---
license: mit
base_model: argilla/notus-7b-v1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: notus-casino-reviews
  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: argilla/notus-7b-v1
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

hub_model_id: AlekseyKorshuk/notus-casino-reviews
hub_strategy: every_save

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: AlekseyKorshuk/casino-reviews
    type: sharegpt
    conversation: zephyr

dataset_prepared_path:
val_set_size: 0.002
output_dir: ./output

sequence_len: 2048
sample_packing: false
pad_to_sequence_len:

lora_r:
lora_alpha:
lora_dropout:
lora_target_modules:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: casino-reviews
wandb_entity:
wandb_watch:
wandb_name: notus-7b-v1
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 1
optimizer: paged_adamw_8bit
adam_beta1: 0.9
adam_beta2: 0.95
max_grad_norm: 1.0
adam_epsilon: 0.00001
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
learning_rate: 2e-5
warmup_ratio: 0.1
weight_decay: 0.01

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true
#float16: false
#bloat16: true

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


evals_per_epoch: 2
eval_table_size: 8 # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
eval_table_max_new_tokens: 768 # Total number of tokens generated for predictions sent to wandb. Default is 128
eval_sample_packing: false

saves_per_epoch: 1
save_total_limit: 1
seed: 42
debug:
deepspeed:

fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true



```

</details><br>

# notus-casino-reviews

This model is a fine-tuned version of [argilla/notus-7b-v1](https://huggingface.co/argilla/notus-7b-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1794

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2587        | 0.0   | 1    | 3.2501          |
| 1.1679        | 0.5   | 214  | 1.1794          |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0