FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper FinBERT: Financial Sentiment Analysis with Pre-trained Language Models and our related blog post on Medium. The model will give softmax outputs for three labels: positive, negative or neutral. About Prosus Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and investing globally…
laya-coreml is an open-weight model for text classification from Aac6fef, released under Apache License 2.0. Its published files total 847.2 MB.
Laya typed decisions on Apple Silicon, using CPU + GPU. This is a portable Core ML bundle for laya-coreml, converted from convaiinnovations/laya. It outputs choice, score, and noul probabilities with zero generated tokens.
Parameters—
Context—
Weights843.6 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Model Card
By Aac6fef, published under apache-2.0, revision fff78b2d9750.
Laya typed decisions on Apple Silicon, using CPU + GPU. This is a portable Core ML bundle for laya-coreml, converted from convaiinnovations/laya. It outputs choice, score, and noul probabilities with zero generated tokens. Inference needs no PyTorch, Transformers, MLX, remote code, or cloud API. Apple Silicon, macOS 15+, Python 3.11–3.13. Tested on M3 Max / macOS 27.2. To download explicitly and then run entirely offline: Use laya.load("aac6fef/laya-coreml", localfilesonly=True) for a cached snapshot or pass a local directory. Use revision=" " to pin a remote revision. This FP16 export retains the original model architecture and decision schema. The enumerated-length GPU export is the…
Read Aac6fef's full model card
Laya typed decisions on Apple Silicon, using CPU + GPU.
This is a portable Core ML bundle for laya-coreml,
converted from convaiinnovations/laya.
It outputs choice, score, and noul probabilities with zero generated tokens.
Inference needs no PyTorch, Transformers, MLX, remote code, or cloud API.
Run
Apple Silicon, macOS 15+, Python 3.11–3.13. Tested on M3 Max / macOS 27.2.
pip install laya-coreml
import laya_coreml as laya
agent = laya.load("aac6fef/laya-coreml") # Download once; Core ML runs locally.
result = agent.predict(
"The customer asks for a refund of a duplicate payment.",
{"refund": {"type": "noul", "instructions": "Does the customer request a refund?"}},
)
print(result["answers"])
To download explicitly and then run entirely offline:
hf download aac6fef/laya-coreml --local-dir models/laya
pip install 'laya-coreml[demo]'
laya-coreml-snake --model models/laya --fps 12
Use laya.load("aac6fef/laya-coreml", local_files_only=True) for a cached snapshot or pass a
local directory. Use revision="<Hub commit SHA>" to pin a remote revision.
Format and fidelity
This FP16 export retains the original model architecture and decision schema. The enumerated-length GPU export is the validated general-purpose configuration.
The complete 63-question fixture agrees with upstream selected answers, with 100 stable repeated calls. The release bundle is checked again after packaging.
The exported capacity is 512 total tokens, batch 1, and 32 option slots. Questions/options and state share this budget. The ANE short exports reject over-capacity prompts. Snake uses planner features and a visible optional cycle safety shield; survival is not a claim of unaided game intelligence.
coreml_config.json records shapes, source revisions and per-file SHA256 checksums.
validation.json contains the packaging-time validation. Port fidelity on this regression
suite does not establish general task accuracy or preserved calibration on arbitrary inputs.
Performance and limits
The multilingual ANE L96 FP16 runtime measured 4.98 / 5.31 ms P50 / P95 for one short question on M3 Max; W8 measured 4.88 / 5.23 ms. Whole-system energy per decision improved 2.78× / 3.19×, respectively, against compiled MLX FP16 in that experiment. Those numbers apply to the named short ANE variants, not every bundle, long contexts, or complete Snake frames. The requested 10× improvement was not achieved.
Measurements and scope · General Core ML benchmarks · Snake demo.
Provenance
- Original checkpoint:
convaiinnovations/layaatc5d78730f3493e4fe16d61507ef4b78eef7318cf. - Original weights SHA256:
891102d372688fc2a094dac56a384bc537b87c63f21f9f3dac0be2b7cbc8d86c. - Upstream implementation: NandhaKishorM/laya,
commit
6a5819129eb220570792e417e49723d697efd76f. - Original models and code are by Convai Innovations and contributors, Apache-2.0.
- Independent conversion; not an official Convai Innovations or Apple release.
See LICENSE and NOTICE. Model quality and task/language limitations originate
with Laya; this runtime is an inference port, not a newly trained decision model.
Identity and Version
- Repository
- aac6fef/laya-coreml
- Publisher
- Aac6fef
- Task
- Text classification
- Modality
- Text
- Library
- coreml
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- fff78b2d9750c6b748fe8c90fcbf8bed0a1522a9
- First published
- 2026-09-20
- Last updated
- 2026-09-20
Files and Weights
13 files, 847.2 MB in total. The weights are 2 files totalling 843.6 MB in bin, mlmodel.
Weights2 files · 843.6 MB
Configuration5 files · 10.2 KB
Tokenizer2 files · 3.6 MB
Documentation3 files · 15.2 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.mlpackage/Data/com.apple.CoreML/model.mlmodel | Weights | 872.6 KB | dc6a6383ad4a |
| model.mlpackage/Data/com.apple.CoreML/weights/weight.bin | Weights | 842.8 MB | 5872b9f6530c |
| coreml_config.json | Configuration | 2.1 KB | — |
| encoder/config.json | Configuration | 2.1 KB | — |
| model.mlpackage/Manifest.json | Configuration | 617 B | — |
| rl_agent_config.json | Configuration | 745 B | — |
| validation.json | Configuration | 4.7 KB | — |
| LICENSE | Documentation | 10.2 KB | — |
| NOTICE | Documentation | 1.2 KB | — |
| README.md | Documentation | 3.8 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer/tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer/tokenizer_config.json | Tokenizer | 308 B | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 843.6 MB
Download from Aac6fef
Released by Aac6fef through its official repository on Hugging Face. Read the license.
Built From
- Derived from convaiinnovations/laya
- Quantized from convaiinnovations/laya
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 843.6 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About laya-coreml
Can I use laya-coreml commercially?
Yes. laya-coreml is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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