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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metadata

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

This repository contains the 1.7B finance Memory Decoder released with Memory Decoder at Scale. 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, then set MODEL_PATH to a compatible frozen backbone and MEMDEC_PATH to this repository:

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:

@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},
}