aviralku/openclaw-phase1-notes-100m
The aviralku/openclaw-phase1-notes-100m is a 27 billion parameter research checkpoint derived from the Qwen3.8-27B base model. It was developed by aviralku through full-parameter next-token training on the balanced OpenClaw recursive-note corpus. This model is specifically trained for note-NTP (next-token prediction) with approximately 100M note-token budget, making it suitable for tasks involving sequential note generation or analysis. Its bfloat16 weight dtype and Hugging Face Transformers export format ensure compatibility and efficiency for research applications.
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OpenClaw Phase-1 Notes Model
The aviralku/openclaw-phase1-notes-100m is a specialized 27 billion parameter research checkpoint, originating from the Qwen3.8-27B base model. It has undergone full-parameter next-token training using the OpenClaw recursive-note corpus, focusing on sequential note prediction.
Key Characteristics
- Base Model: Derived from Qwen3.8-27B.
- Training Focus: Phase 1, specifically for note-NTP (next-token prediction).
- Training Data: Utilized a balanced OpenClaw recursive-note corpus.
- Token Budget: Trained with an approximate 100 million note-token budget.
- Checkpoint Details: Represents epoch 1, step 3125 of the training process.
- Policy KL Coefficient: Set at 0.01.
- Weight Data Type: Uses bfloat16 for efficient computation.
- Export Format: Available in Hugging Face Transformers format, distributed across six safetensors shards.
Potential Use Cases
This model is particularly well-suited for research and development in areas requiring:
- Sequential Note Generation: Creating or extending sequences of notes based on learned patterns.
- Note Analysis: Exploring the properties and behaviors of models trained on structured note data.
- Experimental Language Modeling: Investigating the impact of specific training methodologies and datasets on model performance in a controlled environment.
It provides a valuable resource for researchers working with specialized text generation and understanding tasks, especially within the domain of structured or recursive note-taking systems.