neuroturk/HYZ-01-0.6B-Base

Open-source base language model pre-trained for Turkish by NeuroTürk

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1. Introduction

HYZ-01-0.6B-Base is the base (pre-trained only) version of the HYZ-01 series developed by NeuroTürk. It is a raw language model that has undergone multi-stage Turkish continual pre-training (CPT) on top of a multilingual foundation, without any instruction tuning or alignment. It is intended for researchers and developers who wish to fine-tune the model for their own tasks.

The model is built on a multilingual foundation covering 119 languages and has been continuously pre-trained with a focus on Turkish. The tokenizer has been extended specifically for Turkish morphological structure and advanced use cases. HYZ-01-0.6B-Base is the lightweight, open-source base version of HYZ-01, developed by NeuroTürk for Turkish.

Note: This is the base pre-trained version. For the instruction-tuned version, see: HYZ-01-0.6B

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2. Model Summary

Continual Pre-Training

• Base model: 4-stage Turkish continual pre-training (CPT) applied on top of a multilingual foundation.
• Training stages include general Turkish web corpus, curated domain data, Wikipedia, and high-quality filtered text.
• Optimization: bfloat16, flash-attention-2, AdamW.

Tokenizer Extension

New special tokens were added to the tokenizer for two purposes:

• Language-structure tokens: To represent Turkish morphological features more efficiently.
• Task and structure tokens: To support structural use cases such as chain-of-thought, code blocks, section markers, and language labels.

The following 20 tokens have been added to the vocabulary and are reserved as infrastructure for future advanced capabilities:

Group	Tokens	Future Use
Brand	<|neuroturk|> <|hyz01|> <|tr|> <|en|>	Model identity and multilingual control
Chain-of-Thought	<|think|> <|/think|> <|step|> <|answer|>	Step-by-step reasoning (CoT)
Dialogue	<|system|> <|user|> <|assistant|> <|end|>	Multi-turn dialogue and role management
Code	<|code|> <|/code|> <|output|> <|error|>	Structured code generation and debugging
Structure	<|title|> <|section|> <|list|> <|note|>	Long-form and structured text generation (reports, articles, etc.)

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3. Model Details

Feature	Value
Total parameters	595,798,016 (~0.6B)
Non-embedding parameters	440,467,456 (~0.44B)
Hidden dimension	1,024
Number of layers	28
Attention heads (Q)	16
Attention heads (KV)	8 (GQA)
Head dimension	128
Activation	SiLU
Normalization	RMSNorm (ε = 1 × 10⁻⁶)
Positional encoding	RoPE (θ = 1,000,000)
Vocabulary size	151,690
Training context length	4,096 tokens
Theoretical max context	32,768 tokens
Precision	BFloat16
VRAM usage (fp16)	~1.11 GB
Disk size	~1.11 GB

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4. Training Details

Setting	Value
Training type	Continual Pre-Training (CPT)
Number of stages	4
Optimization	AdamW
Precision	BFloat16
LR schedule	Cosine with warmup
Context length	4,096 tokens

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5. Usage

Warning: This is a base model. It is not instruction-tuned and will not follow instructions reliably. For conversational or task-oriented use, use the instruction-tuned version: HYZ-01-0.6B

Installation

pip install transformers torch accelerate

Text Generation (Completion)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "neuroturk/HYZ-01-0.6B-Base"

tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True,
    fix_mistral_regex=True 
)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "Yapay zeka, bilgisayar sistemlerinin"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.8,
    top_p=0.95,
    do_sample=True,
    repetition_penalty=1.1,
)


new_tokens = outputs[0][inputs['input_ids'].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

Low VRAM (4-bit Quantization)

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)

tokenizer = AutoTokenizer.from_pretrained(
    "neuroturk/HYZ-01-0.6B-Base",
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    "neuroturk/HYZ-01-0.6B-Base",
    quantization_config=bnb_config,
    device_map="auto",
)

Fine-Tuning with Unsloth

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="neuroturk/HYZ-01-0.6B-Base",
    max_seq_length=4096,
    load_in_4bit=True,
)

model = FastLanguageModel.get_peft_model(
    model,
    r=32,
    lora_alpha=64,
    lora_dropout=0.0,
    target_modules=[
        "q_proj", "k_proj", "v_proj", "o_proj",
        "gate_proj", "up_proj", "down_proj",
    ],
    use_gradient_checkpointing="unsloth",
)

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6. Limitations

• This is a base model without instruction tuning — it will not follow instructions reliably.
• Complex multi-step reasoning may be limited with 0.6B parameters.
• Biases present in the training data may be reflected in outputs.
• Performance drops significantly in languages other than Turkish.
• Human verification of outputs is recommended for critical applications.

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7. Citation

@misc{neuroturk2026hyz01,
  author       = {NeuroTürk},
  title        = {HYZ-01-0.6B: A Lightweight Turkish Base Model},
  year         = 2026,

}

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_{NeuroTürk · HYZ01 · 2026}