--- license: apache-2.0 language: - kk - ru - en tags: - qwen3 - kazakh - dare-ties - merge - noesis - dhcf-fno - 600m base_model: - ekitil/ekitil-core-qwen3-600m-kkru-base-v1 - ekitil/ekitil-qwen3-600m-kk - ekitil/ekitil-qwen3-600m-kkru pipeline_tag: text-generation --- # NOESIS-Qwen3-0.6B-Darwin-Ekitil-Sozkz-KZ-BF16 **DARE-TIES merge of three Kazakh/Russian Qwen3-600M models, producing a stronger KK/RU specialist.** 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](https://www.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:** v14.7 - **Release date:** 2026-04 --- ## Model summary | Property | Value | | --- | --- | | Architecture | `Qwen3ForCausalLM` | | Parameters | ~600M | | Hidden size | 1 280 | | Layers | 28 | | Vocab size | 64 000 (custom KK/RU tokenizer) | | Precision | BF16 | | Disk footprint | ~1.3 GB | | Merge method | DARE-TIES (RNG seed 1729) | | Primary language | Kazakh (KK) | | Secondary language | Russian (RU) | --- ## Source models | Model | Role | Weight | Density | | --- | --- | --- | --- | | `ekitil-core-qwen3-600m-kkru-base-v1` | Base (foundation) | -- | -- | | `ekitil-qwen3-600m-kk` (step 4500) | KK specialist | 0.45 | 0.53 | | `ekitil-qwen3-600m-kkru` (step 2000) | KK+RU generalist | 0.35 | 0.53 | The base model provides the foundation architecture. DARE randomly drops (1 - density) of each task vector's weights and rescales survivors, then TIES elects majority-sign directions before summing into the merged model. --- ## Why this merge Each source model captures a different aspect of Kazakh language: - **ekitil-core** -- balanced KK+RU base - **ekitil-kk** -- maximally Kazakh-specialized (4 500 training steps) - **ekitil-kkru** -- bilingual KK+RU (2 000 training steps) DARE-TIES combines all three while suppressing conflicting parameter updates, producing better KK coverage than any single model alone. --- ## How to use ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "AMAImedia/NOESIS-Qwen3-0.6B-Darwin-Ekitil-Sozkz-KZ-BF16" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [{"role": "user", "content": "Salem! Qazaq tilinde soylesesik."}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(text, return_tensors="pt").to(model.device) out = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7) print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)) ``` > **Note:** `vocab_size=64000` -- custom Kazakh tokenizer, not Qwen3 standard 151 936. > Not compatible with standard Qwen3 tokenizer pipelines. --- ## NOESIS context In NOESIS this model provides **Kazakh language domain boost** (KK x10 weight multiplier) for the DUB-LM and CHAT specialists during knowledge distillation. > **KD note:** `vocab_size=64000` is incompatible with NOESIS student vocab (151 936). > Soft label extraction requires a custom cross-vocab projection layer. --- ## Provenance A `merge_provenance.json` file ships alongside the model weights with the full merge trace: source models, weights, densities, DARE-TIES parameters, and RNG seed. --- ## License This model is released under the **Apache License 2.0**, inherited from the upstream Qwen3 base and ekitil model checkpoints. See the `LICENSE` file for the full license text. --- ## Citation ```bibtex @misc{noesis_darwin_kz, title = {NOESIS-Qwen3-0.6B-Darwin-Ekitil-Sozkz-KZ-BF16}, author = {Bolotnikov, Ilia}, year = {2026}, publisher = {AMAImedia}, url = {https://amaimedia.com} } ```