Developers using Higgsfield's training cluster need to complete the following core technical configurations:
- environmental preparation: Get the CLI tool via GitHub (higgsfield-ai/higgsfield), configure the Docker environment and apply for the project deployment key (email verification of developer identity is required)
- data specification: The dataset needs to be converted to Parquet format and uploaded to Hugging Face Hub, and the annotated file needs to contain the scene semantic segmentation tags (refer to the example under tutorials/).
- Training configuration: Define key parameters in the YAML file, such as the recommended settings for the Llama 70B model:
- Global batch size (global_batch_size) = 4M tokens
- Learning rate (lr) = 6e-5 with cosine decay
- Context length (ctx_len) = 2048
- Resource allocation: Select the GPU type (e.g. A100×8 node) through the web panel, and the system automatically handles the gradient synchronization and checkpoint preservation
- Monitoring and debugging: Integrated W&B Kanban tracking of Loss curves in real time and support for triggered model evaluation (automatic test set run every 1000 steps)
Typical training example: fine-tuning the Mistral 7B model on a 50k marketing copy dataset, training can be completed in 40 minutes with API latency controlled within 180ms. The platform also provides a model distillation tool to compress the 70B model to 3B while maintaining 90% accuracy.
This answer comes from the articleHiggsfield AI: Using AI to Generate Lifelike Videos and Personalized AvatarsThe
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