Instructions to use pszemraj/mega-ar-large-2048-simplewiki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/mega-ar-large-2048-simplewiki with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/mega-ar-large-2048-simplewiki")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("pszemraj/mega-ar-large-2048-simplewiki", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pszemraj/mega-ar-large-2048-simplewiki with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pszemraj/mega-ar-large-2048-simplewiki

SGLang

How to use pszemraj/mega-ar-large-2048-simplewiki 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/mega-ar-large-2048-simplewiki" \
    --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/mega-ar-large-2048-simplewiki",
		"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/mega-ar-large-2048-simplewiki" \
        --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/mega-ar-large-2048-simplewiki",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/mega-ar-large-2048-simplewiki with Docker Model Runner:
```
docker model run hf.co/pszemraj/mega-ar-large-2048-simplewiki
```

mega-ar-large-2048-simplewiki

This is a 'large' size autoregressive MEGA model initialized from random weights and trained on pszemraj/simple_wikipedia_LM for three epochs.

It achieves the following results on the evaluation set:

Loss: 3.3412
Accuracy: 0.4360

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: 1
eval_batch_size: 1
seed: 80085
gradient_accumulation_steps: 32
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.05
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
7.2245	0.11	100	6.9372	0.0711
6.6575	0.22	200	6.2335	0.1853
5.9406	0.34	300	5.3724	0.2635
5.4452	0.45	400	4.9243	0.2940
5.2524	0.56	500	4.6568	0.3172
4.7862	0.67	600	4.4488	0.3347
4.7132	0.79	700	4.2699	0.3481
4.6601	0.9	800	4.1502	0.3582
4.5067	1.01	900	4.0461	0.3681
4.4465	1.12	1000	3.9488	0.3773
4.4493	1.24	1100	3.8681	0.3833
4.3136	1.35	1200	3.8039	0.3897
4.2978	1.46	1300	3.7373	0.3956
4.0475	1.57	1400	3.6874	0.4003
4.1328	1.68	1500	3.6339	0.4061
4.0758	1.8	1600	3.5866	0.4115
3.8489	1.91	1700	3.5438	0.4163
3.913	2.02	1800	3.5136	0.4192
3.7746	2.13	1900	3.4860	0.4226
3.9547	2.25	2000	3.4505	0.4255
3.9726	2.36	2100	3.4283	0.4269
3.7546	2.47	2200	3.3999	0.4298
3.7442	2.58	2300	3.3820	0.4317
3.6848	2.7	2400	3.3687	0.4333
3.5491	2.81	2500	3.3531	0.4349
3.9563	2.92	2600	3.3412	0.4360

Framework versions

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

Downloads last month: 9

Safetensors

Model size

0.2B params

Tensor type

F32

pszemraj
/

mega-ar-large-2048-simplewiki

mega-ar-large-2048-simplewiki

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train pszemraj/mega-ar-large-2048-simplewiki