---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- axolotl
- generated_from_trainer
datasets:
- pretraining.jsonl
- AiAF/SCPWiki-Archive-02-March-2025-Datasets
model-index:
- name: Pretrained-SCP-1B-QLoRA
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.8.0.dev0`
```yaml
base_model: meta-llama/Llama-3.2-1B-Instruct
# Automatically upload checkpoint and final model to HF
hub_model_id: AiAF/Pretrained-SCP-1B-QLoRA
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: pretraining.jsonl
type: completion
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/qlora-out/Pretrained-SCP-1B-QLoRA
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project: "LLM-Pretraining"
wandb_entity:
wandb_watch: "all"
wandb_name: "Pretrained-SCP-7B-Instruct"
wandb_log_model: "false"
gradient_accumulation_steps: 3
micro_batch_size: 10
num_epochs: 1
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: 50
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 10
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: "<|end_of_text|>"
```
# Pretrained-SCP-1B-QLoRA
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the pretraining.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2062
## 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: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 30
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.0841 | 0.0020 | 1 | 3.0001 |
| 2.8276 | 0.0215 | 11 | 2.8296 |
| 2.3977 | 0.0431 | 22 | 2.5255 |
| 2.2856 | 0.0646 | 33 | 2.4384 |
| 2.3735 | 0.0862 | 44 | 2.4082 |
| 2.3645 | 0.1077 | 55 | 2.3861 |
| 2.1425 | 0.1292 | 66 | 2.3694 |
| 2.1541 | 0.1508 | 77 | 2.3545 |
| 2.2848 | 0.1723 | 88 | 2.3410 |
| 2.2334 | 0.1939 | 99 | 2.3310 |
| 2.1278 | 0.2154 | 110 | 2.3213 |
| 2.159 | 0.2369 | 121 | 2.3112 |
| 2.1407 | 0.2585 | 132 | 2.3006 |
| 1.9851 | 0.2800 | 143 | 2.2915 |
| 2.0319 | 0.3016 | 154 | 2.2839 |
| 2.2373 | 0.3231 | 165 | 2.2755 |
| 2.1488 | 0.3446 | 176 | 2.2684 |
| 2.0218 | 0.3662 | 187 | 2.2612 |
| 1.9256 | 0.3877 | 198 | 2.2552 |
| 2.0179 | 0.4093 | 209 | 2.2486 |
| 2.0768 | 0.4308 | 220 | 2.2448 |
| 2.1068 | 0.4523 | 231 | 2.2408 |
| 2.1343 | 0.4739 | 242 | 2.2356 |
| 2.2212 | 0.4954 | 253 | 2.2342 |
| 2.0442 | 0.5170 | 264 | 2.2302 |
| 2.0805 | 0.5385 | 275 | 2.2256 |
| 1.9695 | 0.5601 | 286 | 2.2230 |
| 1.8559 | 0.5816 | 297 | 2.2206 |
| 2.0997 | 0.6031 | 308 | 2.2185 |
| 2.0168 | 0.6247 | 319 | 2.2164 |
| 1.9304 | 0.6462 | 330 | 2.2148 |
| 1.9313 | 0.6678 | 341 | 2.2132 |
| 2.1708 | 0.6893 | 352 | 2.2119 |
| 2.0596 | 0.7108 | 363 | 2.2109 |
| 2.1944 | 0.7324 | 374 | 2.2099 |
| 2.0098 | 0.7539 | 385 | 2.2094 |
| 2.0344 | 0.7755 | 396 | 2.2087 |
| 2.1658 | 0.7970 | 407 | 2.2080 |
| 2.1188 | 0.8185 | 418 | 2.2078 |
| 1.879 | 0.8401 | 429 | 2.2072 |
| 1.9652 | 0.8616 | 440 | 2.2068 |
| 2.0429 | 0.8832 | 451 | 2.2066 |
| 2.3038 | 0.9047 | 462 | 2.2064 |
| 2.153 | 0.9262 | 473 | 2.2063 |
| 2.0543 | 0.9478 | 484 | 2.2062 |
| 2.0093 | 0.9693 | 495 | 2.2062 |
| 2.2437 | 0.9909 | 506 | 2.2062 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0