SKU: 80310886144

Cometic Ford Big Block 4.40in Bore .045 Compressed Thickness MLS Head Gasket

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Description

Cometic Ford Big Block 4.40in Bore .045 Compressed Thickness MLS Head GasketComprised of multiple embossed layers of stainless steel with the outer layers utilizing a Viton rubber coating. Compressed operating thickness is 0. 045". Cometic MLS (Multi Layer Steel) Head Gaskets provide maximum sealing performance for todays high output engines and require no sealants. MLS gaskets are ideal for both aluminum heads to aluminum blocks and aluminum heads to cast iron blocks. They can withstand the shearing force created by the two

Comprised of multiple embossed layers of stainless steel with the outer layers utilizing a Viton rubber coating. Compressed operating thickness is 0.045". Cometic MLS (Multi-Layer Steel) Head Gaskets provide maximum sealing performance for todays high output engines and require no sealants. MLS gaskets are ideal for both aluminum heads to aluminum blocks and aluminum heads to cast iron blocks. They can withstand the shearing force created by the two materials. MLS gaskets have increased strength because they are comprised of multiple layers of stainless steel; which also creates the ability to rebound and resist corrosion. The outer layers are embossed and coated on both sides with Viton (a flour elastomer rubber based material that is heat resistant to 250 C/ 482 F). Viton is designed to meet the demands of a variety of harsh sealing environments, load conditions, and surface finishes. The center or shim layer is uncoated stainless steel, which can be varied to accommodate multiple thickness requirements.

This Part Fits:

Year Make Model Submodel
1969-1974 Ford Country Sedan Base
1969-1974 Ford Country Squire Base
1969-1972 Ford Custom Base
1969-1976 Ford Custom 500 Base
1978 Ford E-100 Econoline Base
1978 Ford E-100 Econoline Chateau
1978 Ford E-100 Econoline Custom
1978 Ford E-100 Econoline Northland
1975-1980 Ford E-250 Econoline Base
1975-1980 Ford E-250 Econoline Chateau
1975-1980 Ford E-250 Econoline Custom
1975-1978 Ford E-250 Econoline Northland
1975-1982,1984-1986 Ford E-250 Econoline Club Wagon Base
1975-1982 Ford E-250 Econoline Club Wagon Chateau
1975-1983,1987-1991 Ford E-250 Econoline Club Wagon Custom
1975-1978 Ford E-250 Econoline Club Wagon Northland
1983-1991 Ford E-250 Econoline Club Wagon XL
1984-1991 Ford E-250 Econoline Club Wagon XLT
1975-1982,1984-1986,1992-1996 Ford E-350 Econoline Base
1975-1982 Ford E-350 Econoline Chateau
1975-1983,1987-1991 Ford E-350 Econoline Custom
1975-1978 Ford E-350 Econoline Northland
1983-1996 Ford E-350 Econoline XL
1977-1982,1984-1986 Ford E-350 Econoline Club Wagon Base
1977-1982,1992-1996 Ford E-350 Econoline Club Wagon Chateau
1992-1996 Ford E-350 Econoline Club Wagon Chateau HD
1977-1983,1987-1996 Ford E-350 Econoline Club Wagon Custom
1992-1993 Ford E-350 Econoline Club Wagon Custom HD
1977-1978 Ford E-350 Econoline Club Wagon Northland
1983-1991 Ford E-350 Econoline Club Wagon XL
1994-1996 Ford E-350 Econoline Club Wagon XL HD
1984-1989,1991-1996 Ford E-350 Econoline Club Wagon XLT
1992-1996 Ford E-350 Econoline Club Wagon XLT HD
1975-1976 Ford Elite Base
1973-1974 Ford F-100 Base
1976-1978 Ford F-150 Base
1975-1979 Ford F-150 Custom
1975-1978 Ford F-150 Northland
1975-1979 Ford F-150 Ranger
1978-1979 Ford F-150 Ranger Lariat
1975-1979 Ford F-150 Ranger XLT
1977 Ford F-150 XLT
1973-1974,1976-1978,1983-1986 Ford F-250 Base
1975-1979,1987-1992 Ford F-250 Custom
1995-1996 Ford F-250 Eddie Bauer
1975-1978 Ford F-250 Northland
1975-1979 Ford F-250 Ranger
1978-1979 Ford F-250 Ranger Lariat
1975-1979 Ford F-250 Ranger XLT
1995-1996 Ford F-250 Special
1983-1996 Ford F-250 XL
1983 Ford F-250 XLS
1977,1983-1984,1993-1996 Ford F-250 XLT
1985-1992 Ford F-250 XLT Lariat
1997 Ford F-250 HD XL
1997 Ford F-250 HD XLT
1973-1974,1976-1978,1983-1986,1997 Ford F-350 Base
1975-1979,1987-1992 Ford F-350 Custom
1995 Ford F-350 Eddie Bauer
1975-1978 Ford F-350 Northland
1975-1979 Ford F-350 Ranger
1978-1979 Ford F-350 Ranger Lariat
1975-1979 Ford F-350 Ranger XLT
1995-1996 Ford F-350 Special
1983-1997 Ford F-350 XL
1983 Ford F-350 XLS
1977,1983-1984,1993-1997 Ford F-350 XLT
1985-1992 Ford F-350 XLT Lariat
1970 Ford Fairlane 500
1970 Ford Falcon Base
1970 Ford Falcon Futura
1969-1974 Ford Galaxie 500 Base
1969-1970 Ford Galaxie 500 XL
1972-1976 Ford Gran Torino Base
1974-1976 Ford Gran Torino Brougham
1974-1975 Ford Gran Torino Elite
1972-1975 Ford Gran Torino Sport
1972-1976 Ford Gran Torino Squire
1969-1978 Ford LTD Base
1970-1976 Ford LTD Brougham
1975-1978 Ford LTD Country Squire
1975-1978 Ford LTD Landau
1970-1971 Ford Mustang Base
1970 Ford Mustang Boss 429
1970-1971 Ford Mustang Grande
1970-1971 Ford Mustang Mach 1
1970 Ford Mustang Shelby GT-500
1969-1974 Ford Ranch Wagon Base
1970 Ford Ranch Wagon Police Cruiser
1970-1977 Ford Ranchero 500
1970-1971 Ford Ranchero Base
1970-1977 Ford Ranchero GT
1970-1976 Ford Ranchero Squire
1968-1976 Ford Thunderbird Base
1971 Ford Torino 500
1970-1976 Ford Torino Base
1970-1971 Ford Torino Brougham
1970-1971 Ford Torino Cobra
1970-1971 Ford Torino GT
1970-1971 Ford Torino Squire
1970-1971 Ford Torino Super Cobra Jet
1968-1978 Lincoln Continental Base
1968-1971 Lincoln Mark III Base
1972-1976 Lincoln Mark IV Base
1977-1978 Lincoln Mark V Base
1969-1974 Mercury Colony Park Base
1970-1971,1973 Mercury Cougar Base
1969-1970 Mercury Cougar Boss 429
1970 Mercury Cougar Cobra Jet
1970-1971,1973-1976 Mercury Cougar XR-7
1969-1971 Mercury Cyclone Base
1969 Mercury Cyclone CJ
1970-1971 Mercury Cyclone GT
1969-1971 Mercury Cyclone Spoiler
1975-1978 Mercury Grand Marquis Base
1969-1970 Mercury Marauder Base
1969-1970 Mercury Marauder X-100
1969-1978 Mercury Marquis Base
1969-1978 Mercury Marquis Brougham
1975-1976 Mercury Marquis Colony Park
1970-1974,1976 Mercury Montego Base
1975 Mercury Montego Brougham
1972-1973 Mercury Montego GT
1970-1976 Mercury Montego MX
1970-1974,1976 Mercury Montego MX Brougham
1976 Mercury Montego MX Villager
1970-1975 Mercury Montego Villager
1969-1974 Mercury Monterey Base
1969-1974 Mercury Monterey Custom
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SKU: 80310886144

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4.4 ★★★★★
Based on 26 reviews
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R
Ryan Meyer
Cuba, US
★★★★★ 3
A Broad Overview, But Light on Modern Fine-Tuning
Format: Paperback
I'm currently really interested in fine-tuning LLMs and recently completed my first LoRA-based fine-tuning on a quantized model. I came to this book looking for more detail on fine-tuning. While it touches on the topic, I found the content didn’t quite align with the current state of the field in 2025. Techniques like LoRA, QLoRA, and PEFT weren’t really covered, and the material leaned more toward what I think are older or lower level approaches. That made it harder to connect with what I’m actually working on. That said, when I shifted to other chapters — like the sections on prompt engineering techniques such as Chain of Thought (CoT) and Tree of Thought (ToT) — I found more value. These sections were clearer, and I picked up a few practical insights, like using few-shot examples that walk through the CoT reasoning process. That’s not something I’ve tried before, and I can see how it might help smaller models that struggle with any type of reasoning tasks. Overall, the book feels more like a broad overview of all LLM concepts. For someone exploring many topics across the LLM ecosystem, it offers a wide-ranging introduction. But for readers like me who are actively trying to learn and apply techniques like fine-tuning and quantization, it may leave you wanting up-to-date guidance.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 10, 2025
V
Vineeth Sai
West Palm Beach, US
★★★★★ 5
Great foundation read for security!
Format: Paperback
This book is a great read! It builds a strong foundation and I would highly recommend it for builders who are interetsed in building on LLMs and ensuring everything is secure. Security is super important and this book does it justice!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 27, 2025
C
Verified Purchase
CL
New York, US
★★★★★ 5
Loved it
Format: Paperback
I’ve easily read dozens of tech books. I liked this one a lot. Sure, there were boring parts, but most of it was engaging, especially on dry subjects. I previously read “How AI Works” and found this more informative and way more enjoyable. I got through the 700 pages in about 5 weeks while also learning about probability and linear algebra from other books and online sources. I’d love to read something more advanced by the author, maybe getting into more modern applications. I feel more comfortable with the subject and feel I am now ready to conquer more advanced texts. I initially picked this up to give me some background before reading “How to Build a LLM (from scratch)”. I’ve ordered an intermediary Deep Learning with Python book as well, but wouldn’t mind a more advanced theory book to accompany these books. I’ll definitely be rereading sections of this book to further familiarize myself with topics like backpropagation. Highly recommend if you’re looking for a gentle, but broad introduction to the topic.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 14, 2025
A
Verified Purchase
Amazon Customer
Lake Worth, US
★★★★★ 5
A Good Place to Start Learning AI
Format: Paperback
Diving into the world of artificial intelligence can feel like stepping into a vast, uncharted ocean, and if you're looking for a reliable vessel to navigate these waters, this book is an excellent choice. However, I must be candid—this journey is not for the faint-hearted or those hoping to breeze through. The subject of AI, with its complex algorithms and intricate theories, is notoriously challenging. You won't find yourself flipping pages at a rapid pace, as this is not a title designed for speed-reading. Instead, it demands your full attention and a willingness to engage deeply with the material. At the heart of AI lies mathematics—a fundamental pillar that underpins the entire discipline. This book, while comprehensive, offers only a glimpse into the mathematical framework that drives artificial intelligence. But don’t be disheartened by this. Think of it as a solid foundation, a primer that will arm you with the essential concepts needed before you delve deeper into the more advanced mathematical intricacies elsewhere. When you do eventually tackle those more complex equations, you'll find yourself better equipped, with a clearer understanding of the principles at play. I should also mention that I'm no stranger to Andrew's work. Having explored some of his other writings, I can confidently say that he possesses a unique flair for communication. His ability to distill complex ideas into accessible language, without losing the essence of the subject, is truly commendable. Andrew writes with a certain finesse and sophistication that makes even the most daunting topics seem approachable. His style is not just informative, but also engaging, with a touch of elegance that sets his work apart from others in the field. In summary, while the path to mastering AI is undeniably steep, this book serves as an invaluable guide. It’s not just a starting point; it’s a beacon for those who are serious about understanding the intricacies of artificial intelligence. Be prepared to invest time and effort, and in return, you'll gain a solid foothold in a subject that is as fascinating as it is complex.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on September 2, 2024
M
Verified Purchase
MrGee
Dallas, US
★★★★★ 5
An enjoyable, and seriously excellent, path to understanding Deep Learning...
Format: Paperback
Deep Learning is changing our world. If you want to understand more, this is a great place to start. Andrew Glassner is a talented explainer - I took his short course on Deep Learning and learned so much, but also came away impressed at how well he can make complex material so clear and engaging. And this book is jammed packed with insights, visuals, and clear explanations. The author has a playful, sometimes quirky style that shines through, which gives this tour a lot of personality as well as information. Very enjoyable reading - I felt like he captured all that was good about his course (and then some) and bottled it up in this book. There is a lot more material here than in that course, and it is well laid-out and organized so that it is easy to roam around and come back to review the pieces that matter to you. Even if you plan to go to on to be a world-class Deep Learning engineer or mathematician, you have to start by understanding the concepts. And this book does a great job of presenting all the core ideas in a way that makes them clear and memorable.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 5, 2021

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