Model Overview

The bespokelabs/qwen3-8b-dabstep-reasoning-108-fixed-reasoning-sharegpt-sft is an 8 billion parameter language model, derived from the Qwen/Qwen3-8B architecture. This model has undergone specific fine-tuning on the eval-ds-dabstep-reasoning-108-fixed-reasoning-sharegpt dataset.

Key Characteristics

• Base Model: Qwen/Qwen3-8B
• Parameter Count: 8 billion parameters
• Context Length: Supports a substantial context window of 32768 tokens.
• Fine-tuning Focus: The training dataset name suggests a specialization in reasoning capabilities, particularly for tasks involving fixed reasoning and ShareGPT-style interactions.

Training Details

The model was trained using the following key hyperparameters:

• Learning Rate: 1e-05
• Optimizer: ADAMW_TORCH
• Epochs: 5.0
• Batch Size: A total training batch size of 8 across 8 devices.

Potential Use Cases

Given its fine-tuning on a reasoning-focused dataset, this model is likely well-suited for:

• Complex problem-solving and logical deduction tasks.
• Applications requiring robust understanding and generation of reasoned responses.
• Scenarios benefiting from a large context window for detailed analysis.