README.md

---
base_model: mistralai/Mistral-Nemo-Base-2407
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: qlora_outputs_1ep
  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:

datasets:
  - path: /home/austin/disk1/summaries_fixed.jsonl
    type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./qlora_outputs_1ep

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

wandb_project: summarization-qlora
wandb_entity:
wandb_watch:
wandb_name: actual_run1
wandb_log_model:
 
 #unsloth_cross_entropy_loss: true

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
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
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention: false
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 25
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: ./deepspeed_configs/zero2.json
weight_decay: 0.0
fsdp:
  #  - full_shard
  #  - auto_wrap
fsdp_config:
  #  fsdp_limit_all_gathers: true
  #  fsdp_activation_checkpointing: true
  #  fsdp_sync_module_states: true
  #  fsdp_offload_params: false
  #  fsdp_use_orig_params: false
  #  fsdp_cpu_ram_efficient_loading: false
  #  fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
  #  fsdp_state_dict_type: FULL_STATE_DICT
  #  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
special_tokens:
  pad_token: </s>

```

</details><br>

# qlora_outputs_1ep

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: 1.1865

## 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: 4
- total_train_batch_size: 4
- 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: 25
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0177        | 0.0014 | 1    | 1.6514          |
| 1.623         | 0.2507 | 177  | 1.2010          |
| 1.4373        | 0.5014 | 354  | 1.1926          |
| 1.6839        | 0.7521 | 531  | 1.1865          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1