Instructions to use evalstate/tiny-gpt-memorization-0p5m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use evalstate/tiny-gpt-memorization-0p5m with Transformers:

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

pipe = pipeline("text-generation", model="evalstate/tiny-gpt-memorization-0p5m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("evalstate/tiny-gpt-memorization-0p5m")
model = AutoModelForCausalLM.from_pretrained("evalstate/tiny-gpt-memorization-0p5m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use evalstate/tiny-gpt-memorization-0p5m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "evalstate/tiny-gpt-memorization-0p5m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "evalstate/tiny-gpt-memorization-0p5m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/evalstate/tiny-gpt-memorization-0p5m

SGLang

How to use evalstate/tiny-gpt-memorization-0p5m 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 "evalstate/tiny-gpt-memorization-0p5m" \
    --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": "evalstate/tiny-gpt-memorization-0p5m",
		"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 "evalstate/tiny-gpt-memorization-0p5m" \
        --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": "evalstate/tiny-gpt-memorization-0p5m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use evalstate/tiny-gpt-memorization-0p5m with Docker Model Runner:
```
docker model run hf.co/evalstate/tiny-gpt-memorization-0p5m
```

tiny-gpt-memorization-0p5m / README.md

evalstate HF Staff

upload 0p5m near-capacity checkpoint

0234e94 verified 22 days ago

preview code

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metadata

language: en
library_name: transformers
tags:
  - memorization
  - capacity
  - gpt2
  - random-data
license: mit

Tiny GPT memorization checkpoint (0p5m, near-capacity / saturation boundary)

From an exploratory tiny-scale replication of How much do language models memorize?.

Architecture: GPT-2 (transformers), trained from scratch.
Parameters: 481,840 (non-embedding 311,520).
Vocab: 2048 data tokens (uniform random) + BOS = 2049 model vocab.
Sequence length: 64 (paper S=64).
Dataset: 2500 sequences, 160,000 data tokens, dataset entropy 1,760,000 bits (1.760 Mbits).
Trained 7463 steps, AdamW, bfloat16, lr 0.002, batch 1024.
Result: train loss 4.0047 bits/tok, held loss 16.8499, memorized 1,119,247 bits = 2.323 bits/parameter.

This is the near-capacity (saturation-boundary) run for this model size. Below- and above-capacity checkpoints for the same architecture are published as state.pt files in the results dataset evalstate/tiny-memorization-results.

Load with:

from transformers import GPT2LMHeadModel
model = GPT2LMHeadModel.from_pretrained("evalstate/tiny-gpt-memorization-0p5m")

Findings are scoped as an exploratory tiny-scale check (three architectures), NOT a universal scaling law.