nolanplatt/hvf-slm-v1-magistral

HVF-SLM v1 (Magistral): Experimental Baseline for Maritime Domain LLM Research

This is the first experimental iteration (v1) in the HVF-SLM series, serving as a baseline for maritime domain-specialized language models. While it demonstrated 131k context capability during training, significant limitations were discovered during evaluation.

Model Status

This model is provided for research purposes only as part of our iterative development process documented in [paper citation pending]. It has:

• Successful 131k token context window during training
• Poor coordinate extraction accuracy
• Tendency to generate incorrect vessel positions
• Limited understanding of maritime JSON structure

Model Details

• Base Model: Magistral-Small-2506 (24B parameters)
• Context Length: 131k tokens (via RoPE scaling factor 3.2)
• Training Dataset: ~22,000 synthetic maritime Q&A pairs
• Fine-tuning Method: QLoRA (4-bit) rank 128
• Status: Superseded by v2-llama and v3-qwen

Why This Model Failed

Despite achieving low training loss (0.004), the model failed to generalize to real-world maritime queries:

1. Memorization over comprehension: The model memorized training patterns rather than learning vessel relationships
2. JSON parsing failures: Unable to reliably extract specific vessels from complex AIS data
3. Coordinate hallucination: Generated plausible but incorrect lat/lon positions

This baseline informed critical improvements in subsequent versions:

• v2-llama: Better extraction but with hallucination issues
• v3-qwen: Architectural changes to address fundamental limitations

Citation

Part of a larger, in-depth paper by HVF. Full citation available upon publication.