precisionailabs/julia-routing-strix-halo-multilingual

Julia Routing Strix Halo Multilingual Experimental

Experimental multilingual fine-tune of Julia-1 for fast bounded-decision routing on AMD Strix Halo-class local machines.

Runtime status:

• CPU: tested
• GPU: expected to work through the Julia/PyTorch runtime, not separately benchmarked for this release
• NPU: not validated yet; AMD/XDNA NPU packaging and execution are future work

This is an experimental v0.2 multilingual candidate, not a replacement for the English-focused v0.1 checkpoint.

Intended use

Good candidates:

• task routing over fixed options
• eval-case routing: category and deterministic grader type
• documentation-update triage
• context-management metadata
• non-authoritative tool-policy hints

Do not use this model as the final authority for destructive commands, credential handling, production deploys, security replay safety, durable memory writes, summarization, or final prose.

Training summary

• Base model: SupersonicLabs/Julia-1
• Starting checkpoint: julia-routing-strix-halo v0.1 tuned routing model
• Fine-tuning method: native partial fine-tune, no LoRA
• Trainable parameters: 7,634,563
• Unfrozen modules: decision head plus last 2 encoder layers
• Learning rate: 1e-5
• Epochs: 1
• Languages: Spanish, French, German, Portuguese, Chinese, Japanese, Arabic
• Labels remain canonical English option IDs

Local eval summary

Eval	Score	Accuracy
Multilingual holdout small	187/224	83.5%
Multilingual holdout large	574/672	85.4%
English holdout	98/126	77.8%

By language on the large multilingual holdout:

Language	Accuracy
Portuguese	93.8%
French	90.6%
Spanish	88.5%
German	85.4%
Japanese	82.3%
Chinese	79.2%
Arabic	78.1%

All latency numbers in development were measured on CPU. NPU acceleration has not been validated.

Usage

Install the Julia runtime from the base model package, then load this checkpoint:

from julia import load_model

engine = load_model(
    "./julia-routing-strix-halo-multilingual",
    device="cpu",
    strict_encoding=True,
    max_length=1024,
    head_length=512,
)

result = engine.predict(
    state={"language": "es", "request": "Corrige la prueba TypeScript que falla y ejecuta la suite."},
    questions={
        "route": {
            "type": "choice",
            "instructions": "Which worker route should handle this request?",
            "criteria": {
                "code_edit": "modify repository files",
                "browser_qa": "interact with a web UI",
                "research": "gather information only",
                "chat": "answer conversationally",
            },
        }
    },
)
print(result)

Files

• model.safetensors — fine-tuned Julia decision weights
• julia_config.json — Julia runtime configuration
• encoder/config.json — encoder configuration
• tokenizer/ — tokenizer files

Limitations

• Experimental multilingual candidate.
• Specialized for bounded decisions and fixed option sets.
• Not a general-purpose embedding model.
• Not a generative model.
• Can be confidently wrong outside the routing distributions represented in training.
• NPU execution has not been validated yet.