mhseo2/Qwen3-8B-Base-opd

TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Oct 16, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

mhseo2/Qwen3-8B-Base-opd is an 8.2 billion parameter causal language model from the Qwen series, pre-trained on 36 trillion tokens across 119 languages with a 32,768 token context length. It incorporates architectural refinements like qk layernorm and a three-stage pre-training process focusing on general language modeling, reasoning skills, and long-context comprehension. This base model is designed for broad language understanding and serves as a foundation for further fine-tuning.

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Qwen3-8B-Base-opd Overview

Qwen3-8B-Base-opd is an 8.2 billion parameter causal language model, part of the latest generation in the Qwen series. It is a pre-trained base model, not instruction-tuned, and features a substantial context length of 32,768 tokens. This model builds upon significant advancements in training data and architectural design, offering improved stability and performance over previous Qwen iterations.

Key Capabilities

  • Extensive Pre-training Corpus: Trained on an expanded corpus of 36 trillion tokens covering 119 languages, significantly increasing language coverage and data quality, including coding, STEM, reasoning, and multilingual data.
  • Advanced Training Techniques: Incorporates architectural refinements such as qk layernorm and global-batch load balancing loss for MoE models, enhancing stability and overall performance.
  • Three-Stage Pre-training: Utilizes a structured pre-training approach:
    • Stage 1: Focuses on broad language modeling and general knowledge acquisition.
    • Stage 2: Improves reasoning skills, including STEM, coding, and logical reasoning.
    • Stage 3: Enhances long-context comprehension by extending training sequence lengths.
  • Scaling Law Guided Tuning: Hyperparameters are systematically tuned across the three-stage pipeline for optimal training dynamics and performance.

Good For

  • Foundation for Custom Applications: Ideal as a robust base model for developers to fine-tune for specific downstream tasks and applications.
  • Multilingual Text Processing: Its extensive multilingual pre-training makes it suitable for applications requiring broad language understanding.
  • Research and Development: Provides a strong foundation for exploring advanced language model capabilities and architectural innovations.

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