---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: intervitens/internlm2-limarp-chat-20b
model-index:
- name: outputs/qlora-out
  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
mlflow_tracking_uri: http://127.0.0.1:2340
mlflow_experiment_name: Default

base_model: intervitens/internlm2-limarp-chat-20b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: ResplendentAI/Alpaca_NSFW_Shuffled
    type: alpaca
  - path: diffnamehard/toxic-dpo-v0.1-NoWarning-alpaca
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/qlora-out

adapter: lora
lora_model_dir:

sequence_len: 8192
sample_packing: false
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: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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>

# outputs/qlora-out

This model is a fine-tuned version of [intervitens/internlm2-limarp-chat-20b](https://huggingface.co./intervitens/internlm2-limarp-chat-20b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9868

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 56
- total_eval_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.465         | 0.0476 | 1    | 1.4508          |
| 1.3472        | 0.2857 | 6    | 1.4126          |
| 1.1997        | 0.5714 | 12   | 1.1998          |
| 1.0735        | 0.8571 | 18   | 1.1192          |
| 1.077         | 1.1429 | 24   | 1.0703          |
| 1.0478        | 1.4286 | 30   | 1.0410          |
| 0.9997        | 1.7143 | 36   | 1.0259          |
| 0.9696        | 2.0    | 42   | 1.0091          |
| 0.8861        | 2.2857 | 48   | 1.0042          |
| 0.8961        | 2.5714 | 54   | 0.9928          |
| 0.8615        | 2.8571 | 60   | 0.9889          |
| 0.8603        | 3.1429 | 66   | 0.9860          |
| 0.7825        | 3.4286 | 72   | 0.9877          |
| 0.9228        | 3.7143 | 78   | 0.9860          |
| 0.8684        | 4.0    | 84   | 0.9868          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1