AiAF / Pretrained-Adapter-SCP-1B-QLoRA

huggingface.co
Total runs: 15
24-hour runs: 1
7-day runs: 3
30-day runs: 10
Model's Last Updated: March 05 2025

Introduction of Pretrained-Adapter-SCP-1B-QLoRA

Model Details of Pretrained-Adapter-SCP-1B-QLoRA

Built with Axolotl

See axolotl config

axolotl version: 0.8.0.dev0

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 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

Runs of AiAF Pretrained-Adapter-SCP-1B-QLoRA on huggingface.co

15
Total runs
1
24-hour runs
2
3-day runs
3
7-day runs
10
30-day runs

More Information About Pretrained-Adapter-SCP-1B-QLoRA huggingface.co Model

More Pretrained-Adapter-SCP-1B-QLoRA license Visit here:

https://choosealicense.com/licenses/llama3.2

Pretrained-Adapter-SCP-1B-QLoRA huggingface.co

Pretrained-Adapter-SCP-1B-QLoRA huggingface.co is an AI model on huggingface.co that provides Pretrained-Adapter-SCP-1B-QLoRA's model effect (), which can be used instantly with this AiAF Pretrained-Adapter-SCP-1B-QLoRA model. huggingface.co supports a free trial of the Pretrained-Adapter-SCP-1B-QLoRA model, and also provides paid use of the Pretrained-Adapter-SCP-1B-QLoRA. Support call Pretrained-Adapter-SCP-1B-QLoRA model through api, including Node.js, Python, http.

Pretrained-Adapter-SCP-1B-QLoRA huggingface.co Url

https://huggingface.co/AiAF/Pretrained-Adapter-SCP-1B-QLoRA

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AiAF Pretrained-Adapter-SCP-1B-QLoRA online free url in huggingface.co:

https://huggingface.co/AiAF/Pretrained-Adapter-SCP-1B-QLoRA

Pretrained-Adapter-SCP-1B-QLoRA install

Pretrained-Adapter-SCP-1B-QLoRA is an open source model from GitHub that offers a free installation service, and any user can find Pretrained-Adapter-SCP-1B-QLoRA on GitHub to install. At the same time, huggingface.co provides the effect of Pretrained-Adapter-SCP-1B-QLoRA install, users can directly use Pretrained-Adapter-SCP-1B-QLoRA installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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Pretrained-Adapter-SCP-1B-QLoRA huggingface.co Url

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