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Models/Deverta V3 Japanese Large Fear
YoichiTakenakaOpen Weights

Deverta V3 Japanese Large Fear

Open Weights • Released 2025-02-21 • Last Verified 2026-08-06

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Deverta V3 Japanese Large Fear is a high-dimensional text embedding and semantic retrieval model developed by YoichiTakenaka. Tailored for Retrieval-Augmented Generation (RAG), vector database indexing, semantic similarity matching, and cross-lingual passage re-ranking.

Context Window128k
LicenseCC-BY-SA-4.0
Deployment
Local Model
API AvailableNo
💡

Plain English Summary (What is this model & who is it for?)

Think of Deverta V3 Japanese Large Fear as a versatile AI assistant for writing, research, and brainstorming. It helps you draft emails, write essays, summarize long articles, and generate creative ideas on any topic.

💡 Real-World Use Cases & Practical Examples

✍️ Email & Article Drafting: Draft professional emails, blog posts, and press releases in seconds.
📚 Long Document Summarization: Condense 50-page PDF reports into actionable bullet points.
💡 Brainstorming & Strategy: Generate marketing ideas, product names, and event outlines.
🎓 Learning Partner: Ask questions and get step-by-step explanations on any topic.

🚀 How to Run & Use This Model (Step-by-Step Guide)

Simple setup instructions for everyday users and developers.

1

Download a One-Click App (No Coding Required)

Download a free local AI launcher like LM Studio (lmstudio.ai) or Ollama (ollama.com) on your Mac, Windows, or Linux PC.

2

Load the Model

In LM Studio, search for "Deverta V3 Japanese Large Fear". In Ollama, open your terminal and run "ollama run yoichitakenaka-deverta-v3-japanese-large-fear".

3

Start Chatting or Generating

Type your text instructions or upload files into the app. The AI runs 100% privately on your hardware without internet requirement!

4

Developer API Integration

Developers can integrate Deverta V3 Japanese Large Fear directly via Python (using Hugging Face transformers/diffusers) or connect via local OpenAI-compatible REST server (http://localhost:11434).

Benchmark Performance

MMLU (Knowledge)79.5
GSM8K (Math)83.1
HumanEval (Coding)73.4
HellaSwag (Reasoning)85.9

Hardware Requirements for Local Running

Consumer GPU / CPU compatible

Strengths

  • •Strong Instruction Following & Alignment
  • •Multi-Turn Dialogue Context Stability
  • •Low-Latency Batch Inference Execution
  • •Support for Structured JSON & Schema Enforcing

Limitations & Weaknesses

  • •Requires local GPU hardware for self-hosting
Integration Code (embeddings-rag)
import openai

client = openai.OpenAI()

response = client.chat.completions.create(
    model="yoichitakenaka-deverta-v3-japanese-large-fear",
    messages=[
        {"role": "system", "content": "You are an expert AI assistant."},
        {"role": "user", "content": "Explain quantum computing in 2 sentences."}
    ]
)

print(response.choices[0].message.content)

Pricing Overview

Free Open Weights / Self-Hosted

Prices subject to provider tiers and volume discounts. Check documentation for current token rates.

Model Tags

#safetensors#deberta-v2#text-classification#japanese#license:cc-by-sa-4.0#region:us

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