peft tensorboard safetensors mistral axolotl generated_from_trainer base_model:openaccess-ai-collective/tiny-mistral 8-bit bitsandbytes region:us
---
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: openaccess-ai-collective/tiny-mistral
model-index:
- name: axolotl-test
  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: `0.3.0`
```yaml
base_model: openaccess-ai-collective/tiny-mistral
flash_attention: true
sequence_len: 1024
load_in_8bit: true
adapter: lora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
val_set_size: 0.1
special_tokens:
  unk_token: <unk>
  bos_token: <s>
  eos_token: </s>
datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
num_epochs: 2
micro_batch_size: 2
gradient_accumulation_steps: 1
output_dir: temp_dir
learning_rate: 0.00001
optimizer: adamw_torch
lr_scheduler: cosine
max_steps: 20
save_steps: 10
eval_steps: 10
hub_model_id: hamel/axolotl-test
dataset_processes: 1
```

</details><br>

# axolotl-test

This model is a fine-tuned version of [openaccess-ai-collective/tiny-mistral](https://huggingface.co/openaccess-ai-collective/tiny-mistral) on the None dataset.

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 20

### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: None
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32

### Framework versions


- PEFT 0.6.0
