Instructions to use Leogrin/eleuther-pythia1b-hh-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Leogrin/eleuther-pythia1b-hh-sft with Transformers:

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

pipe = pipeline("text-generation", model="Leogrin/eleuther-pythia1b-hh-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Leogrin/eleuther-pythia1b-hh-sft")
model = AutoModelForCausalLM.from_pretrained("Leogrin/eleuther-pythia1b-hh-sft")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Leogrin/eleuther-pythia1b-hh-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Leogrin/eleuther-pythia1b-hh-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Leogrin/eleuther-pythia1b-hh-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Leogrin/eleuther-pythia1b-hh-sft

SGLang

How to use Leogrin/eleuther-pythia1b-hh-sft 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 "Leogrin/eleuther-pythia1b-hh-sft" \
    --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": "Leogrin/eleuther-pythia1b-hh-sft",
		"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 "Leogrin/eleuther-pythia1b-hh-sft" \
        --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": "Leogrin/eleuther-pythia1b-hh-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Leogrin/eleuther-pythia1b-hh-sft with Docker Model Runner:
```
docker model run hf.co/Leogrin/eleuther-pythia1b-hh-sft
```

Infos

Pythia-1b supervised finetuned with Anthropic-hh-rlhf dataset for 1 epoch.

wandb log

See Pythia-1b for model details (paper).

Benchmark raw results:

Results for the base model are taken from the Pythia paper.

Zero shot

Task	1B_base	1B_sft
Lambada (OpenAI)	0.562 ± 0.007	0.563 ± 0.007
PIQA	0.707 ± 0.011	0.711 ± 0.011
WinoGrande	0.537 ± 0.014	0.534 ± 0.014
WSC	0.365 ± 0.047	0.365 ± 0.047
ARC - Easy	0.569 ± 0.010	0.583 ± 0.010
ARC - Challenge	0.244 ± 0.013	0.248 ± 0.013
SciQ	0.840 ± 0.012	0.847 ± 0.011
LogiQA	0.223 ± 0.016	--

Five shot

Task	1B_base	1B_sft
Lambada (OpenAI)	0.507 ± 0.007	0.4722 ± 0.007
PIQA	0.705 ± 0.011	0.7165 ± 0.0105
WinoGrande	0.532 ± 0.014	0.5343 ± 0.014
WSC	0.365 ± 0.047	0.5000 ± 0.0493
ARC - Easy	0.594 ± 0.010	0.6010 ± 0.010
ARC - Challenge	0.259 ± 0.013	0.2679 ± 0.0129
SciQ	0.920 ± 0.009	0.9100 ± 0.0091
LogiQA	0.227 ± 0.016	N/A

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Dataset used to train Leogrin/eleuther-pythia1b-hh-sft

Paper for Leogrin/eleuther-pythia1b-hh-sft

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Paper • 2101.00027 • Published Dec 31, 2020 • 10