Instructions to use Rubin-Wei/MemoryDecoder-Pythia-2.8B-general with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rubin-Wei/MemoryDecoder-Pythia-2.8B-general with Transformers:

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

pipe = pipeline("text-generation", model="Rubin-Wei/MemoryDecoder-Pythia-2.8B-general")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rubin-Wei/MemoryDecoder-Pythia-2.8B-general")
model = AutoModelForCausalLM.from_pretrained("Rubin-Wei/MemoryDecoder-Pythia-2.8B-general", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rubin-Wei/MemoryDecoder-Pythia-2.8B-general with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rubin-Wei/MemoryDecoder-Pythia-2.8B-general"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rubin-Wei/MemoryDecoder-Pythia-2.8B-general",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Rubin-Wei/MemoryDecoder-Pythia-2.8B-general

SGLang

How to use Rubin-Wei/MemoryDecoder-Pythia-2.8B-general 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 "Rubin-Wei/MemoryDecoder-Pythia-2.8B-general" \
    --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": "Rubin-Wei/MemoryDecoder-Pythia-2.8B-general",
		"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 "Rubin-Wei/MemoryDecoder-Pythia-2.8B-general" \
        --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": "Rubin-Wei/MemoryDecoder-Pythia-2.8B-general",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Rubin-Wei/MemoryDecoder-Pythia-2.8B-general with Docker Model Runner:
```
docker model run hf.co/Rubin-Wei/MemoryDecoder-Pythia-2.8B-general
```

Add metadata and paper link for Memory Decoder at Scale

by nielsr HF Staff - opened about 17 hours ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

nielsr

about 17 hours ago

This PR improves the model card by:

Adding the pipeline_tag: text-generation and library_name: transformers metadata so the model is properly categorized and the “Use in Transformers” button appears.
Including a reference to the Memory Decoder at Scale paper along with links to the project page and code repository, since this Pythia model is used as the backbone in that work.

Add metadata and paper link for Memory Decoder at Scalef2bce4a6

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README.md

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