets:
  - path: /home/austin/disk2/axolotl_data/fixed_pyg3.jsonl
    type: sharegpt
    conversation: chatml

dataset_prepared_path: /home/austin/disk2/axolotl_data/data_tokenized
val_set_size: 0.01
output_dir: /home/austin/disk2/axolotl_storage/pyg3_qlora_2e-4

sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

wandb_project: pyg3-qlora
wandb_entity:
wandb_watch:
wandb_name: 1e-5
wandb_log_model:
 
 #unsloth_cross_entropy_loss: true

gradient_accumulation_steps: 1
micro_batch_size: 3
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 10
eval_table_size:
saves_per_epoch: 10
debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
  pad_token: </s>

```

</details><br>

# home/austin/disk2/axolotl_storage/pyg3_qlora_2e-4

This model is a fine-tuned version of [mistralai/Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8024

## 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: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 24
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8656        | 0.0006 | 1    | 1.1181          |
| 1.5716        | 0.1004 | 175  | 0.8479          |
| 1.6573        | 0.2008 | 350  | 0.8308          |
| 1.8387        | 0.3012 | 525  | 0.8230          |
| 1.5855        | 0.4016 | 700  | 0.8167          |
| 1.7139        | 0.5020 | 875  | 0.8123          |
| 1.5684        | 0.6024 | 1050 | 0.8087          |
| 1.6986        | 0.7028 | 1225 | 0.8055          |
| 1.6505        | 0.8032 | 1400 | 0.8035          |
| 1.6028        | 0.9036 | 1575 | 0.8024          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1