Stage-org/4b-solvability-200-27b-hard-fixed-epoch3
Stage-org/4b-solvability-200-27b-hard-fixed-epoch3 is a 4.5 billion parameter language model based on the Qwen3.5-4B architecture, fine-tuned using a reinforcement learning (RL) method. This model was specifically trained on the '4b-solvability-200-27b-hard-fixed' dataset over three epochs, with a focus on enhancing problem-solving capabilities. It features a substantial context length of 32768 tokens, making it suitable for tasks requiring extensive contextual understanding and complex reasoning.
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Model Overview
Stage-org/4b-solvability-200-27b-hard-fixed-epoch3 is a 4.5 billion parameter model derived from the Qwen3.5-4B base architecture. It has undergone specialized training using a reinforcement learning (RL) approach, specifically configured for 3 epochs and 10,000 learner steps. The training utilized the Stage-org/4b-solvability-200-27b-hard-fixed dataset, indicating a focus on particular problem-solving or reasoning tasks.
Key Training Details
- Base Model: Qwen3.5-4B
- Training Method: Reinforcement Learning (RL)
- Dataset:
Stage-org/4b-solvability-200-27b-hard-fixed - Epochs: 3
- Learner Steps: 10,000
- Context Length: The model supports a sequence length of up to 300,000 tokens during training, with an inference model configured for a maximum length of 65,536 tokens, and a generation max token limit of 4096.
- Optimization: Utilizes AdamW optimizer with specific learning rate and weight decay settings, and Flash Attention 2 for efficiency.
Potential Use Cases
Given its RL fine-tuning on a specialized dataset, this model is likely optimized for:
- Complex Problem Solving: Tasks requiring iterative refinement and strategic decision-making.
- Reasoning-intensive Applications: Scenarios where logical deduction and structured output are crucial.
- Long Context Understanding: Its significant context window makes it suitable for processing and generating responses based on large amounts of input text.