Instructions to use dogtooth/open-lm-3b-202407 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dogtooth/open-lm-3b-202407 with Transformers:

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

pipe = pipeline("text-generation", model="dogtooth/open-lm-3b-202407", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("dogtooth/open-lm-3b-202407", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dogtooth/open-lm-3b-202407 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-202407"
# 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-202407",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dogtooth/open-lm-3b-202407

SGLang

How to use dogtooth/open-lm-3b-202407 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-202407" \
    --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-202407",
		"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-202407" \
        --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-202407",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dogtooth/open-lm-3b-202407 with Docker Model Runner:
```
docker model run hf.co/dogtooth/open-lm-3b-202407
```

open-lm-3b-202407 / README.md

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metadata

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

Open LM 3B — Knowledge Cutoff July 2024

This is a HuggingFace-format conversion of the Apple Open LM 3B oracle model trained with a knowledge cutoff of July 2024, from the TiC-LM (Time-Continual Language Modeling) project.

Model Details

Property	Value
Architecture	LLaMA-style (pre-norm, SwiGLU, RoPE)
Parameters	~2.7B
Training tokens	220B
Knowledge cutoff	July 2024
Vocab size	50,432
Context length	2,048
Original format	Apple Open LM

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "dogtooth/open-lm-3b-202407",
    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

@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}
}