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
• X (Twitter): @AMAImediacom
• LinkedIn: Ilia Bolotnikov
• Telegram: @djbionicl
• NOESIS version: v14.7
• Release date: 2026-04

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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)

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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.

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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.

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How to use

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.

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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.

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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.

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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.

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Citation

@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}
}