---
base_model: unsloth/Mistral-Small-Instruct-2409
library_name: peft
tags:
- axolotl
- generated_from_trainer
model-index:
- name: mistral-small-fujin-qlora
  results: []
---
**NOT FOR PUBLIC USE**

This is only public so we can use it with a merging system that doesn't have access to the org.
<!-- 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-adventure.yml
# accelerate launch -m axolotl.cli.train ms-adventure.yml
# python -m axolotl.cli.merge_lora ms-adventure.yml

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

load_in_8bit: false
load_in_4bit: true
strict: false
sequence_len: 16384 # 99% vram
min_sample_len: 128
bf16: true
fp16:
tf32: false
flash_attention: true
special_tokens:

# Data
dataset_prepared_path: last_run_prepared
datasets:
  - path: botmall/rosier-inf-split-16k
    type: completion
warmup_steps: 20
shuffle_merged_datasets: true

save_safetensors: true

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

# Iterations
num_epochs: 1

# Output
output_dir: ./ms-fujin
hub_model_id: BeaverAI/mistral-small-fujin-qlora
hub_strategy: "checkpoint"

# Sampling
sample_packing: true
pad_to_sequence_len: true

# Batching
gradient_accumulation_steps: 1
micro_batch_size: 2
eval_batch_size: 2
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

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

# 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: 0.0001
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:

# Checkpoints
resume_from_checkpoint:
saves_per_epoch: 5

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-fujin-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: 2.5938

## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.9557        | 0.0031 | 1    | 2.6437          |
| 1.8648        | 0.2025 | 66   | 2.6013          |
| 1.9514        | 0.4049 | 132  | 2.5771          |
| 1.9213        | 0.6074 | 198  | 2.5940          |
| 1.9094        | 0.8098 | 264  | 2.5938          |


### Framework versions

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