Instructions to use Rubin-Wei/MemoryDecoder-OLMo-1.7B-law with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rubin-Wei/MemoryDecoder-OLMo-1.7B-law with Transformers:

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

pipe = pipeline("text-generation", model="Rubin-Wei/MemoryDecoder-OLMo-1.7B-law")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rubin-Wei/MemoryDecoder-OLMo-1.7B-law")
model = AutoModelForCausalLM.from_pretrained("Rubin-Wei/MemoryDecoder-OLMo-1.7B-law", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rubin-Wei/MemoryDecoder-OLMo-1.7B-law with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rubin-Wei/MemoryDecoder-OLMo-1.7B-law"
# 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-OLMo-1.7B-law",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Rubin-Wei/MemoryDecoder-OLMo-1.7B-law

SGLang

How to use Rubin-Wei/MemoryDecoder-OLMo-1.7B-law 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-OLMo-1.7B-law" \
    --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-OLMo-1.7B-law",
		"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-OLMo-1.7B-law" \
        --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-OLMo-1.7B-law",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Rubin-Wei/MemoryDecoder-OLMo-1.7B-law with Docker Model Runner:
```
docker model run hf.co/Rubin-Wei/MemoryDecoder-OLMo-1.7B-law
```

Rubin-Wei commited on 7 days ago

Commit

1a6969b

verified ·

1 Parent(s): 4c23be2

Add model card with project links and citation

Browse files

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README.md +10 -11

README.md CHANGED Viewed

@@ -14,6 +14,14 @@ datasets:
 ---
 # MemoryDecoder-OLMo-1.7B-law
 This repository contains the **1.7B law Memory Decoder** released
 with [Memory Decoder at Scale](https://arxiv.org/abs/2607.27919). It is a pretrained parametric long-term
 memory that can be swapped into a compatible frozen language-model backbone.
@@ -35,11 +43,10 @@ and vocabularies.
 | Intended backbone | Frozen OLMo-family model with the matching tokenizer/vocabulary |
 | Release contents | Inference weights, configuration, and tokenizer files |
-Optimizer states and training-resume artifacts are not part of this release.
 ## Usage
-Install the matching environment from the [GitHub repository](https://github.com/LUMIA-Group/MemoryDecoder-at-Scale), then
 set `MODEL_PATH` to a compatible frozen backbone and `MEMDEC_PATH` to this
 repository:
@@ -59,14 +66,6 @@ domain. Its outputs depend on the backbone, prompt, and interpolation settings.
 Domain specialization does not guarantee factual correctness or safety, and the
 model may inherit biases and errors from its training sources.
-## Links
-- [Project page](https://rubin-wei.github.io/memory-decoder-at-scale/)
-- [GitHub repository](https://github.com/LUMIA-Group/MemoryDecoder-at-Scale)
-- [Paper](https://arxiv.org/abs/2607.27919)
-- [Model and data collection](https://huggingface.co/collections/Rubin-Wei/memorydecoder-at-scale)
-- [Released domain data](https://huggingface.co/datasets/Rubin-Wei/MemoryDecoder-at-Scale-domain-data/tree/main/law)
 ## Citation
 If you use this checkpoint, please cite:

 ---
 # MemoryDecoder-OLMo-1.7B-law
+## Resources
+- [Project Page: Memory Decoder at Scale](https://rubin-wei.github.io/memory-decoder-at-scale/)
+- [GitHub Repository: LUMIA-Group/MemoryDecoder-at-Scale](https://github.com/LUMIA-Group/MemoryDecoder-at-Scale)
+- [Paper: Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory](https://arxiv.org/abs/2607.27919)
+- [Hugging Face Collection: MemoryDecoder-at-Scale](https://huggingface.co/collections/Rubin-Wei/memorydecoder-at-scale)
+- [Dataset: MemoryDecoder-at-Scale Domain Data (Law)](https://huggingface.co/datasets/Rubin-Wei/MemoryDecoder-at-Scale-domain-data/tree/main/law)
 This repository contains the **1.7B law Memory Decoder** released
 with [Memory Decoder at Scale](https://arxiv.org/abs/2607.27919). It is a pretrained parametric long-term
 memory that can be swapped into a compatible frozen language-model backbone.
 | Intended backbone | Frozen OLMo-family model with the matching tokenizer/vocabulary |
 | Release contents | Inference weights, configuration, and tokenizer files |
 ## Usage
+Install the matching environment from
+[LUMIA-Group/MemoryDecoder-at-Scale](https://github.com/LUMIA-Group/MemoryDecoder-at-Scale), then
 set `MODEL_PATH` to a compatible frozen backbone and `MEMDEC_PATH` to this
 repository:
 Domain specialization does not guarantee factual correctness or safety, and the
 model may inherit biases and errors from its training sources.
 ## Citation
 If you use this checkpoint, please cite: