Instructions to use Alignment-Lab-AI/GPT-2-optim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alignment-Lab-AI/GPT-2-optim with Transformers:

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

pipe = pipeline("text-generation", model="Alignment-Lab-AI/GPT-2-optim")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alignment-Lab-AI/GPT-2-optim")
model = AutoModelForCausalLM.from_pretrained("Alignment-Lab-AI/GPT-2-optim")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Alignment-Lab-AI/GPT-2-optim with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alignment-Lab-AI/GPT-2-optim"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alignment-Lab-AI/GPT-2-optim",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Alignment-Lab-AI/GPT-2-optim

SGLang

How to use Alignment-Lab-AI/GPT-2-optim 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 "Alignment-Lab-AI/GPT-2-optim" \
    --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": "Alignment-Lab-AI/GPT-2-optim",
		"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 "Alignment-Lab-AI/GPT-2-optim" \
        --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": "Alignment-Lab-AI/GPT-2-optim",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Alignment-Lab-AI/GPT-2-optim with Docker Model Runner:
```
docker model run hf.co/Alignment-Lab-AI/GPT-2-optim
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for gpt2-124M-edu-fineweb-10B

A 124M parameter GPT2 model trained with the 10B fineweb-edu dataset using https://github.com/karpathy/llm.c

Training took 20 hours on a single 4090 GPU (limited to 350W), giving the following graphs:

Training

The training parameters where:

./train_gpt2cu \
    -i "dev/data/edu_fineweb10B/edu_fineweb_train_*.bin" \
    -j "dev/data/edu_fineweb10B/edu_fineweb_val_*.bin" \
    -o log124M \
    -e "d12" \
    -b 56 -t 1024 \
    -d 458752 \
    -r 1 \
    -z 1 \
    -c 0.1 \
    -l 0.002 \
    -q 0.0 \
    -u 700 \
    -n 5000 \
    -v 250 -s 20000 \
    -h 1

The model has had no further finetuning.

Evaluation

Evals using Eleuther AI Harness as described in the open_llm_leaderboard and comparing with those published for openai-community/gpt2

Eval Test	Score
arc_challenge (25 shot)	24.49
gsm8k (5 shot)	0.08
hellaswag (10 shot)	32.64
mmlu (5 shot)	26.06
truthfulqa (0 shot)	42.45
winogrande (5 shot)	52.17
Overall Score	29.65

Downloads last month: 6

Safetensors

Model size

0.1B params

Tensor type

F16

Model tree for Alignment-Lab-AI/GPT-2-optim

Quantizations

1 model

Duplicated from rhysjones/gpt2-124M-edu-fineweb-10B

Alignment-Lab-AI
/

GPT-2-optim

Model Card for gpt2-124M-edu-fineweb-10B

Training

Evaluation

Model tree for Alignment-Lab-AI/GPT-2-optim

Dataset used to train Alignment-Lab-AI/GPT-2-optim