---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: home/yujia/home/CN_Hateful/trained_models/mistral/CN/toxi/1e-5/
  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-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  # - path: mhenrichsen/alpaca_2k_test
  # - path: /home/yujia/home/CN_Hateful/train_toxiCN.json
  - path: /home/yujia/home/CN_Hateful/train_toxiCN_cn.json
  # - path: /home/yujia/home/CN_Hateful/train.json
    ds_type: json
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
# output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/toxi/1e-5/
output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/CN/toxi/1e-5/
# output_dir: /home/yujia/home/CN_Hateful/trained_models/mistral/cold/3e-5/



adapter: lora
lora_model_dir:

sequence_len: 256
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
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: false

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:

```

</details><br>

# home/yujia/home/CN_Hateful/trained_models/mistral/CN/toxi/1e-5/

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0627

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5188        | 0.01  | 1    | 2.5282          |
| 1.0047        | 0.25  | 17   | 0.8628          |
| 0.086         | 0.51  | 34   | 0.0862          |
| 0.0732        | 0.76  | 51   | 0.0753          |
| 0.0719        | 1.02  | 68   | 0.0753          |
| 0.0722        | 1.25  | 85   | 0.0680          |
| 0.0676        | 1.51  | 102  | 0.0666          |
| 0.068         | 1.76  | 119  | 0.0648          |
| 0.0562        | 2.02  | 136  | 0.0637          |
| 0.0674        | 2.25  | 153  | 0.0628          |
| 0.0611        | 2.51  | 170  | 0.0625          |
| 0.0536        | 2.76  | 187  | 0.0627          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0