Model card: V4.2 is Vime's deployed default, V2.1 the fallback

7d88fe2 verified 13 days ago

12.8 kB

---
language:
- ja
license: gpl-3.0
pipeline_tag: text-generation
tags:
- pytorch
- coreml
- japanese
- causal-lm
- contextual-reranking
- tiny-gpt
- rmsnorm
- swiglu
---
# VimeML Tiny Japanese GPT v4.2

A **15,070,848-parameter** Japanese decoder-only language model for local contextual candidate reranking and short within-sentence continuation. This release contains the selected **V4.2 epoch 20 / step 1,081,942** FP32 inference bundle, its **INT8 block32, FP32-compute Core ML** stateless package, and a matching **chunked KV-cache** Core ML package.

**Status: deployed default.** V4.2 is the default reranking and next-word model of the Vime input method; [V2.1](https://huggingface.co/Voltline/vimeml-tiny-ja-v2.1) ships alongside it as the fallback. V4.2 is not a demonstrated accuracy improvement over V2.1 (see Evaluation).

This is a custom PyTorch model, not a Transformers `AutoModel` integration or an instruction/chat model. SentencePiece, kana retrieval, candidate scoring and search run outside the Core ML graph. The architecture, loader and scoring code are included under `source/`; no `trust_remote_code` or training checkpoint is required.

## Files and identity

| Path | Contents |
| --- | --- |
| `inference/` | Unique FP32 state_dict, config, tokenizer, token manifest and provenance; no optimizer |
| `coreml/ios18-int8-block32/` | Stateless Core ML `.mlpackage` and manifest |
| `coreml/ios18-int8-block32-kvchunk/` | Chunked KV-cache Core ML `.mlpackage` and manifest |
| `source/` | Frozen VimeML Python source, scripts, configurations and requirements |
| `infer.py` | FP32 generation and candidate-scoring wrapper using the V4 bundle loader |
| `evaluation/summary.json` | Aggregate quality, alignment and measurement results; no benchmark texts or traces |
| `RELEASE.json`, `SHA256SUMS.txt`, `verify_release.py` | Release inventory and optional byte verification |

- Source: [Voltline/VimeML](https://github.com/Voltline/VimeML), commit `7f4e87f030f67e5e4a75aed8e795aeda9ce49023`.
- FP32 inference manifest: `05ba14f33f28aa18317adfe007d4ac2a355d38ce8636b294ff37468b2209a41b`.
- Core ML manifests: stateless `0c3b6f3b809aaf3dd2f9ae18626c363f72dd34c59953dea48fe865ebbde9d6da`, chunked KV `fd4bbb6c31fa2f71b2c9be6ab5feb0bbb03b7f51216907c7ff271171d26d1b75`.
- Tokenizer: `296a2bdf4f4862be8bda848ed1cd261f0b0fd43064181fad5a513c6ce73081b5`.
- Original selected training checkpoint: `0e83ea7417b9816b37183044468343b151f4e610da0764c31f1f780c5fa5796d`; the optimizer-bearing checkpoint is not distributed.

These identities are SHA256 digests. Original model manifests remain unchanged; their historical paths describe provenance rather than required installation locations. The release inventory uses the same format as V1 and V2.1.

## Architecture and tokenization

Pre-Norm **RMSNorm/SwiGLU**, bias-free Linear layers, learned absolute positions and causal attention. Vocabulary 8,192; context 128; width 384; 7 layers; 6 heads of dimension 64; SwiGLU width 960; dropout 0; RMSNorm epsilon 1e-5. Input embedding and LM head share one parameter.

SentencePiece 0.2.1 Unigram trained on a shared train-only sample: identity normalization, whitespace preservation, byte fallback and `add_dummy_prefix=false`. PAD/UNK/BOS/EOS IDs are 0/1/2/3. IDs 4–6 are user-defined markers `<CTX>`, `<READ>`, `<OUT>` from an earlier reading-conditioned experiment; **V4.2 never trains on them**. Reject them in input and exclude them from generation (the loader does the latter). **V4 token IDs differ from V1 and V2**; model and tokenizer must be updated together.

## Quick start: downloaded files

Python≥3.11. Install the included requirements after selecting a suitable PyTorch build. FP32 inference does not invoke Apple runtime services.

```sh
python -m pip install -r source/requirements.txt
python verify_release.py
python infer.py --prompt "今日は雨が降っているので、" --max-new-tokens 8
python infer.py --prompt "雨が降っているので、" --candidate "傘を持っていく" --candidate "橋を渡っていく"
```

The candidate command ranks supplied strings; it does not retrieve dictionary candidates or perform kana conversion. The diagnostic wrapper raises on invalid or overlong inputs; the application retains dictionary order when a whole candidate pool cannot be scored.

The V4 loader reads weights with `torch.load(..., weights_only=True)`, restores the shared head, and checks the model configuration, parameter count and tokenizer contract. `verify_release.py` checks the complete inventory and sizes without running a model; `--hashes` additionally checks all file bytes against the recorded digests. Checksums are not a digital signature. Keep `source/`, the tokenizer and inference files together.

## Candidate scoring

Encode `context + candidate` jointly with BOS. Find the common token prefix of the context and all candidate sequences, then sum full-vocabulary log-softmax over the remaining suffix. Do not append candidate-final EOS; ties keep original order. This suffix likelihood is a scoring proxy, not exact string conditional probability. Oversized or invalid pools fall back as a whole, and recall failures stay in evaluation denominators. Deployment uses pure LM scores, with AzooKey retrieving kana candidates.

## Training

Random initialization (seed 1219827974), 20 epochs, 1,081,942 updates, batch 512, BF16 with FP32 weights, fused AdamW (β 0.9/0.95, weight decay 0.1, clip 1.0), cosine learning rate 6e-4→6e-5 after 1,000 warmup updates, and a 30% prefix crop on eligible windows. Total exposure is 12,104,389,994 supervised next-token targets on one NVIDIA L20C GPU.

- **Web text:** the frozen V1/V2 Japanese sentence corpus from nine shards of [FineWeb2-Edu Japanese](https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese) and [Tatoeba](https://tatoeba.org/), re-encoded with the 8K tokenizer: 25,185,368 training sentences, excluding 97 known validation overlaps.
- **Dialogue:** independent utterances from [RealPersonaChat](https://github.com/nu-dialogue/real-persona-chat) and the [Multi-Relational Multi-Party Chat Corpus](https://github.com/nu-dialogue/multi-relational-multi-party-chat-corpus) (477,334 training turns, dialogue-level splits), sampled to about 5% of supervised targets per epoch, which repeats the dialogue pool several times.

The objective is ordinary unmarked next-token loss. Epoch 20 was selected because it has the best complete validation LM BPC, before any IME evaluation. The test split and the historical 1,000-case blind pool were not used.

On the original V1/V2 validation text, **FP32 LM BPC is 3.045152**, versus 3.080191 for V2.1 measured the same way; chat validation BPC is 3.122060. BPC is total NLL including EOS divided by Unicode characters×ln2. Token losses across tokenizers are not compared directly.

## Evaluation

Fixed AzooKey N-best pools are reranked without adding correct answers. The Standard IME tracks use WRIME and JMultiWOZ cases with frozen **AI-expert-reviewed labels, not native-speaker gold**; Legacy is an exposed historical web subset with draft labels; [AJIMEE JWTD_v2](https://github.com/azooKey/AJIMEE-Bench) is public and has been used for error analysis.

| Pool | AzooKey Top-1 | V4.2 FP32 Top-1 / Top-5 | V4.2 INT8 Top-1 / Top-5 | V2.1 FP32 / INT8 Top-1 | Pool coverage |
| --- | ---: | ---: | ---: | ---: | ---: |
| Standard development | 453/728 | 523 / 624 | 522 / 623 | 505 / 506 | 630/728 |
| Fixed reference | 467/725 | 541 / 643 | — | 535 / — | 650/725 |
| Legacy regression | 317/500 | 370 / 442 | 371 / 442 | 374 / 375 | 443/500 |
| AJIMEE | 87/200 | 139 / 161 | 139 / 161 | 144 / 144 | 162/200 |
| Historical development | 111/137 | 123 / 136 | 123 / 136 | 122 / 122 | 136/137 |

The 725-case reference split had already been consumed by an earlier comparison; V4.2 was registered under plan `81512f147d3749ef9a36e61470fce173` before scoring and evaluated once, so it is a fixed reference, **not a fresh blind test**. Against V2.1 it is +6 (40 improvements, 34 regressions; exploratory exact McNemar p=0.56). The Standard development gain (+18, p=0.047) has been reused across experiments and does not replicate at the same size on the reference. Legacy (−4) and AJIMEE (−5) trail V2.1 without statistical significance.

About 61% of all Top-1 flips against V2.1 are notation variants of the same word (kana/kanji/okurigana), where the frozen references list one form; V4.2 leans toward hiragana. AJIMEE losses are mainly encyclopedic homophone errors. Exact-match Top-1 is therefore sensitive to orthographic style. No labels were changed in response to model outputs.

## Core ML variants

**Weight-only symmetric INT8, block size 32; FP32 computation; CPU_ONLY; minimum iOS 18.** No activation quantization or INT8 arithmetic is claimed. 37 matrices are compressed; RMSNorm weights, masks and a converter-inserted head bias constant remain FP32. The stateless package is **17,164,271 bytes**, versus 60,467,999 bytes uncompressed (about 71.6% smaller).
- **Stateless:** `input_ids` INT32 `[1,T]`, 1≤T≤128, right PAD → `logits` FLOAT32 `[1,T,8192]`.
- **Chunked KV (17,193,424 bytes):** `input_ids` INT32 `[1,T]`, `position` INT32 `[1]` (current cache length, position+T≤128), `key_cache`/`value_cache` FLOAT32 `(7,1,6,128,64)` → logits for the new tokens plus updated caches. The caller owns cache snapshots and rollback.

```sh
xcrun coremlcompiler compile coreml/ios18-int8-block32/model.mlpackage compiled-int8 --platform ios --deployment-target 18.0
```

Uncompressed conversions pass frozen logits alignment (maximum absolute error 5.2e-5 stateless and chunked). **INT8 fails strict FP32 logits alignment** (maximum 0.477, mean 0.0495 at T=128; V2.1 INT8 reached 1.009 and 0.1085) at unchanged atol/rtol 3e-4; padding and future-token invariants are exact. The chunked INT8 graph matches the stateless INT8 package within 4.0e-5, and Top-1 differs from FP32 by at most one case per pool. The strict failure is recorded as lossy quantization drift, not numerical equivalence.

## Performance evidence and limits

Apple M3 Mac, Core ML CPU_ONLY, same harness for both models: warm stateless INT8 p50 1.30 ms (T=1) / 6.07 ms (T=128) versus V2.1 0.99 / 5.68 ms; chunked-KV prefill of 119 tokens 6.6 ms versus V2.1 6.1 ms.
iPhone 16 Pro Max (iOS 27.2) **XCTest host process**, optimized build, wired and charging, thermal state `fair`: `predict` T=128 p50 4.90 ms versus V2.1 4.55 ms; candidate rerank p50/p95 25.1/120.9 ms versus 21.3/136.8 ms; footprint peak 44.35 MB versus 49.95 MB. The keyboard extension, typing latency in host apps, cold boot and energy use were **not measured** for V4.2.
## Intended use and limitations
Japanese within-sentence candidate reranking and short continuation, primarily for local input-method experiments. This small non-instruction-tuned model can repeat, drift, truncate or invent facts. It is not a standalone kana-to-kanji converter, general assistant or factual reference. Fixed candidate-pool metrics do not cover all real inputs, and candidate coverage bounds achievable reranking accuracy. Public training overlap, semantic near-duplicates and web-text biases have not been comprehensively audited.
## License and attribution
Since 2026-10-22, the weights, tokenizer, Core ML packages and included VimeML code are released under **GNU GPL v3.0 or later** with an additional permission under GPLv3 section 7 for conveyance through application distribution services such as the App Store. See `LICENSE` (GPLv3), `LICENSE-EXCEPTION` (copyright, additional permission and the source form of model weights) and `NOTICE.md` (training data attribution that applications bundling this model must show). This release was previously published under GPL-2.0-only; copies received under those terms keep them. `source/` is the unchanged snapshot of the recorded source commit, so `source/LICENSE` still shows the GPL v2.0 text that applied there. Training sources and third-party dependencies retain their own terms; they are not relicensed.
V4.2 was trained in part on dialogue text from **RealPersonaChat** and the **Multi-Relational Multi-Party Chat Corpus** by the [nu-dialogue](https://github.com/nu-dialogue) project, both distributed under **CC BY-SA 4.0**. FineWeb2-Edu Japanese's dataset card declares ODC-BY; [Tatoeba text terms](https://tatoeba.org/en/terms_of_use) default to CC-BY 2.0 FR with author attribution. Evaluation sources: AJIMEE (CC-BY-SA 3.0), WRIME (CC-BY-NC-ND 4.0) and JMultiWOZ (CC-BY-SA 4.0). This repository distributes the model and aggregate evaluation metrics only; it contains no corpus, dialogue or benchmark text. Optimizer/RNG state, user inputs, raw device traces, credentials and the separate Vime client are excluded.

	---
	language:
	- ja
	license: gpl-3.0
	pipeline_tag: text-generation
	tags:
	- pytorch
	- coreml
	- japanese
	- causal-lm
	- contextual-reranking
	- tiny-gpt
	- rmsnorm
	- swiglu
	---

	# VimeML Tiny Japanese GPT v4.2

	A 15,070,848-parameter Japanese decoder-only language model for local contextual candidate reranking and short within-sentence continuation. This release contains the selected V4.2 epoch 20 / step 1,081,942 FP32 inference bundle, its INT8 block32, FP32-compute Core ML stateless package, and a matching chunked KV-cache Core ML package.

	Status: deployed default. V4.2 is the default reranking and next-word model of the Vime input method; [V2.1](https://huggingface.co/Voltline/vimeml-tiny-ja-v2.1) ships alongside it as the fallback. V4.2 is not a demonstrated accuracy improvement over V2.1 (see Evaluation).

	This is a custom PyTorch model, not a Transformers `AutoModel` integration or an instruction/chat model. SentencePiece, kana retrieval, candidate scoring and search run outside the Core ML graph. The architecture, loader and scoring code are included under `source/`; no `trust_remote_code` or training checkpoint is required.

	## Files and identity

	\| Path \| Contents \|
	\| --- \| --- \|
	\| `inference/` \| Unique FP32 state_dict, config, tokenizer, token manifest and provenance; no optimizer \|
	\| `coreml/ios18-int8-block32/` \| Stateless Core ML `.mlpackage` and manifest \|
	\| `coreml/ios18-int8-block32-kvchunk/` \| Chunked KV-cache Core ML `.mlpackage` and manifest \|
	\| `source/` \| Frozen VimeML Python source, scripts, configurations and requirements \|
	\| `infer.py` \| FP32 generation and candidate-scoring wrapper using the V4 bundle loader \|
	\| `evaluation/summary.json` \| Aggregate quality, alignment and measurement results; no benchmark texts or traces \|
	\| `RELEASE.json`, `SHA256SUMS.txt`, `verify_release.py` \| Release inventory and optional byte verification \|

	- Source: [Voltline/VimeML](https://github.com/Voltline/VimeML), commit `7f4e87f030f67e5e4a75aed8e795aeda9ce49023`.
	- FP32 inference manifest: `05ba14f33f28aa18317adfe007d4ac2a355d38ce8636b294ff37468b2209a41b`.
	- Core ML manifests: stateless `0c3b6f3b809aaf3dd2f9ae18626c363f72dd34c59953dea48fe865ebbde9d6da`, chunked KV `fd4bbb6c31fa2f71b2c9be6ab5feb0bbb03b7f51216907c7ff271171d26d1b75`.
	- Tokenizer: `296a2bdf4f4862be8bda848ed1cd261f0b0fd43064181fad5a513c6ce73081b5`.
	- Original selected training checkpoint: `0e83ea7417b9816b37183044468343b151f4e610da0764c31f1f780c5fa5796d`; the optimizer-bearing checkpoint is not distributed.

	These identities are SHA256 digests. Original model manifests remain unchanged; their historical paths describe provenance rather than required installation locations. The release inventory uses the same format as V1 and V2.1.

	## Architecture and tokenization

	Pre-Norm RMSNorm/SwiGLU, bias-free Linear layers, learned absolute positions and causal attention. Vocabulary 8,192; context 128; width 384; 7 layers; 6 heads of dimension 64; SwiGLU width 960; dropout 0; RMSNorm epsilon 1e-5. Input embedding and LM head share one parameter.

	SentencePiece 0.2.1 Unigram trained on a shared train-only sample: identity normalization, whitespace preservation, byte fallback and `add_dummy_prefix=false`. PAD/UNK/BOS/EOS IDs are 0/1/2/3. IDs 4–6 are user-defined markers `<CTX>`, `<READ>`, `<OUT>` from an earlier reading-conditioned experiment; V4.2 never trains on them. Reject them in input and exclude them from generation (the loader does the latter). V4 token IDs differ from V1 and V2; model and tokenizer must be updated together.

	## Quick start: downloaded files

	Python≥3.11. Install the included requirements after selecting a suitable PyTorch build. FP32 inference does not invoke Apple runtime services.

	```sh
	python -m pip install -r source/requirements.txt
	python verify_release.py
	python infer.py --prompt "今日は雨が降っているので、" --max-new-tokens 8
	python infer.py --prompt "雨が降っているので、" --candidate "傘を持っていく" --candidate "橋を渡っていく"
	```

	The candidate command ranks supplied strings; it does not retrieve dictionary candidates or perform kana conversion. The diagnostic wrapper raises on invalid or overlong inputs; the application retains dictionary order when a whole candidate pool cannot be scored.

	The V4 loader reads weights with `torch.load(..., weights_only=True)`, restores the shared head, and checks the model configuration, parameter count and tokenizer contract. `verify_release.py` checks the complete inventory and sizes without running a model; `--hashes` additionally checks all file bytes against the recorded digests. Checksums are not a digital signature. Keep `source/`, the tokenizer and inference files together.

	## Candidate scoring

	Encode `context + candidate` jointly with BOS. Find the common token prefix of the context and all candidate sequences, then sum full-vocabulary log-softmax over the remaining suffix. Do not append candidate-final EOS; ties keep original order. This suffix likelihood is a scoring proxy, not exact string conditional probability. Oversized or invalid pools fall back as a whole, and recall failures stay in evaluation denominators. Deployment uses pure LM scores, with AzooKey retrieving kana candidates.

	## Training

	Random initialization (seed 1219827974), 20 epochs, 1,081,942 updates, batch 512, BF16 with FP32 weights, fused AdamW (β 0.9/0.95, weight decay 0.1, clip 1.0), cosine learning rate 6e-4→6e-5 after 1,000 warmup updates, and a 30% prefix crop on eligible windows. Total exposure is 12,104,389,994 supervised next-token targets on one NVIDIA L20C GPU.

	- Web text: the frozen V1/V2 Japanese sentence corpus from nine shards of [FineWeb2-Edu Japanese](https://huggingface.co/datasets/hotchpotch/fineweb-2-edu-japanese) and [Tatoeba](https://tatoeba.org/), re-encoded with the 8K tokenizer: 25,185,368 training sentences, excluding 97 known validation overlaps.
	- Dialogue: independent utterances from [RealPersonaChat](https://github.com/nu-dialogue/real-persona-chat) and the [Multi-Relational Multi-Party Chat Corpus](https://github.com/nu-dialogue/multi-relational-multi-party-chat-corpus) (477,334 training turns, dialogue-level splits), sampled to about 5% of supervised targets per epoch, which repeats the dialogue pool several times.

	The objective is ordinary unmarked next-token loss. Epoch 20 was selected because it has the best complete validation LM BPC, before any IME evaluation. The test split and the historical 1,000-case blind pool were not used.

	On the original V1/V2 validation text, FP32 LM BPC is 3.045152, versus 3.080191 for V2.1 measured the same way; chat validation BPC is 3.122060. BPC is total NLL including EOS divided by Unicode characters×ln2. Token losses across tokenizers are not compared directly.

	## Evaluation

	Fixed AzooKey N-best pools are reranked without adding correct answers. The Standard IME tracks use WRIME and JMultiWOZ cases with frozen AI-expert-reviewed labels, not native-speaker gold; Legacy is an exposed historical web subset with draft labels; [AJIMEE JWTD_v2](https://github.com/azooKey/AJIMEE-Bench) is public and has been used for error analysis.

	\| Pool \| AzooKey Top-1 \| V4.2 FP32 Top-1 / Top-5 \| V4.2 INT8 Top-1 / Top-5 \| V2.1 FP32 / INT8 Top-1 \| Pool coverage \|
	\| --- \| ---: \| ---: \| ---: \| ---: \| ---: \|
	\| Standard development \| 453/728 \| 523 / 624 \| 522 / 623 \| 505 / 506 \| 630/728 \|
	\| Fixed reference \| 467/725 \| 541 / 643 \| — \| 535 / — \| 650/725 \|
	\| Legacy regression \| 317/500 \| 370 / 442 \| 371 / 442 \| 374 / 375 \| 443/500 \|
	\| AJIMEE \| 87/200 \| 139 / 161 \| 139 / 161 \| 144 / 144 \| 162/200 \|
	\| Historical development \| 111/137 \| 123 / 136 \| 123 / 136 \| 122 / 122 \| 136/137 \|

	The 725-case reference split had already been consumed by an earlier comparison; V4.2 was registered under plan `81512f147d3749ef9a36e61470fce173` before scoring and evaluated once, so it is a fixed reference, not a fresh blind test. Against V2.1 it is +6 (40 improvements, 34 regressions; exploratory exact McNemar p=0.56). The Standard development gain (+18, p=0.047) has been reused across experiments and does not replicate at the same size on the reference. Legacy (−4) and AJIMEE (−5) trail V2.1 without statistical significance.

	About 61% of all Top-1 flips against V2.1 are notation variants of the same word (kana/kanji/okurigana), where the frozen references list one form; V4.2 leans toward hiragana. AJIMEE losses are mainly encyclopedic homophone errors. Exact-match Top-1 is therefore sensitive to orthographic style. No labels were changed in response to model outputs.

	## Core ML variants

	Weight-only symmetric INT8, block size 32; FP32 computation; CPU_ONLY; minimum iOS 18. No activation quantization or INT8 arithmetic is claimed. 37 matrices are compressed; RMSNorm weights, masks and a converter-inserted head bias constant remain FP32. The stateless package is 17,164,271 bytes, versus 60,467,999 bytes uncompressed (about 71.6% smaller).

	- Stateless: `input_ids` INT32 `[1,T]`, 1≤T≤128, right PAD → `logits` FLOAT32 `[1,T,8192]`.
	- Chunked KV (17,193,424 bytes): `input_ids` INT32 `[1,T]`, `position` INT32 `[1]` (current cache length, position+T≤128), `key_cache`/`value_cache` FLOAT32 `(7,1,6,128,64)` → logits for the new tokens plus updated caches. The caller owns cache snapshots and rollback.

	```sh
	xcrun coremlcompiler compile coreml/ios18-int8-block32/model.mlpackage compiled-int8 --platform ios --deployment-target 18.0
	```

	Uncompressed conversions pass frozen logits alignment (maximum absolute error 5.2e-5 stateless and chunked). INT8 fails strict FP32 logits alignment (maximum 0.477, mean 0.0495 at T=128; V2.1 INT8 reached 1.009 and 0.1085) at unchanged atol/rtol 3e-4; padding and future-token invariants are exact. The chunked INT8 graph matches the stateless INT8 package within 4.0e-5, and Top-1 differs from FP32 by at most one case per pool. The strict failure is recorded as lossy quantization drift, not numerical equivalence.

	## Performance evidence and limits

	Apple M3 Mac, Core ML CPU_ONLY, same harness for both models: warm stateless INT8 p50 1.30 ms (T=1) / 6.07 ms (T=128) versus V2.1 0.99 / 5.68 ms; chunked-KV prefill of 119 tokens 6.6 ms versus V2.1 6.1 ms.

	iPhone 16 Pro Max (iOS 27.2) XCTest host process, optimized build, wired and charging, thermal state `fair`: `predict` T=128 p50 4.90 ms versus V2.1 4.55 ms; candidate rerank p50/p95 25.1/120.9 ms versus 21.3/136.8 ms; footprint peak 44.35 MB versus 49.95 MB. The keyboard extension, typing latency in host apps, cold boot and energy use were not measured for V4.2.

	## Intended use and limitations

	Japanese within-sentence candidate reranking and short continuation, primarily for local input-method experiments. This small non-instruction-tuned model can repeat, drift, truncate or invent facts. It is not a standalone kana-to-kanji converter, general assistant or factual reference. Fixed candidate-pool metrics do not cover all real inputs, and candidate coverage bounds achievable reranking accuracy. Public training overlap, semantic near-duplicates and web-text biases have not been comprehensively audited.

	## License and attribution

	Since 2026-10-22, the weights, tokenizer, Core ML packages and included VimeML code are released under GNU GPL v3.0 or later with an additional permission under GPLv3 section 7 for conveyance through application distribution services such as the App Store. See `LICENSE` (GPLv3), `LICENSE-EXCEPTION` (copyright, additional permission and the source form of model weights) and `NOTICE.md` (training data attribution that applications bundling this model must show). This release was previously published under GPL-2.0-only; copies received under those terms keep them. `source/` is the unchanged snapshot of the recorded source commit, so `source/LICENSE` still shows the GPL v2.0 text that applied there. Training sources and third-party dependencies retain their own terms; they are not relicensed.

	V4.2 was trained in part on dialogue text from RealPersonaChat and the Multi-Relational Multi-Party Chat Corpus by the [nu-dialogue](https://github.com/nu-dialogue) project, both distributed under CC BY-SA 4.0. FineWeb2-Edu Japanese's dataset card declares ODC-BY; [Tatoeba text terms](https://tatoeba.org/en/terms_of_use) default to CC-BY 2.0 FR with author attribution. Evaluation sources: AJIMEE (CC-BY-SA 3.0), WRIME (CC-BY-NC-ND 4.0) and JMultiWOZ (CC-BY-SA 4.0). This repository distributes the model and aggregate evaluation metrics only; it contains no corpus, dialogue or benchmark text. Optimizer/RNG state, user inputs, raw device traces, credentials and the separate Vime client are excluded.