Instructions to use pszemraj/mega-ar-small-4096-wikitext-103-raw-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/mega-ar-small-4096-wikitext-103-raw-v1 with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/mega-ar-small-4096-wikitext-103-raw-v1")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("pszemraj/mega-ar-small-4096-wikitext-103-raw-v1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pszemraj/mega-ar-small-4096-wikitext-103-raw-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/mega-ar-small-4096-wikitext-103-raw-v1"
# 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-small-4096-wikitext-103-raw-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/mega-ar-small-4096-wikitext-103-raw-v1

SGLang

How to use pszemraj/mega-ar-small-4096-wikitext-103-raw-v1 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-small-4096-wikitext-103-raw-v1" \
    --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-small-4096-wikitext-103-raw-v1",
		"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-small-4096-wikitext-103-raw-v1" \
        --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-small-4096-wikitext-103-raw-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/mega-ar-small-4096-wikitext-103-raw-v1 with Docker Model Runner:
```
docker model run hf.co/pszemraj/mega-ar-small-4096-wikitext-103-raw-v1
```

mega-ar-small-4096: MWE

mega-ar on wikitext-103-raw-v1 (document level)

This model is a fine-tuned version of pszemraj/random-mega-ar-small-4096 on the EleutherAI/wikitext_document_level dataset (wikitext-103-raw-v1). This model has ~ 65M params.

It achieves the following results on the evaluation set:

Loss: 4.0338
Accuracy: 0.3243

Training procedure

This was tuned with bf16, while the authors recommend tuning with fp32. Will try fp32 later.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 4
eval_batch_size: 1
seed: 80085
gradient_accumulation_steps: 32
total_train_batch_size: 128
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
7.3662	0.11	100	7.2782	0.0935
6.3064	0.22	200	6.2066	0.1634
5.8203	0.33	300	5.7299	0.1931
5.55	0.44	400	5.4173	0.2117
5.3194	0.55	500	5.1937	0.2278
5.1678	0.66	600	5.0206	0.2406
5.0375	0.77	700	4.8891	0.2508
4.9194	0.88	800	4.7592	0.2605
4.8272	0.99	900	4.6653	0.2681
4.7571	1.1	1000	4.5817	0.2754
4.6345	1.21	1100	4.5066	0.2820
4.6218	1.32	1200	4.4472	0.2867
4.5585	1.43	1300	4.3827	0.2923
4.5047	1.54	1400	4.3328	0.2963
4.4726	1.65	1500	4.2860	0.3002
4.4094	1.76	1600	4.2452	0.3038
4.3705	1.87	1700	4.2168	0.3062
4.3739	1.98	1800	4.1852	0.3095
4.2836	2.09	1900	4.1599	0.3112
4.302	2.2	2000	4.1307	0.3149
4.2847	2.31	2100	4.1113	0.3165
4.2348	2.42	2200	4.0925	0.3184
4.2837	2.53	2300	4.0743	0.3207
4.2058	2.64	2400	4.0612	0.3217
4.22	2.75	2500	4.0494	0.3224
4.1827	2.86	2600	4.0397	0.3237
4.1967	2.97	2700	4.0338	0.3243

Framework versions

Transformers 4.32.1
Pytorch 2.1.0.dev20230727+cu118
Datasets 2.13.1
Tokenizers 0.13.3

Downloads last month: 8

Safetensors

Model size

64.8M params

Tensor type

F32

pszemraj
/

mega-ar-small-4096-wikitext-103-raw-v1