Instructions to use dogtooth/open-lm-3b-202101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dogtooth/open-lm-3b-202101 with Transformers:

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

pipe = pipeline("text-generation", model="dogtooth/open-lm-3b-202101", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("dogtooth/open-lm-3b-202101", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dogtooth/open-lm-3b-202101 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dogtooth/open-lm-3b-202101"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dogtooth/open-lm-3b-202101",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dogtooth/open-lm-3b-202101

SGLang

How to use dogtooth/open-lm-3b-202101 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 "dogtooth/open-lm-3b-202101" \
    --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": "dogtooth/open-lm-3b-202101",
		"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 "dogtooth/open-lm-3b-202101" \
        --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": "dogtooth/open-lm-3b-202101",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dogtooth/open-lm-3b-202101 with Docker Model Runner:
```
docker model run hf.co/dogtooth/open-lm-3b-202101
```

open-lm-3b-202101 / README.md

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	---
	license: apple-ascl
	tags:
	- open-lm
	- temporal
	- tic-lm
	- causal-lm
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Open LM 3B — Knowledge Cutoff January 2021

	This is a HuggingFace-format conversion of the Apple Open LM 3B oracle model
	trained with a knowledge cutoff of January 2021, from the
	[TiC-LM (Time-Continual Language Modeling)](https://arxiv.org/abs/2410.14660) project.

	## Model Details

	\| Property \| Value \|
	\|---\|---\|
	\| Architecture \| LLaMA-style (pre-norm, SwiGLU, RoPE) \|
	\| Parameters \| ~2.7B \|
	\| Training tokens \| 220B \|
	\| Knowledge cutoff \| January 2021 \|
	\| Vocab size \| 50,432 \|
	\| Context length \| 2,048 \|
	\| Original format \| Apple Open LM \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model = AutoModelForCausalLM.from_pretrained(
	"dogtooth/open-lm-3b-202101",
	dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True,
	)
	tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
	```

	## Conversion Notes

	- Converted from the original Open LM `.pt` checkpoint to a custom `OpenLMForCausalLM` format.
	- Uses LayerNorm (not RMSNorm) to match the original Open LM training.
	- Includes QK norm (LayerNorm on Q and K projections before attention).
	- Architecture dimensions are auto-detected from checkpoint weights.
	- Requires `trust_remote_code=True` when loading.

	## Citation

	```bibtex
	@article{jain2024ticlm,
	title={Time-Continual Learning from a Streaming Language Model},
	author={Jain, Ameya and Ramesh, Aakanksha and Li, Tianjian and others},
	journal={arXiv preprint arXiv:2410.14660},
	year={2024}
	}
	```