Model Overview

This model, laion/GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has been specifically fine-tuned using the DCAgent2/GLM-4.7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k dataset.

Key Characteristics

• Base Model: Fine-tuned from Qwen/Qwen3-8B.
• Parameter Count: 8 billion parameters.
• Context Length: Supports a substantial context window of 32768 tokens.
• Training Data: Utilizes a specialized dataset, indicating potential optimization for specific interactive or agent-based tasks.

Training Details

The fine-tuning process involved a learning rate of 4e-05, a total train batch size of 16 (with 8 multi-GPU devices and 2 gradient accumulation steps), and a cosine learning rate scheduler with a 0.1 warmup ratio over 7 epochs. The optimizer used was ADAMW_TORCH_FUSED.

Potential Use Cases

Given its fine-tuning dataset, this model is likely suited for applications involving:

• Agent-based systems: Interacting within defined environments or simulations.
• Sandbox testing: Generating or interpreting actions within constrained, verifiable systems.
• Automated verification: Tasks requiring interaction with oracle-verified outputs over specific timeframes (e.g., 120s).

Further details on specific intended uses and limitations are not provided in the current model description.