RichardErkhov/pszemraj_-_pythia-31m-simplewiki-2048-4bits

Quantization made by Richard Erkhov.

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pythia-31m-simplewiki-2048 - bnb 4bits

• Model creator: https://huggingface.co/pszemraj/
• Original model: https://huggingface.co/pszemraj/pythia-31m-simplewiki-2048/

Original model description:

tags: - generated_from_trainer metrics: - accuracy inference: parameters: max_new_tokens: 64 do_sample: true repetition_penalty: 1.1 no_repeat_ngram_size: 5 guidance_scale: 1.01 eta_cutoff: 0.001 widget: - text: My name is El Microondas the Wise and example_title: El Microondas - text: A meme is example_title: meme - text: >- Barack Obama nominated Hilary Clinton as his secretary of state on Monday. He chose her because she had example_title: Coreference resolution - text: >- On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book example_title: Logic puzzles - text: >- The two men running to become New York City's next mayor will face off in their first debate Wednesday night example_title: Reading comprehension datasets: - pszemraj/simple_wikipedia_LM pipeline_tag: text-generation license: apache-2.0

pythia-31m-simplewiki-2048

This was initialized from random weights based on the config of EleutherAI/pythia-31m and trained on pszemraj/simple_wikipedia_LM for 3 epochs.

It achieves the following results on the evaluation set:

• Loss: 3.6874
• Accuracy: 0.4105

Model description

More information needed

Intended uses & limitations

This is a baseline for comparison to other models.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0005
• train_batch_size: 1
• eval_batch_size: 1
• seed: 80085
• gradient_accumulation_steps: 64
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-07
• lr_scheduler_type: inverse_sqrt
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
6.0657	0.22	100	5.6210	0.2414
5.2447	0.45	200	4.9316	0.3054
4.8397	0.67	300	4.6011	0.3343
4.7933	0.9	400	4.3878	0.3530
4.274	1.12	500	4.2352	0.3646
4.4867	1.35	600	4.1224	0.3723
4.3434	1.57	700	4.0282	0.3791
4.1857	1.8	800	3.9552	0.3841
4.229	2.02	900	3.8890	0.3909
3.9189	2.25	1000	3.8301	0.3967
4.084	2.47	1100	3.7782	0.4023
3.8965	2.7	1200	3.7302	0.4069
3.915	2.92	1300	3.6874	0.4105

Framework versions

• Transformers 4.33.1
• Pytorch 2.2.0.dev20230907+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	24.35
ARC (25-shot)	22.18
HellaSwag (10-shot)	25.55
MMLU (5-shot)	23.12
TruthfulQA (0-shot)	49.37
Winogrande (5-shot)	49.41
GSM8K (5-shot)	0.0
DROP (3-shot)	0.81