You can deploy this model and start earning money today!

ffurfaro/
Titans-v2-Mistral-7B-v0.3

Name: Titans-v2-Mistral-7B-v0.3
Availability: InStock

mistralai/Mistral-7B-v0.3

finetune

apache-2.0

endpoints_compatible

Views

No views yet

Titans-v2-Mistral-7B-v0.3

arXiv

Titanesque version of mistralai/Mistral-7B-v0.3 with parallel linearized attention (TPTT 😊) and PEFT.

The architecture was presented in the paper TPTT.

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

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
"ffurfaro/Titans-v2-Mistral-7B-v0.3",
subfolder="tptt_subfolder", # see in repo tree
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("ffurfaro/mistralai/Mistral-7B-v0.3")

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.