shirley09221/qwen3-8b-full-pretrain-control-tweet-1m-en

TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 4, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

The shirley09221/qwen3-8b-full-pretrain-control-tweet-1m-en model is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. It is specifically adapted using the control_tweet_1m_en dataset, suggesting an optimization for processing or generating content related to tweets or short-form text. This model is designed for tasks requiring understanding and generation within a 32768 token context length, leveraging its specialized fine-tuning for social media-like data.

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Model Overview

This model, qwen3-8b-full-pretrain-control-tweet-1m-en, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has undergone specific fine-tuning on the control_tweet_1m_en dataset, indicating a specialization in handling English tweet-like data or short, controlled text formats. The model supports a substantial context length of 32768 tokens.

Training Details

The fine-tuning process utilized the following key hyperparameters:

  • Learning Rate: 1e-05
  • Batch Sizes: train_batch_size of 1, eval_batch_size of 8
  • Optimizer: ADAMW_TORCH with default betas and epsilon
  • LR Scheduler: cosine type with 0.1 warmup steps
  • Epochs: Trained for 3.0 epochs

The training was conducted across 8 devices in a multi-GPU distributed setup, ensuring efficient adaptation to the target dataset. The framework versions used include Transformers 5.8.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.

Potential Use Cases

Given its fine-tuning on a tweet-related dataset, this model is likely well-suited for applications involving:

  • Social Media Analysis: Understanding sentiment, topics, or trends in short-form text.
  • Content Generation: Creating tweet-style posts or concise English text.
  • Text Classification: Categorizing or tagging short messages based on their content.

Further details on intended uses, limitations, and specific evaluation data are not provided in the current model card.

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