SKU: 77170967119

Sousaishojoteien Plastic Model Kit Grand Scale Dress Up Body M with Stylet 25 cm

$76.50 $85.00
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Description

Sousaishojoteien Plastic Model Kit Grand Scale Dress Up Body M with Stylet 25 cmSTYLET joins the GRANDE SCALE DRESS UP BODY series! The original CAD data of DRESS UP BODY [M] has been enlarged and remodeled, and while maintaining its simple yet highly articulated structure, it can now be easily combined with 1 6 scale doll clothes and accessories! (The model is approx. 21cm in height without the head part.) Product Highlights: Head Part: The kit includes the head part from GRANDE SCALE STYLET without the headgear. As a bonus

STYLET joins the GRANDE SCALE DRESS UP BODY series! The original CAD data of DRESS UP BODY [M] has been enlarged and remodeled, and while maintaining its simple yet highly articulated structure, it can now be easily combined with 1/6 scale doll clothes and accessories! (The model is approx. 21cm in height without the head part.)

Product Highlights:

Head Part: The kit includes the head part from GRANDE SCALE STYLET without the headgear. As a bonus part, the back of the head from INNOCENTIA is included, allowing for a standard short hairstyle or a set of twintails to be recreated. The bangs can be swapped between standard bangs and bangs with joints. Cat ears can be attached to the bangs with joints.

Two Body Types: The set includes two models with different body types. *The set includes only one head part. The loungewear body wears a short camisole tank top and shorts, ideal for settings like relaxing at home. The innerwear body has been designed for use with separately sold doll clothes for 22cm to 25cm models. *Note that some clothes may not fit the model depending on their specs or designs.

Three Different Length Neck Joints: The set includes three types of neck joints. Choose and assemble the model with the joint that best fits the height of the collar of the clothes. The size of the ball joints on each are the same as the Frame Arms Girl GRANDE SCALE series kits, making them compatible.

Sliding Neck Joint: The neck joints have the forward and back sliding feature from Koyomi Takanashi Swim Style, allowing for dynamic vertical movement such as tucking in the chin and looking up. It can be used to prevent hair parts from getting in the way of articulation.

Different Length Socks: The set comes with three types of interchangeable calf parts and socks: short socks, high socks, and thigh-high socks. *The same thigh parts will be used when assembling legs with the short socks or high socks.

Material Colors: The body uses Skin Color A which makes it interchangeable with parts from the Frame Arms Girl GRANDE SCALE series kits.

Included Items:
- 1 x Loungewear Body
- 1 x Innerwear Body
- Left and Right x Short Sock Leg Parts
- Left and Right x High Sock Leg Parts
- Left and Right x Thigh-High Sock Leg Parts
- Left and Right x Bare Leg Parts
- Left and Right x Sneakers
- Left and Right Each x 6 Types of Hand Parts (Open (Elegant)/Holding (Sword)/Holding (Narrow)/Supporting/V-Sign/Pointing B)
*Note that only one pair of each is included, meaning the loungewear and innerwear bodies cannot be displayed with the same type of hand parts simultaneously.
- 2 x Neck Joint Type A
- 2 x Neck Joint Type B
- 2 x Neck Joint Type C
- 1 x GRANDE SCALE STYLET Head (Without Headgear)
- Pre-Painted Face Parts (Neutral Facing Forward/Smiling Facing Forward/Surprised Facing Forward)
Product Size: 25 cm

Shipping Notes
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Exchange/Return Notes
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  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
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SKU: 77170967119

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4.8 ★★★★★
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Product Reviews
O
Om S
Houston, US
★★★★★ 4
Title: Really Good Book for Learning LLMs
Format: Paperback, Format: Paperback
I picked up this book after struggling with LLM implementation at work. Ken Huang explains things clearly without too much technical jargon. The book covers everything from data preparation to building AI agents. I especially liked the chapters on RAG and prompting techniques - they helped me improve my current projects. The code examples actually work, which is nice. Some parts are pretty advanced, so you need basic Python knowledge. I had to read a few chapters twice to fully get it. The fairness and bias detection section was eye-opening. Good practical advice throughout. Not just theory - real solutions you can use. Worth the money if you're serious about LLM development. Recommended for anyone building AI systems professionally.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 25, 2025
J
Jiewen Wang
Omaha, US
★★★★★ 5
a comprehensive guide at the intersection of generative AI and cybersecurity
Format: Kindle
This book blends deep theoretical foundations with practical frameworks and forward-looking strategies. From adversarial risk models to actionable guidance using OWASP Top 10 for LLMs and the NIST AI RMF, it offers both technical depth and operational clarity. What makes it stand out is its balance of academic rigor and real-world CISO insights, providing a holistic perspective on securing GenAI systems. While it leans enterprise-focused, the content remains accessible to security engineers, risk managers, and policy leaders alike. Generative AI Security is a timely and essential read for anyone working to deploy GenAI responsibly—building systems with both power and integrity in today’s fast-evolving threat landscape.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 2, 2025
N
Nader
Lexington, US
★★★★★ 1
Light on substance and heavy on flaws
Format: Paperback
The book has a great list of topics, but fails to provide much substance any of them. Most of the provided code is just comments that avoid the actual crux of the issues being discussed. (e.g. #implement the logic to validate XYZ - while the whole point of this chapter is teach how the heck we validate XYZ!) Some parts are plain wrong, for example the part on Graph based RAG is fundamentally flawed as it assumes the text embedding and the graph embedding are in the same latent space. (This is one of many more examples). Seems like the book was rushed, and the author has limited hands on experience (if any). At least we know based on the amount of flaws that it was not written by an LLM
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 31, 2025
N
noam barkay
Louisville, US
★★★★★ 5
Excellent book to truly understand LLM design patterns
Format: Paperback
I just finished reviewing Ken Huang's pocket book on LLM Design Patterns, and WOW what an amazing resource! This book is excellent if you want to truly understand how to create and enhance intelligent AI language models, all that in your pocket! Ken makes the difficult things seem surprisingly easy, and that's the real MAGIC. - How to prepare your data for training by making it extremely clean. Developing the brains: the practical aspects of training, optimizing, and maintaining your models. - Learn amazing prompting techniques (such as Chain-of-Thought and Tree-of-Thoughts) to improve your AI's reasoning and problem-solving abilities. Learn everything there is to know about RAGs so that your LLM can incorporate outside expertise. - It also delves into creating "agentic" AI that is capable of action and planning (not only simple plan and execute but also enhanced techniques like ReWoo!) Really, this feels like a useful toolkit, so Ken thank you for that resource Thanks, Idan Habler
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 9, 2025
R
Ryan Meyer
Alexandria, 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

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