Instructions to use ffurfaro/Titans-v2-OLMoE-1B-7B-0924 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ffurfaro/Titans-v2-OLMoE-1B-7B-0924 with Transformers:

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

pipe = pipeline("text-generation", model="ffurfaro/Titans-v2-OLMoE-1B-7B-0924")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ffurfaro/Titans-v2-OLMoE-1B-7B-0924", dtype="auto")

PEFT
How to use ffurfaro/Titans-v2-OLMoE-1B-7B-0924 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ffurfaro/Titans-v2-OLMoE-1B-7B-0924 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ffurfaro/Titans-v2-OLMoE-1B-7B-0924"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ffurfaro/Titans-v2-OLMoE-1B-7B-0924",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ffurfaro/Titans-v2-OLMoE-1B-7B-0924

SGLang

How to use ffurfaro/Titans-v2-OLMoE-1B-7B-0924 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 "ffurfaro/Titans-v2-OLMoE-1B-7B-0924" \
    --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": "ffurfaro/Titans-v2-OLMoE-1B-7B-0924",
		"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 "ffurfaro/Titans-v2-OLMoE-1B-7B-0924" \
        --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": "ffurfaro/Titans-v2-OLMoE-1B-7B-0924",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ffurfaro/Titans-v2-OLMoE-1B-7B-0924 with Docker Model Runner:
```
docker model run hf.co/ffurfaro/Titans-v2-OLMoE-1B-7B-0924
```

Titans-v2-OLMoE-1B-7B-0924

Titanesque version of allenai/OLMoE-1B-7B-0924 with parallel linearized attention (TPTT 😊) and PEFT.

The architecture was presented in the paper TPTT: Transforming Pretrained Transformers into Titans.

Model list

Classic model parameter with LiZA injection :

Subfolder	Max Self Attn Length	Mag Weight	Cross Gate	Max Chunk Size	Bidirectional	LoRA	Description
delta_rule	8192 (default)	0.5	False	64	False	Yes	Parallel linearized attention with delta_rule operator
delta_rule_gelu	8192 (default)	0.5	False	64	False	Yes	Non-linear operator with gelu activation
delta_product	8192 (default)	0.5	False	64	False	Yes	Second order operator with derivative trick
delta_product_r	8192 (default)	0.5	False	64	False	Yes	Second order operator with rotative trick
delta_product_c	8192 (default)	0.5	False	64	False	Yes	Second order operator with combined trick

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
"ffurfaro/Titans-v2-OLMoE-1B-7B-0924",
subfolder="tptt_subfolder", # see in repo tree
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("ffurfaro/allenai/OLMoE-1B-7B-0924")

prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs, skip_special_tokens=True))

Citation & Contact

If you use TPTT in your academic work, please cite Furfaro. For questions or support, please open an issue on the GitHub repository or contact the maintainer.

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Model tree for ffurfaro/Titans-v2-OLMoE-1B-7B-0924

Base model

allenai/OLMoE-1B-7B-0924

Finetuned

(6)

this model

Dataset used to train ffurfaro/Titans-v2-OLMoE-1B-7B-0924

Paper for ffurfaro/Titans-v2-OLMoE-1B-7B-0924

TPTT: Transforming Pretrained Transformer into Titans

Paper • 2506.17671 • Published Jun 21, 2025 • 5