Instructions to use Felladrin/Sheared-Pythia-160m-WebGLM-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Felladrin/Sheared-Pythia-160m-WebGLM-QA with Transformers:

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

pipe = pipeline("text-generation", model="Felladrin/Sheared-Pythia-160m-WebGLM-QA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Felladrin/Sheared-Pythia-160m-WebGLM-QA")
model = AutoModelForCausalLM.from_pretrained("Felladrin/Sheared-Pythia-160m-WebGLM-QA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Felladrin/Sheared-Pythia-160m-WebGLM-QA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Felladrin/Sheared-Pythia-160m-WebGLM-QA

SGLang

How to use Felladrin/Sheared-Pythia-160m-WebGLM-QA 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 "Felladrin/Sheared-Pythia-160m-WebGLM-QA" \
    --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": "Felladrin/Sheared-Pythia-160m-WebGLM-QA",
		"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 "Felladrin/Sheared-Pythia-160m-WebGLM-QA" \
        --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": "Felladrin/Sheared-Pythia-160m-WebGLM-QA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Felladrin/Sheared-Pythia-160m-WebGLM-QA with Docker Model Runner:
```
docker model run hf.co/Felladrin/Sheared-Pythia-160m-WebGLM-QA
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Princeton NLP's Sheared-Pythia-160m trained on the WebGLM-QA dataset

Base model: princeton-nlp/Sheared-Pythia-160m
Dataset: THUDM/webglm-qa

Recommended prompt format

<|references|>
{references}</s>
<|question|>
{question}</s>
<|answer|>

Downloads last month: 6

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for Felladrin/Sheared-Pythia-160m-WebGLM-QA

Base model

princeton-nlp/Sheared-Pythia-160m

Finetuned

(2)

this model

Quantizations

1 model

Felladrin
/

Sheared-Pythia-160m-WebGLM-QA

Princeton NLP's Sheared-Pythia-160m trained on the WebGLM-QA dataset

Recommended prompt format

Model tree for Felladrin/Sheared-Pythia-160m-WebGLM-QA

Dataset used to train Felladrin/Sheared-Pythia-160m-WebGLM-QA