SKU: 81754622506

Sweetlix Whitetail Deer Pressed Block

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Description

Sweetlix Whitetail Deer Pressed BlockHighly palatable supplement block for deer of all ages Delivers nutrients known to be deficient in most natural diets Intended for wildife conservation practices only Feeding Instructions IMPORTANT: THE USE OF THIS DEER PRODUCT IS INTENDED FOR WILDLIFE CONSERVATION PRACTICES ONLY. Feeding Directions and Management SWEETLIX Whitetail Deer Blocks should be self fed year round to growing and breeding deer in their natural habitat. SWEETLIX Whitetail Deer

  • Highly palatable supplement block for deer of all ages
  • Delivers nutrients known to be deficient in most natural diets
  • Intended for wildife conservation practices only

Feeding Instructions

IMPORTANT: THE USE OF THIS DEER PRODUCT IS INTENDED FOR WILDLIFE CONSERVATION PRACTICES ONLY. 

Feeding Directions and Management

SWEETLIX Whitetail Deer Blocks should be self- fed year-round to growing and breeding deer in their natural habitat. SWEETLIX Whitetail Deer Block should be positioned in areas frequented by deer such as trails, rubs and watering spots. SWEETLIX Whitetail Deer Block is scientifically balanced to supplement deer’s natural vegetation by providing protein, minerals, vitamins and energy necessary for optimal growth, antler and bone development and reproduction.

Consumption of this product will vary according to the deer’s body weight and the quality and quantity of available natural vegetation. Intake of this product by growing and breeding deer should be at the rate of 1/4 to 1/2 pound per head per day.

Nutrition Information

Crude Protein, Min 14.00%
Crude Fat, Min 2.00%
Crude Fiber, Max 10.00%
Calcium, Min 8.00%
Calcium, Max 9.60%
Phosphorus, Min 1.00%
Salt, Min 12.00%
Salt, Max 14.40%
Cobalt, Min 3 ppm
Copper, Min 250 ppm
Iodine, Min 10 ppm
Manganese, Min 1,000 ppm
Selenium, Min 3.1 ppm
Zinc, Min 700 ppm
Vitamin A, Min 25,000 IU/lb
Vitamin D-3, Min 2,500 IU/lb
Vitamin E, Min 25 IU/lb

Product Ingredients

Processed Grain By-Products, Grain Products, Plant Protein Products, Salt, Calcium Carbonate, Molasses Products, Roughage Products, Calcium Hydroxide, Monocalcium Phosphate, Dicalcium Phosphate, Manganous Oxide, Manganese Sulfate, Zinc Oxide, Zinc Sulfate, Copper Sulfate, Selenium Yeast, Ethylenediamine Dihydroiodide, Calcium Iodate, Cobalt Carbonate, Vitamin A Supplement, Vitamin D-3 Supplement, Vitamin E Supplement, Mineral Oil and Artificial Flavoring (source of apple flavor).

 

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SKU: 81754622506

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★★★★★ 5
Excellent introduction
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This is a well-written introduction for quickly learning how to use Ollama to run LLMs on your local machine. The book is light on theory but very good for facilitating hands-on experimentation. Only minimal Python experience is needed. Contrary to another reviewer's comment, the source code, which can be copied from the Kindle version of the text, is also readily available by emailing the author at the address provided. The final exercise in the book leads you through generating a RAG application to use Ollama to process your own documents without having to make them available to an external server.
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Reviewed in the United States on April 6, 2026
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Simon Mok
Waukegan, US
★★★★★ 5
Effective book for learning ollama
Format: Paperback
The author has explained clearly the concept. Helpful and effectively written.
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Format: Kindle
I just read this book and really enjoyed how it breaks down using Ollama as your own private LLM. It gives you just enough info to spark some cool ideas and make you want to dive deeper into AI. If you’re totally new and curious, this is a fun, easy place to start!
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Reviewed in the United States on August 4, 2025
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jrw879
Charlottesville, US
★★★★★ 5
Lim provides an easy-to-follow path to get an LLM-powered app up and running
Format: Kindle
Lim has written another really clear, easy-to-follow tutorial. This one walked me through the Ollama platform showing step-by-step how to download open weight models, query them, supply them with my own documents, and write applications that are powered by LLMs. Lim provides an easy-to-follow path to get the first version of an LLM-powered app up and running.
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Reviewed in the United States on February 20, 2026
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Wil C
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Excellent roadmap for the local AI jungle
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Great little book. Every page has something useful. No fluff. Clearly written by someone whose been in the trenches lol
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