laion/glm46-Toolscale-tasks-traces
The laion/glm46-Toolscale-tasks-traces model is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. It is specifically adapted using the DCAgent/glm46-Toolscale-tasks-traces dataset, suggesting an optimization for tool-use or agent-based task execution. This model is designed for scenarios requiring precise task tracing and interaction within complex environments, leveraging its Qwen3-8B foundation and a 32K context length.
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Overview
This model, glm46-Toolscale-tasks-traces, is an 8 billion parameter language model built upon the Qwen/Qwen3-8B architecture. It has been fine-tuned using the DCAgent/glm46-Toolscale-tasks-traces dataset, indicating a specialized focus on tasks related to tool-use, agent interactions, or tracing complex operational sequences.
Key Characteristics
- Base Model: Qwen/Qwen3-8B, a robust foundation for general language understanding.
- Fine-tuning Dataset: DCAgent/glm46-Toolscale-tasks-traces, suggesting a specialization in agent-based tasks and tracing.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: 32,768 tokens, enabling the processing of extensive input sequences for complex task understanding.
Training Details
The model was trained with a learning rate of 4e-05, a total batch size of 16 (achieved with gradient accumulation), and utilized the AdamW optimizer. Training spanned 7 epochs, employing a cosine learning rate scheduler with a 0.1 warmup ratio. This configuration aims to optimize performance for its specialized fine-tuning data.
Potential Use Cases
Given its fine-tuning on task traces, this model is likely suitable for applications involving:
- Agentic workflows: Understanding and generating responses for AI agents interacting with tools.
- Task automation: Interpreting and executing multi-step instructions.
- Complex system tracing: Analyzing and predicting sequences of actions or events.