Instructions to use pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e")
model = AutoModelForMultimodalLM.from_pretrained("pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e

SGLang

How to use pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e with Docker Model Runner:
```
docker model run hf.co/pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BL-pythia-31m-simplepile-lite-2048-scratch

Train from scratch based on config of EleutherAI/pythia-31m on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.9891
Accuracy: 0.3498

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: 0.0005
train_batch_size: 2
eval_batch_size: 1
seed: 80085
gradient_accumulation_steps: 64
total_train_batch_size: 128
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: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
7.4089	0.07	100	7.3885	0.1133
6.2774	0.13	200	6.2091	0.1621
5.7019	0.2	300	5.7450	0.1890
5.4922	0.27	400	5.4697	0.2080
5.233	0.33	500	5.2846	0.2195
5.0523	0.4	600	5.1479	0.2296
4.9396	0.47	700	5.0391	0.2376
4.7633	0.53	800	4.9366	0.2458
4.7516	0.6	900	4.8339	0.2559
4.5937	0.67	1000	4.7286	0.2676
4.5079	0.73	1100	4.6293	0.2798
4.4608	0.8	1200	4.5433	0.2903
4.3426	0.87	1300	4.4719	0.2988
4.1722	0.93	1400	4.4089	0.3057
4.1655	1.0	1500	4.3585	0.3107
4.0927	1.07	1600	4.3101	0.3161
4.1439	1.13	1700	4.2714	0.3206
4.0064	1.2	1800	4.2330	0.3249
4.0633	1.27	1900	4.2015	0.3281
3.9948	1.33	2000	4.1702	0.3311
3.9389	1.4	2100	4.1439	0.3338
3.8833	1.47	2200	4.1200	0.3367
3.8411	1.53	2300	4.0949	0.3395
3.8481	1.6	2400	4.0764	0.3408
3.8397	1.67	2500	4.0578	0.3420
3.8897	1.73	2600	4.0383	0.3440
3.8785	1.8	2700	4.0206	0.3459
3.8126	1.87	2800	4.0044	0.3478
3.783	1.93	2900	3.9891	0.3498

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.	24.7
ARC (25-shot)	21.59
HellaSwag (10-shot)	25.79
MMLU (5-shot)	24.99
TruthfulQA (0-shot)	50.62
Winogrande (5-shot)	48.62
GSM8K (5-shot)	0.0
DROP (3-shot)	1.32

Downloads last month: 69

Safetensors

Model size

30.5M params

Tensor type

F32

Model tree for pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e

Quantizations

1 model

pszemraj
/

pythia-31m-simplepile-lite-2048-scratch-2e

BL-pythia-31m-simplepile-lite-2048-scratch

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Open LLM Leaderboard Evaluation Results

Model tree for pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e

Dataset used to train pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e