README.md

---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
model-index:
- name: nohto-v0-10e
  results: []
---

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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-Instruct-v0.2
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: inst

datasets:
  - path: ./data/nohto/training.jsonl
    type: sharegpt

dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ../nohto-v0-10e

adapter: lora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 16
lora_alpha: 32
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:

eval_sample_packing: false

hub_model_id: dyang415/nohto-v0-10e

wandb_project: nohto
wandb_name: nohto-v0
wandb_log_model: end

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 10
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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
eval_steps: 0.2
save_steps: 0.1
eval_max_new_tokens: 128
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:

```

</details><br>

# nohto-v0-10e

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8229

## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7166        | 0.18  | 1    | 3.7658          |
| 0.5158        | 1.64  | 10   | 0.5278          |
| 0.2492        | 3.09  | 20   | 0.5739          |
| 0.0338        | 4.73  | 30   | 0.7476          |
| 0.0083        | 6.36  | 40   | 0.8089          |
| 0.0078        | 8.0   | 50   | 0.8229          |


### Framework versions

- PEFT 0.7.0
- Transformers 4.37.0
- Pytorch 2.0.1+cu117
- Datasets 2.17.1
- Tokenizers 0.15.0