Instructions to use lil-lab/CoLMLM-360M-FW with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lil-lab/CoLMLM-360M-FW with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lil-lab/CoLMLM-360M-FW")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lil-lab/CoLMLM-360M-FW") model = AutoModelForCausalLM.from_pretrained("lil-lab/CoLMLM-360M-FW") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lil-lab/CoLMLM-360M-FW with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lil-lab/CoLMLM-360M-FW" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lil-lab/CoLMLM-360M-FW", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lil-lab/CoLMLM-360M-FW
- SGLang
How to use lil-lab/CoLMLM-360M-FW 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 "lil-lab/CoLMLM-360M-FW" \ --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": "lil-lab/CoLMLM-360M-FW", "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 "lil-lab/CoLMLM-360M-FW" \ --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": "lil-lab/CoLMLM-360M-FW", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lil-lab/CoLMLM-360M-FW with Docker Model Runner:
docker model run hf.co/lil-lab/CoLMLM-360M-FW
CoLMLM-360M-FW
The 360M-parameter, SmolLM2-based Co-LMLM retriever from the paper Co-LMLM: Continuous-Query Limited Memory Language Models, trained on FineWeb-Edu.
Co-LMLM is a retrieval-aware language model: at each <FACT> position it emits a continuous query
from its hidden state that retrieves the fact's content from an external index at inference time,
instead of storing it in its weights.
Usage
The model is used together with a released retrieval index. See the code repository for setup, index downloads, and a copy-pasteable quick start:
👉 github.com/lil-lab/Co-LMLM
This model is part of the Co-LMLM collection.
Citation
@misc{feldman2026colmlmcontinuousquerylimitedmemory,
title={Co-LMLM: Continuous-Query Limited Memory Language Models},
author={Yair Feldman and Linxi Zhao and Nathan Godey and Dongyoung Go and Yilun Hua and Kilian Q. Weinberger and Jennifer J. Sun and Yoav Artzi},
year={2026},
eprint={2607.07707},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2607.07707},
}
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