---
license: apache-2.0
language:
- en
tags:
- maritime
- AIS
- vessel-tracking
- navigation
- fine-tuned
- experimental
- research
base_model: mistralai/Magistral-Small-2506
datasets:
- synthetic-maritime-ais-qa
model-index:
- name: hvf-slm-v1-magistral
  results: []
---

# 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- **Status**: Superseded by v2-llama and v3-qwen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.