---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
model-index:
- name: financial-phrasebank-sentiment-reasoning
  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: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

chat_template: chatml
datasets:
  - path: winglian/financial_phrasebank_augmented
    type: sharegpt
    split: train
    strict: false
test_datasets:
  - path: winglian/financial_phrasebank_augmented-validation
    type: sharegpt
    split: train
    strict: false
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./finetuned-out
hub_model_id: winglian/financial-phrasebank-sentiment-reasoning

adapter: lora
lora_model_dir:

sequence_len: 768 
sample_packing: false
pad_to_sequence_len: false

lora_r: 32
lora_alpha: 16
lora_dropout: 0.1
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: financial-phrasebank-reasoning
wandb_entity: oaaic
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00001

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

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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

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:
  eos_token: "<|im_end|>"
tokens:
  - "<|im_start|>"

```

</details><br>

# financial-phrasebank-sentiment-reasoning

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: 0.6457

## 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
- gradient_accumulation_steps: 4
- total_train_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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1125        | 0.0   | 1    | 1.3634          |
| 0.4981        | 0.25  | 71   | 0.7488          |
| 0.3236        | 0.5   | 142  | 0.6989          |
| 0.2667        | 0.76  | 213  | 0.6727          |
| 0.2922        | 1.01  | 284  | 0.6553          |
| 0.2897        | 1.26  | 355  | 0.6526          |
| 0.3039        | 1.51  | 426  | 0.6491          |
| 0.2948        | 1.77  | 497  | 0.6462          |
| 0.2858        | 2.02  | 568  | 0.6440          |
| 0.2795        | 2.27  | 639  | 0.6448          |
| 0.1904        | 2.52  | 710  | 0.6463          |
| 0.2829        | 2.77  | 781  | 0.6457          |


### Framework versions

- PEFT 0.9.1.dev0
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0