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 20 hours ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

Files changed (1) hide show

README.md +8 -3

README.md CHANGED Viewed

@@ -1,15 +1,20 @@
 ---
 language:
 - en
 tags:
 - pytorch
 - causal-lm
 - pythia
-license: apache-2.0
-datasets:
-- EleutherAI/pile
 ---
 The *Pythia Scaling Suite* is a collection of models developed to facilitate
 interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
 It contains two sets of eight models of sizes

 ---
+datasets:
+- EleutherAI/pile
 language:
 - en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 tags:
 - pytorch
 - causal-lm
 - pythia
 ---
+This model serves as the base language model (backbone) for the paper **[Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory](https://huggingface.co/papers/2607.27919)**.
+📄 [Paper](https://arxiv.org/pdf/2607.27919) · 🌐 [Project Page](https://rubin-wei.github.io/memory-decoder-at-scale/) · 💻 [Code](https://github.com/LUMIA-Group/MemoryDecoder-at-Scale)
 The *Pythia Scaling Suite* is a collection of models developed to facilitate
 interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
 It contains two sets of eight models of sizes