Instructions to use yongzx/pythia-160m-sft-hh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yongzx/pythia-160m-sft-hh with Transformers:

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

pipe = pipeline("text-generation", model="yongzx/pythia-160m-sft-hh")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("yongzx/pythia-160m-sft-hh")
model = AutoModelForMultimodalLM.from_pretrained("yongzx/pythia-160m-sft-hh")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yongzx/pythia-160m-sft-hh with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yongzx/pythia-160m-sft-hh"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yongzx/pythia-160m-sft-hh",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yongzx/pythia-160m-sft-hh

SGLang

How to use yongzx/pythia-160m-sft-hh 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 "yongzx/pythia-160m-sft-hh" \
    --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": "yongzx/pythia-160m-sft-hh",
		"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 "yongzx/pythia-160m-sft-hh" \
        --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": "yongzx/pythia-160m-sft-hh",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yongzx/pythia-160m-sft-hh with Docker Model Runner:
```
docker model run hf.co/yongzx/pythia-160m-sft-hh
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

wandb run: https://wandb.ai/eleutherai/pythia-rlhf/runs/e0drjcsz?workspace=user-yongzx

Model Evals:

Task	Version	Filter	Metric	Value		Stderr
arc_challenge	Yaml	none	acc	0.1877	±	0.0114
		none	acc_norm	0.2372	±	0.0124
arc_easy	Yaml	none	acc	0.4390	±	0.0102
		none	acc_norm	0.4082	±	0.0101
logiqa	Yaml	none	acc	0.1889	±	0.0154
		none	acc_norm	0.2473	±	0.0169
piqa	Yaml	none	acc	0.6213	±	0.0113
		none	acc_norm	0.6279	±	0.0113
sciq	Yaml	none	acc	0.7230	±	0.0142
		none	acc_norm	0.6840	±	0.0147
winogrande	Yaml	none	acc	0.5162	±	0.0140
wsc	Yaml	none	acc	0.3654	±	0.0474
lambada_openai	Yaml	none	perplexity	58.9478	±	2.7662
		none	acc	0.2602	±	0.0061

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