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---
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
---
<!-- 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