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---
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