Instructions to use rhysjones/gpt2-774M-fineweb-150B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rhysjones/gpt2-774M-fineweb-150B with Transformers:

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

pipe = pipeline("text-generation", model="rhysjones/gpt2-774M-fineweb-150B", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rhysjones/gpt2-774M-fineweb-150B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("rhysjones/gpt2-774M-fineweb-150B", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rhysjones/gpt2-774M-fineweb-150B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rhysjones/gpt2-774M-fineweb-150B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rhysjones/gpt2-774M-fineweb-150B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rhysjones/gpt2-774M-fineweb-150B

SGLang

How to use rhysjones/gpt2-774M-fineweb-150B 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 "rhysjones/gpt2-774M-fineweb-150B" \
    --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": "rhysjones/gpt2-774M-fineweb-150B",
		"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 "rhysjones/gpt2-774M-fineweb-150B" \
        --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": "rhysjones/gpt2-774M-fineweb-150B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rhysjones/gpt2-774M-fineweb-150B with Docker Model Runner:
```
docker model run hf.co/rhysjones/gpt2-774M-fineweb-150B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is karpathy's model from the llm.c project converted to HF format to investigate bfloat16 performance.

The training run was 150B tokens, 1.5 epochs over the 100B FineWeb sample dataset.

There's active work underway at https://github.com/karpathy/llm.c so I'd suggest following the developments there!

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Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for rhysjones/gpt2-774M-fineweb-150B

Quantizations

3 models

rhysjones
/

gpt2-774M-fineweb-150B

Model tree for rhysjones/gpt2-774M-fineweb-150B

Dataset used to train rhysjones/gpt2-774M-fineweb-150B