laya-MLX-8bit

MLX conversion of convaiinnovations/laya for Apple Silicon: the root (English) checkpoint and the multilingual and typed-decisions checkpoints.

• Converted from the base model at revision 55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851.
• Not affiliated with or endorsed by convaiinnovations.
• Converted by Sasan Sotoodehfar, CAVI AI (https://cavi-ai.xyz).

Contents

Path	Checkpoint	Encoder	Context (max_len / head_max_len)	Size
/	laya (English)	ModernBERT-large, 28 layers	512 / 192	524 MB
multilingual/	laya-multilingual	mmBERT-base, 22 layers, 256k vocabulary	1024 / 256	569 MB
typed-decisions/	laya-typed-decisions	ModernBERT-large, 28 layers	1024 / 256	524 MB

• Each folder holds model.safetensors, config.json, tokenizer.json, tokenizer_config.json.
• Quantized to 8-bit (affine, group size 32): the linear layers of the encoder and of the decision head.
• Kept in float16: token embeddings, norms, type embedding, option scorer, act head.
• config.json carries the decision settings and the shipped temperatures (temperature, temperature_by_options).
• laya/: MLX model code (ModernBERT encoder from mlx-embeddings, decision head, request rendering, calibrated answers). mlx-embeddings 0.1.0 does not include this model.
• 8-bit only: at 4 bits the multilingual checkpoint changed 3 of 25 reference answers.

Requirements

• Apple Silicon Mac.
• Python 3.12.
• pip install mlx-embeddings==0.1.0

Usage

import sys
from huggingface_hub import snapshot_download
path = snapshot_download("cavi-ai/laya-MLX-8bit")
sys.path.insert(0, path)
from laya import load, predict

model, tokenizer = load(path)  # or f"{path}/multilingual", f"{path}/typed-decisions"
state = {"document": "I was charged twice for my subscription this month. Please refund the duplicate charge."}
questions = {
    "department": {"type": "choice", "instructions": "Which team should handle this ticket?",
                   "criteria": {"billing": "payments, invoices, refunds", "technical": "bugs, outages, errors", "sales": "new purchases and upgrades"}},
    "urgency": {"type": "score", "instructions": "How urgent is this ticket?", "criteria": ["not urgent", "somewhat urgent", "very urgent"]},
    "refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"},
}
print(predict(model, tokenizer, state, questions)["answers"])

• state: text or a JSON object.
• Question types: choice (criteria as a list or an object of option: description), score (ordered list of levels), noul (optional true / false texts).
• Answers: choice with probabilities and confidence; score (expected level) with probabilities; noul (probability of true).
• All questions of a request run in one forward pass.

Measured results

Hardware: Apple M5 Max.

Check	root	multilingual	typed-decisions
Reference questions	17 (English)	25 (7 languages)	17 (English)
Same answer as the PyTorch fp32 reference	17/17	25/25	17/17
Mean / max probability change vs the reference	0.0009 / 0.011	0.0026 / 0.021	0.0008 / 0.003
Usage example above: department, refund probability	billing 0.95, 0.89	billing 1.00, 0.99	billing 0.82, 0.73

• Reference: transformers ModernBertModel plus torch.nn decision-head layers, float32, on CPU, loaded from the original checkpoints; tokenization through AutoTokenizer.
• Port check: the MLX code in float32 on CPU matches the reference within 2.5e-5 on every option logit, with identical input ids.
• One request with three questions answers in 12–46 ms after loading.

License

• Model weights, tokenizer, and configuration files: Apache-2.0, inherited from the base model. See LICENSE.
• Code in laya/: MIT. See laya/LICENSE.

Links

• Base model: https://huggingface.co/convaiinnovations/laya
• MLX port source: https://github.com/cavi-ai/mlx-agent