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

Libraries

How to use Rubin-Wei/MemoryDecoder-OLMo-1.7B-finance 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-finance")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

MemoryDecoder-OLMo-1.7B-finance / README.md

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	---
	library_name: transformers
	license: apache-2.0
	tags:
	- memory-decoder
	- parametric-memory
	- long-term-memory
	- memorydecoder-at-scale
	- text-generation
	- olmo
	- finance
	datasets:
	- Rubin-Wei/MemoryDecoder-at-Scale-domain-data
	---
	# MemoryDecoder-OLMo-1.7B-finance

	## 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 (Finance)](https://huggingface.co/datasets/Rubin-Wei/MemoryDecoder-at-Scale-domain-data/tree/main/finance)

	This repository contains the 1.7B finance 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.

	This OLMo-vocabulary memory was trained for two epochs on the finance CPT corpus. The released configuration uses the OLMo vocabulary so that the memory can be combined with a vocabulary-compatible frozen OLMo backbone.

	This checkpoint is a memory component, not a standalone chat- or
	instruction-tuned model. The memory and backbone must use compatible token IDs
	and vocabularies.

	## Model details

	\| Field \| Value \|
	\|---\|---\|
	\| Memory size \| 1.7B class \|
	\| Architecture/tokenizer family \| Qwen3-style 1.7B memory; OLMo tokenizer/vocabulary \|
	\| Domain \| Finance \|
	\| Evaluation benchmark \| FinEval \|
	\| 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:

	```bash
	MODEL_PATH=/path/to/compatible-base-model \
	MEMDEC_PATH=Rubin-Wei/MemoryDecoder-OLMo-1.7B-finance \
	bash eval/lm-evaluation-harness/scripts/domain/evaluate_fineval.sh
	```

	See the repository documentation and launcher for benchmark-specific options,
	including interpolation weights and batch settings.

	## Intended use and limitations

	This checkpoint is intended for research and evaluation in the finance
	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.

	## Citation

	If you use this checkpoint, please cite:

	```bibtex
	@misc{wei2026memorydecoderscalepretrained,
	title={Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory},
	author={Rubin Wei and Jiaqi Cao and Jiarui Wang and Junming Zhang and Qipeng Guo and Bowen Zhou and Zhouhan Lin},
	year={2026},
	eprint={2607.27919},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2607.27919},
	}
	```