peft safetensors qwen2 generated_from_trainer base_model:sail/Sailor-7B license:apache-2.0 4-bit bitsandbytes region:us
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: sail/Sailor-7B
model-index:
- name: Sailor-7B-toba
  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: sail/Sailor-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_mistral_derived_model: false

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
#we used a small dataset to teach the model function calling abilities
  - path: ./echonettobatrain.jsonl
    ds_type: json
    type: sharegpt

dataset_prepared_path: last_run_function_call
#0.05
val_set_size: 0.02
output_dir: ./Sailor-7B-toba

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: false
eval_sample_packing: true
pad_to_sequence_len: true

# important, to get the same trainable parameters then for a qlora training with lora_r=32 and lora_alpha=16 you need to adjust the lora_r depending on the amount of filtered layers you want to use. With top_n=4 you would go for lora_r= 256

lora_r: 64
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: false
lora_fan_in_fan_out:
lora_target_modules:
- layers.0.self_attn.v_proj
- layers.1.self_attn.v_proj
- layers.2.self_attn.v_proj
- layers.3.self_attn.v_proj
- layers.4.self_attn.v_proj
- layers.5.self_attn.v_proj
- layers.6.self_attn.v_proj
- layers.7.self_attn.v_proj
- layers.8.self_attn.v_proj
- layers.9.self_attn.v_proj
- layers.10.self_attn.v_proj
- layers.11.self_attn.v_proj
- layers.12.self_attn.v_proj
- layers.13.self_attn.v_proj
- layers.14.self_attn.v_proj
- layers.15.self_attn.v_proj
- layers.16.self_attn.v_proj
- layers.17.self_attn.v_proj
- layers.18.self_attn.v_proj
- layers.19.self_attn.v_proj
- layers.20.self_attn.v_proj
- layers.21.self_attn.v_proj
- layers.22.self_attn.v_proj
- layers.23.self_attn.v_proj
- layers.24.self_attn.v_proj
- layers.25.self_attn.v_proj
- layers.26.self_attn.v_proj
- layers.27.self_attn.v_proj
- layers.28.self_attn.v_proj
- layers.29.self_attn.v_proj
- layers.30.self_attn.v_proj
- layers.31.self_attn.v_proj
- layers.0.self_attn.k_proj
- layers.1.self_attn.k_proj
- layers.2.self_attn.k_proj
- layers.3.self_attn.k_proj
- layers.4.self_attn.k_proj
- layers.5.self_attn.k_proj
- layers.6.self_attn.k_proj
- layers.7.self_attn.k_proj
- layers.8.self_attn.k_proj
- layers.9.self_attn.k_proj
- layers.10.self_attn.k_proj
- layers.11.self_attn.k_proj
- layers.12.self_attn.k_proj
- layers.13.self_attn.k_proj
- layers.14.self_attn.k_proj
- layers.15.self_attn.k_proj
- layers.16.self_attn.k_proj
- layers.17.self_attn.k_proj
- layers.18.self_attn.k_proj
- layers.19.self_attn.k_proj
- layers.20.self_attn.k_proj
- layers.21.self_attn.k_proj
- layers.22.self_attn.k_proj
- layers.23.self_attn.k_proj
- layers.24.self_attn.k_proj
- layers.25.self_attn.k_proj
- layers.26.self_attn.k_proj
- layers.27.self_attn.k_proj
- layers.28.self_attn.k_proj
- layers.29.self_attn.k_proj
- layers.30.self_attn.k_proj
- layers.31.self_attn.k_proj
- layers.0.self_attn.o_proj
- layers.1.self_attn.o_proj
- layers.2.self_attn.o_proj
- layers.3.self_attn.o_proj
- layers.4.self_attn.o_proj
- layers.5.self_attn.o_proj
- layers.6.self_attn.o_proj
- layers.7.self_attn.o_proj
- layers.8.self_attn.o_proj
- layers.9.self_attn.o_proj
- layers.10.self_attn.o_proj
- layers.11.self_attn.o_proj
- layers.12.self_attn.o_proj
- layers.13.self_attn.o_proj
- layers.14.self_attn.o_proj
- layers.15.self_attn.o_proj
- layers.16.self_attn.o_proj
- layers.17.self_attn.o_proj
- layers.18.self_attn.o_proj
- layers.19.self_attn.o_proj
- layers.20.self_attn.o_proj
- layers.21.self_attn.o_proj
- layers.22.self_attn.o_proj
- layers.23.self_attn.o_proj
- layers.24.self_attn.o_proj
- layers.25.self_attn.o_proj
- layers.26.self_attn.o_proj
- layers.27.self_attn.o_proj
- layers.28.self_attn.o_proj
- layers.29.self_attn.o_proj
- layers.30.self_attn.o_proj
- layers.31.self_attn.o_proj
- layers.0.self_attn.q_proj
- layers.1.self_attn.q_proj
- layers.2.self_attn.q_proj
- layers.3.self_attn.q_proj
- layers.4.self_attn.q_proj
- layers.5.self_attn.q_proj
- layers.6.self_attn.q_proj
- layers.7.self_attn.q_proj
- layers.8.self_attn.q_proj
- layers.9.self_attn.q_proj
- layers.10.self_attn.q_proj
- layers.11.self_attn.q_proj
- layers.12.self_attn.q_proj
- layers.13.self_attn.q_proj
- layers.14.self_attn.q_proj
- layers.15.self_attn.q_proj
- layers.16.self_attn.q_proj
- layers.17.self_attn.q_proj
- layers.18.self_attn.q_proj
- layers.19.self_attn.q_proj
- layers.20.self_attn.q_proj
- layers.21.self_attn.q_proj
- layers.22.self_attn.q_proj
- layers.23.self_attn.q_proj
- layers.24.self_attn.q_proj
- layers.25.self_attn.q_proj
- layers.26.self_attn.q_proj
- layers.27.self_attn.q_proj
- layers.28.self_attn.q_proj
- layers.29.self_attn.q_proj
- layers.30.self_attn.q_proj
- layers.31.self_attn.q_proj
- layers.0.mlp.down_proj
- layers.1.mlp.down_proj
- layers.2.mlp.down_proj
- layers.3.mlp.down_proj
- layers.4.mlp.down_proj
- layers.5.mlp.down_proj
- layers.6.mlp.down_proj
- layers.7.mlp.down_proj
- layers.8.mlp.down_proj
- layers.9.mlp.down_proj
- layers.10.mlp.down_proj
- layers.11.mlp.down_proj
- layers.12.mlp.down_proj
- layers.13.mlp.down_proj
- layers.14.mlp.down_proj
- layers.15.mlp.down_proj
- layers.16.mlp.down_proj
- layers.17.mlp.down_proj
- layers.18.mlp.down_proj
- layers.19.mlp.down_proj
- layers.20.mlp.down_proj
- layers.21.mlp.down_proj
- layers.22.mlp.down_proj
- layers.23.mlp.down_proj
- layers.24.mlp.down_proj
- layers.25.mlp.down_proj
- layers.26.mlp.down_proj
- layers.27.mlp.down_proj
- layers.28.mlp.down_proj
- layers.29.mlp.down_proj
- layers.30.mlp.down_proj
- layers.31.mlp.down_proj
- layers.0.mlp.up_proj
- layers.1.mlp.up_proj
- layers.2.mlp.up_proj
- layers.3.mlp.up_proj
- layers.4.mlp.up_proj
- layers.5.mlp.up_proj
- layers.6.mlp.up_proj
- layers.7.mlp.up_proj
- layers.8.mlp.up_proj
- layers.9.mlp.up_proj
- layers.10.mlp.up_proj
- layers.11.mlp.up_proj
- layers.12.mlp.up_proj
- layers.13.mlp.up_proj
- layers.14.mlp.up_proj
- layers.15.mlp.up_proj
- layers.16.mlp.up_proj
- layers.17.mlp.up_proj
- layers.18.mlp.up_proj
- layers.19.mlp.up_proj
- layers.20.mlp.up_proj
- layers.21.mlp.up_proj
- layers.22.mlp.up_proj
- layers.23.mlp.up_proj
- layers.24.mlp.up_proj
- layers.25.mlp.up_proj
- layers.26.mlp.up_proj
- layers.27.mlp.up_proj
- layers.28.mlp.up_proj
- layers.29.mlp.up_proj
- layers.30.mlp.up_proj
- layers.31.mlp.up_proj
  # important: you need to unfreeze the lm.head
- lm.head

wandb_project: axolotl-sailor7b-toba
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00025

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

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

warmup_steps: 100
eval_steps: 0.2
eval_table_size:
eval_table_max_new_tokens: 128
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```

</details><br>

# Sailor-7B-toba

This model is a fine-tuned version of [sail/Sailor-7B](https://huggingface.co/sail/Sailor-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3876

## 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: 0.00025
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.0998        | 0.0   | 1    | 5.1501          |
| 1.3477        | 0.6   | 622  | 1.6304          |
| 1.268         | 1.2   | 1244 | 1.4755          |
| 0.8714        | 1.8   | 1866 | 1.2799          |
| 0.4408        | 2.4   | 2488 | 1.3876          |


### 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