peft pytorch tensorboard safetensors mistral text-generation axolotl generated_from_trainer transformers dataset:tatsu-lab/alpaca base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 region:us
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- axolotl
- generated_from_trainer
- peft
- transformers
model-index:
- name: Mistral-7B-Alpaca-52k-v0.2
  results: []
datasets:
- tatsu-lab/alpaca
pipeline_tag: text-generation
---

<!-- 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: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true


hub_model_id: MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.2
hf_use_auth_token: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: tatsu-lab/alpaca
    type: alpaca
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.2

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

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
```

</details><br>

# Mistral-7B-Alpaca-52k-v0.2

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9730

## 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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3017        | 0.04  | 1    | 1.4067          |
| 1.1285        | 0.25  | 6    | 1.0677          |
| 1.0586        | 0.5   | 12   | 0.9915          |
| 1.0515        | 0.75  | 18   | 0.9769          |
| 1.0608        | 1.0   | 24   | 0.9700          |
| 1.0003        | 1.23  | 30   | 0.9689          |
| 0.9761        | 1.48  | 36   | 0.9679          |
| 0.9783        | 1.73  | 42   | 0.9659          |
| 0.9631        | 1.98  | 48   | 0.9663          |
| 0.9273        | 2.21  | 54   | 0.9724          |
| 0.9093        | 2.46  | 60   | 0.9720          |
| 0.9038        | 2.71  | 66   | 0.9729          |
| 0.903         | 2.96  | 72   | 0.9724          |
| 0.9231        | 3.19  | 78   | 0.9725          |
| 0.9017        | 3.44  | 84   | 0.9729          |
| 0.9279        | 3.69  | 90   | 0.9730          |
| 0.9069        | 3.94  | 96   | 0.9730          |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.0