---
library_name: peft
tags:
- generated_from_trainer
base_model: intervitens/internlm2-base-20b-llama
model-index:
- name: internlm-limarp-lora
  results: []
---

Don't use this yet, there's a problem with the llamafied internlm2 tokenizer.
Prompt format: ChatML.

[<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: `0.3.0`
```yaml
base_model: /data/internlm2-base-20b-llama
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: /data/train-all-8k.jsonl
    type: completion
dataset_prepared_path:
val_set_size: 0.05
output_dir: /data/internlm-limarp-lora-out

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 128
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00002

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

gradient_checkpointing: true
gradient_checkpointing_kwargs: 
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```

</details><br>

# internlm-limarp-lora

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1216

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3563        | 0.01  | 1    | 2.3995          |
| 2.1815        | 0.25  | 37   | 2.2693          |
| 2.1364        | 0.51  | 74   | 2.1684          |
| 2.1355        | 0.76  | 111  | 2.1526          |
| 2.1624        | 1.03  | 148  | 2.1435          |
| 2.1326        | 1.28  | 185  | 2.1367          |
| 1.9987        | 1.54  | 222  | 2.1330          |
| 2.0494        | 1.79  | 259  | 2.1291          |
| 2.0505        | 2.04  | 296  | 2.1266          |
| 2.075         | 2.3   | 333  | 2.1243          |
| 2.0183        | 2.55  | 370  | 2.1229          |
| 2.1047        | 2.81  | 407  | 2.1227          |
| 2.1309        | 3.06  | 444  | 2.1218          |
| 2.1249        | 3.31  | 481  | 2.1214          |
| 2.1423        | 3.57  | 518  | 2.1214          |
| 2.0913        | 3.82  | 555  | 2.1216          |


### Framework versions

- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0