---
base_model: unsloth/Mistral-Small-Instruct-2409
library_name: peft
tags:
- axolotl
- generated_from_trainer
model-index:
- name: mistral-small-dampf-qlora
  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
# huggingface-cli login --token $hf_key && wandb login $wandb_key
# python -m axolotl.cli.preprocess ms-creative.yml
# accelerate launch -m axolotl.cli.train ms-creative.yml
# python -m axolotl.cli.merge_lora ms-creative.yml
# huggingface-cli upload Columbidae/ms-type2-creative train-workspace/merged . --private

# Model
base_model: unsloth/Mistral-Small-Instruct-2409
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false
bf16: true
fp16:
tf32: false
flash_attention: true
special_tokens:

# Output
output_dir: ./ms-creative
hub_model_id: BeaverAI/mistral-small-dampf-qlora
hub_strategy: "checkpoint"
resume_from_checkpoint:
saves_per_epoch: 5

# Data
sequence_len: 16384 # fits
min_sample_len: 128
dataset_prepared_path: last_run_prepared
datasets:
  - path: Dampfinchen/Creative_Writing_Multiturn
    type: custommistralv3
warmup_steps: 20
shuffle_merged_datasets: true
sample_packing: true
pad_to_sequence_len: true

# Batching
num_epochs: 1
gradient_accumulation_steps: 1
micro_batch_size: 5
eval_batch_size: 5

# Evaluation
val_set_size: 100
evals_per_epoch: 5
eval_table_size:
eval_max_new_tokens: 256
eval_sample_packing: false

save_safetensors: true

mlflow_tracking_uri: http://127.0.0.1:7860
mlflow_experiment_name: Default
# WandB
#wandb_project: Mistral-Small-Creative-Multiturn
#wandb_entity:

gradient_checkpointing: 'unsloth'
gradient_checkpointing_kwargs:
   use_reentrant: true

unsloth_cross_entropy_loss: true
#unsloth_lora_mlp: true
#unsloth_lora_qkv: true
#unsloth_lora_o: true

# LoRA
adapter: qlora
lora_model_dir:
lora_r: 64
lora_alpha: 128
lora_dropout: 0.125
lora_target_linear: 
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj
lora_modules_to_save:

# Optimizer
optimizer: paged_adamw_8bit # adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00005
cosine_min_lr_ratio: 0.1
weight_decay: 0.01
max_grad_norm: 1.0

# Misc
train_on_inputs: false
group_by_length: false
early_stopping_patience:
local_rank:
logging_steps: 1
xformers_attention:
debug:
deepspeed: deepspeed_configs/zero3.json # previously blank
fsdp:
fsdp_config:

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

```

</details><br>

# mistral-small-dampf-qlora

This model is a fine-tuned version of [unsloth/Mistral-Small-Instruct-2409](https://huggingface.co/unsloth/Mistral-Small-Instruct-2409) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0232

## 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: 5e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- distributed_type: multi-GPU
- num_devices: 6
- total_train_batch_size: 30
- total_eval_batch_size: 30
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.477         | 0.0065 | 1    | 1.3211          |
| 1.2338        | 0.2065 | 32   | 1.1156          |
| 1.1973        | 0.4129 | 64   | 1.0707          |
| 1.301         | 0.6194 | 96   | 1.0402          |
| 1.1063        | 0.8258 | 128  | 1.0232          |


### Framework versions

- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.20.0