---
library_name: transformers
language:
  - zh
  - en
  - fr
  - pt
  - es
  - ja
  - tr
  - ru
  - ar
  - ko
  - th
  - it
  - de
  - vi
  - ms
  - id
  - tl
  - hi
  - pl
  - cs
  - nl
  - km
  - my
  - fa
  - gu
  - ur
  - te
  - mr
  - he
  - bn
  - ta
  - uk
  - bo
  - kk
  - mn
  - ug
license: other
license_name: tencent-hy-community
license_link: LICENSE.md
base_model:
  - tencent/Hy-MT2-7.5B
pipeline_tag: translation
tags:
  - translation
  - noesis
  - noesis-dub-lm
  - dhcf-fno
  - amaimedia
  - sft
  - qlora
  - bf16
  - hunyuan
  - hy-mt2
  - tencent
  - translation
  - dub-lm
  - live-voice-translate
inference: false
---

## NOESIS / AMAImedia

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

- **Founder:** Ilia Bolotnikov
- **Organization:** AMAImedia.com
- **X (Twitter):** [@AMAImediacom](https://x.com/AMAImediacom)
- **LinkedIn:** [Ilia Bolotnikov](https://www.linkedin.com/in/ilia-bolotnikov)
- **Telegram:** [@djbionicl](https://t.me/djbionicl)
- **NOESIS version:** v16.1
- **Release date:** 2026-08-26

# NOESIS-Hy-MT2-7.5B-BF16

> **NOESIS Professional Multilingual Dubbing Automation Platform**
> framework: DHCF-FNO — *Deterministic Hybrid Control Framework for Frozen Neural Operators*
>
> - **Trained:** 2026-06-05/06
> - **Last updated:** 2026-08-29

> ⚠️ **License regime — Tencent HY Community License Agreement.** Three license gates apply: (1) Territory — EU excluded; (2) 100M MAU cap (separate Tencent license required above); (3) No-KD: outputs cannot be used to train other AI models. Full text in `LICENSE.md`.

---

## Role in NOESIS pipeline

**Primary translation backend** for the NOESIS dubbing pipeline (Stage 3 source→target compress translate). 33 mainland + 5 ethnic + Cantonese languages.

This bundle = **BF16 merged checkpoint** of `tencent/Hy-MT2-7.5B` + NOESIS SFT-LoRA
adapter (`nt312_sft_hymt2_7b`) trained on the NOESIS dubbing corpus.
Format: bf16 dense (master). Disk ≈ 14.0 GB, VRAM peak load ≈ 14.0 GB on RTX 3060.

---

## NOESIS A/B test results — 2026-06-06

Evaluation harness: `scripts/nt318_eval_ab.py` (chat-format prompt + `<|im_end|>` stop +
held-out 30-sample slice of `dub_subscene_MERGED.jsonl`, filtered to common target langs).

| Metric | Upstream NF4 (baseline) | NOESIS SFT (this) | Δ |
|---|---|---|---|
| garbage% (n=30) | 16.7% | **0.0%** | ✅ -16.7 pp |
| iso_fit (dubbing budget) | 0.502 | **0.549** | ✅ +0.047 |
| overlap vs gold (Jaccard) | 0.202 | **0.226** | ✅ +0.024 |


**Verdict:** ✅ no regression + measurable quality improvement on every metric.

### Real example outputs

| Source | Upstream NF4 | NOESIS SFT |
|---|---|---|
| `Свртете се на десно.` | `Translation: "Turn right." |` | **`Turn right here.`** |
| `Можам ли да ги видам?` | `Конеч语可以吗?Can we see them?` | **`I can see them.`** |

Old/new sample dumps: `logs_heal/ab2/{old,new}_hymt2_7b.json`.

---

## Bundle inventory

| File | Description |
|------|-------------|
| `model.safetensors` | weights (BF16, single shard) |
| `config.json` | model architecture configuration |
| `generation_config.json` | default decoding params |
| `tokenizer.json` / `tokenizer_config.json` | tokenizer |
| `chat_template.jinja` | chat template (im_start / im_end markers) |
| `NOESIS_MERGE_MANIFEST.json` | NOESIS provenance (base, adapter, created-at) |
| `README.md` | this file |
| `LICENSE.md` | NOESIS provenance + upstream license terms |

---

## Training details

| Field | Value |
|---|---|
| Method | SFT (Supervised Fine-Tuning) with QLoRA |
| Trainer | `scripts/nt312_train_sft_lora.py` |
| LoRA rank / alpha | 16 / 32 |
| LoRA targets | down_proj, q_proj, v_proj (lean) |
| Max steps | 500, save every 50, --resume capable |
| LR scheduler | warmup_stable_decay (WSD, MiniCPM-style) |
| Optimizer | AdamW 8-bit (paged) |
| Adapter | `LORA/nt312_sft_hymt2_7b/adapter` |
| Dataset | `LORA/Hy-MT2-SFT-100k.jsonl` |
| Sealed rule | `R-SEALED-LORA-RECIPE-V3-NF4` |

---

## Quick Start

### Load
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

path = "NOESIS-Hy-MT2-7.5B-BF16"
tok = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    path, torch_dtype=torch.bfloat16, device_map={"": 0},
    trust_remote_code=True,
)
```

### Translate (chat format — MANDATORY for correct stop)
```python
src_lang, tgt_lang, src = "en", "ru", "Get out of the city, now!"
prompt = (
    f"<|im_start|>user\n"
    f"Переведи реплику дубляжа {src_lang}->{tgt_lang}, только перевод.\n"
    f"SOURCE ({src_lang}): {src}<|im_end|>\n"
    f"<|im_start|>assistant\n"
)
stop_ids = [tok.eos_token_id, tok.convert_tokens_to_ids("<|im_end|>")]
ids = tok(prompt, return_tensors="pt").input_ids.cuda()
g = model.generate(
    ids, max_new_tokens=64, do_sample=False,
    repetition_penalty=1.2,
    eos_token_id=stop_ids, pad_token_id=tok.eos_token_id,
)
print(tok.decode(g[0][ids.shape[1]:], skip_special_tokens=True))
# → "Уберись из города! Немедленно."
```

Convert via `convert_hf_to_gguf.py` (b8808 patched) → llama-quantize q5_k_m/q8_0 (b9523).

---

## NOESIS sealed rules

| Rule | Summary |
|------|---------|
| `R-MT-PRIMARY-7B-NF4-FALLBACK-1.8B` | Hy-MT2 7.5B = primary translator; 1.8B = low-VRAM fallback |
| `R-MT-OWN-NF4-NOT-GGUF` | Translator NF4 is OWN bnb quant, not GGUF (GGUF is a separate dense artifact) |
| `R-SEALED-LORA-RECIPE-V3-NF4` | Canonical SFT-LoRA recipe (rank 16, lean targets, WSD) |
| `R-HF-NAMING-QUANT-ONLY-PRESERVE-UPSTREAM` | Naming convention for trained derivatives |
| `R-NEVER-DELETE-WITHOUT-EXPLICIT-CONSENT` | Bundle must not be deleted without explicit operator instruction |

---

## Upstream

- **Base model:** [`tencent/Hy-MT2-7.5B`](https://huggingface.co/tencent/Hy-MT2-7.5B)
- **License:** Tencent HY Community License Agreement — see `LICENSE.md` for the full text and NOESIS compliance notes.
- **Training corpus:** internal NOESIS dubbing dataset (translation pairs with phoneme budgets + isochrony targets).

NOESIS provenance metadata, bundle inventory, sealed-rule annotations, and DHCF-FNO
integration notes © AMAImedia 2026 (NOESIS DHCF-FNO project).

## MT benchmark — FLORES-200 devtest (2026-06-17)

Real eval (not smoke): n=100 × 4 directions (eng↔rus, eng↔cmn), GPU via resident
`llama-server -ngl 99`. Primary metric **COMET** (wmt22-comet-da, neural — how "best
translator" is judged), plus chrF++ / BLEU / length-ratio. Each model prompted in its own
native format (MT2 = dubbing ChatML "SOURCE (lang): … Только перевод"; 9B = ChatML + no-think).
Data + COMET checkpoint: `D:/models/by_expert/07_MT_TRANSLATION`.

| Model | Size | COMET avg | chrF++ | BLEU | gen tok/s |
|-------|------|-----------|--------|------|-----------|
| Qwopus3.5-9B-Translate Q4 | 5.24 GB | **0.8870** | 50.7 | 22.5 | 49 |
| NOESIS-Hy-MT2-7.5B Q5 | 5.0 GB | 0.8709 | 46.2 | 21.4 | 52 |
| NOESIS-Hy-MT2-1.8B Q8 | 1.78 GB | 0.8481 | 43.9 | 19.1 | 121 |

Per-direction COMET — 9B-Translate wins all 4 (eng-rus .902 / eng-cmn .897 / rus-eng .872 /
cmn-eng .877); MT2-7.5B 2nd, MT2-1.8B 3rd.

**Notes:**
- MT2 is a **dubbing** translator (isochrony): its outputs are shorter (len_ratio ~0.87-0.89
  vs 9B ~1.0) because it compresses to fit speech slots → lower chrF on *literal* FLORES news.
  FLORES does NOT measure MT2's slot-fit strength, so it under-rates MT2 for its actual job.
- 1.8B→7.5B degradation: COMET +0.023, chrF +2.3, BLEU +2.3 — modest; 1.8B is 2.4× faster
  and 2.8× smaller (good lightweight tradeoff).
- BLEU for eng-cmn is low for all (Chinese needs char-tokenization); use chrF++/COMET there.