---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: premai-io/prem-1B
model-index:
- name: prem-1B-32k
  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: premai-io/prem-1B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: argilla/distilabel-capybara-dpo-7k-binarized
    type: orpo.chat_template
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./prem-1B-32k
save_safetensors: true
sequence_len: 8192
sample_packing: false
pad_to_sequence_len: false
use_pose: true
pose_max_context_len: 262144
min_sample_len: 6144
pose_num_chunks: 16
curriculum_sampling: true

overrides_of_model_config:
  rope_theta: 500000.0
  max_position_embeddings: 262144

  # peft_use_dora: true
adapter: lora
peft_use_rslora: true
lora_model_dir:
lora_r: 1024
lora_alpha: 1024
lora_dropout: 0.1
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project:
wandb_entity: 
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 20
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00001
max_grad_norm: 1.0
adam_beta2: 0.95

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

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

warmup_steps: 10
evals_per_epoch: 8
saves_per_epoch: 8
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>



```

</details><br>

# prem-1B-32k

This model is a fine-tuned version of [premai-io/prem-1B](https://huggingface.co/premai-io/prem-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0059

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7672        | 1.0   | 1    | 3.0074          |
| 0.7672        | 2.0   | 2    | 2.6057          |
| 0.7422        | 3.0   | 3    | 2.2898          |
| 0.7211        | 4.0   | 4    | 2.1453          |
| 0.6591        | 5.0   | 5    | 1.6360          |
| 0.4514        | 6.0   | 6    | 0.7589          |
| 0.24          | 7.0   | 7    | 0.6621          |
| 0.1584        | 8.0   | 8    | 0.8121          |
| 0.1235        | 9.0   | 9    | 0.7538          |
| 0.0998        | 10.0  | 10   | 0.7743          |
| 0.0869        | 11.0  | 11   | 0.7771          |
| 0.1692        | 12.0  | 12   | 0.8293          |
| 0.0702        | 13.0  | 13   | 0.8939          |
| 0.063         | 14.0  | 14   | 0.9582          |
| 0.0567        | 15.0  | 15   | 0.9825          |
| 0.052         | 16.0  | 16   | 0.9960          |
| 0.0488        | 17.0  | 17   | 0.9883          |
| 0.0457        | 18.0  | 18   | 1.0004          |
| 0.0436        | 19.0  | 19   | 1.0056          |
| 0.0427        | 20.0  | 20   | 1.0059          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0