bespokelabs/qwen3-8b-dabstep-reasoning-108-fixed-reasoning-sharegpt-sft
The bespokelabs/qwen3-8b-dabstep-reasoning-108-fixed-reasoning-sharegpt-sft model is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. It was specifically trained on the eval-ds-dabstep-reasoning-108-fixed-reasoning-sharegpt dataset, indicating an optimization for reasoning tasks. With a 32768 token context length, this model is designed for applications requiring robust logical processing and understanding of complex prompts.
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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.