Toz-1.5 API & Inference Endpoint

Model details

Table

Parameters	604M
Architecture	Qwen3-style decoder (`Qwen3ForCausalLM`)
Hidden size / layers	1152 / 32
Attention	GQA: 9 query heads, 3 KV heads, head dim 128, QK-norm
FFN	SwiGLU, 4096
Normalization	RMSNorm
Positional encoding	RoPE (θ = 10,000)
Vocabulary	32k (tokenizer trained for Turkish)
Context length	1024 tokens
Embeddings	Tied input/output, no biases
Pre-training	15B tokens, JAX, TPU v5e-8

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "TozAI/Toz-1.5"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16) messages = [{"role": "user", "content": "Fotosentez nedir?"}]inputs = tok.apply_chat_template(    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True)out = model.generate(**inputs, max_new_tokens=256)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Chat template:

text

Kullanıcı: {message}Töz: {response}<|endoftext|>

Tool use: calculator

Töz-1.5 does not compute arithmetic itself. It writes the expression inside <hesap>…</hesap> ("hesap" = calculation), an external calculator evaluates it, and the result is inserted inside <sonuc>…</sonuc> ("sonuç" = result). The model then continues and writes the final answer:

text

Kullanıcı: 25*4-10 kaç eder peki?Töz: Hesaplayalım: <hesap>25*4-10</hesap><sonuc>90</sonuc> Sonuç: 90.

How it works:

Generate until the model writes </hesap>, then stop.
Evaluate the expression between <hesap> and </hesap>.
Append \n<sonuc>{result}</sonuc>\n to the text and continue generating.

If you skip the tool loop, the model may fill in <sonuc> on its own, and that value is not reliable, especially for large numbers. Always run the calculator.

python

import ast, operatorfrom fractions import Fractionimport torchfrom transformers import AutoTokenizer, AutoModelForCausalLM model_id = "TozAI/Toz-1.5"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16)  # use torch.float32 on CPU OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul,       ast.Div: operator.truediv, ast.Mod: operator.mod, ast.Pow: operator.pow,       ast.USub: operator.neg, ast.UAdd: operator.pos} def calculate(expr: str) -> str:    """Safe calculator: numbers, + - * / % ** and parentheses only. No eval()."""    expr = expr.replace("×", "*").replace("÷", "/").replace("^", "**")    def ev(n):        if isinstance(n, ast.Expression):            return ev(n.body)        if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)):            return Fraction(str(n.value))        if isinstance(n, ast.BinOp) and type(n.op) in OPS:            a, b = ev(n.left), ev(n.right)            if isinstance(n.op, ast.Pow):                if b.denominator != 1 or abs(b) > 1000:                    raise ValueError("unsupported exponent")                b = int(b)            return OPS[type(n.op)](a, b)        if isinstance(n, ast.UnaryOp) and type(n.op) in OPS:            return OPS[type(n.op)](ev(n.operand))        raise ValueError("unsupported expression")    r = ev(ast.parse(expr, mode="eval"))    return str(r.numerator) if r.denominator == 1 else f"{float(r):.10g}" def chat(question: str, max_new_tokens: int = 256, max_calls: int = 5) -> str:    text = tok.apply_chat_template([{"role": "user", "content": question}],                                   add_generation_prompt=True, tokenize=False)    start = len(text)    for _ in range(max_calls + 1):        ids = tok(text, return_tensors="pt", add_special_tokens=False)        out = model.generate(**ids, max_new_tokens=max_new_tokens, do_sample=False,                             stop_strings=["</hesap>"], tokenizer=tok)        new = tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True)        text += new        if not new.rstrip().endswith("</hesap>"):            break        expr = text.rsplit("<hesap>", 1)[1].rsplit("</hesap>", 1)[0]        try:            result = calculate(expr)        except Exception:            result = "hata"  # "error"        text += f"\n<sonuc>{result}</sonuc>\n"    return text[start:].strip() print(chat("12*7 kaç eder?"))# Hesaplayalım: <hesap>12*7</hesap># <sonuc>84</sonuc>#  Sonuç: 84.

The calculator uses exact integer and fraction arithmetic, so large numbers are not rounded. It never calls eval(). Only numbers, the four operations, %, ** and parentheses are accepted.

Pre-training

Fineweb-2 + wiki filtered, trained 15B token

Supervised fine-tuning (SFT)

The SFT data for Töz-1.5 was built from scratch, independently of Töz-1's, with a focus on higher quality.

Compared to Töz-1: better at math with tool use, more accurate answers, and fewer hallucinations.

No data under non-commercial (NC) licenses was used.

Evaluation

The results below are for the pre-trained model (Töz-1.5-Base). The SFT model was not separately evaluated on these benchmarks. All evaluations are 0-shot and use the same script. The other models were re-evaluated with this script.

Table with columns: Benchmark, Metric, Chance, Töz-1.5-Base (604M), ufakzeka-1-base (182M), Töz-1-Base (195M)
Benchmark	Metric	Chance	Töz-1.5-Base (604M)	ufakzeka-1-base (182M)	Töz-1-Base (195M)
HellaSwag-tr	acc_norm	25%	40.8%	36.8%	35.0%
XCOPA-tr	acc_norm	50%	60.6%	59.8%	55.8%
TurBLiMP	acc	50%	93.6%	90.4%	91.8%
Wikitext-2 (English)	BPB ↓	—	1.377	1.362	1.520
Turkish-MMLU	acc	20%	chance level	chance level	chance level

Notes:

On TurBLiMP, Töz models were given a BOS token while the other HF models were not. This may give Töz a small advantage.
Wikitext-2 is an English benchmark. The Töz models were trained mainly on Turkish and have no advantage there.
Turkish-MMLU does not separate models at this scale (under 1B). All models score at chance level.

Limitations

This is a small model. It can make up facts (hallucinate). Verify its answers on anything important.
Because the pre-training data is news-heavy, responses can sometimes drift into a news-article style.
It was trained only for Turkish. It is weak in other languages and at code generation.
It falls behind larger models on multi-step reasoning and questions that need broad world knowledge.

Citation

bibtex

@misc{toz15,  title  = {Töz-1.5: A 604M-parameter Turkish chat model},  author = {Arnak, Ahmet Emin},  year   = {2026},  url    = {https://huggingface.co/ORG/Toz-1.5}}

Türkçe özet

Töz-1.5, Töz AI'ın sıfırdan eğittiği 604M parametrelik Türkçe sohbet modelidir. Mimarisi Qwen3 tipindedir. 15 milyar Türkçe token ile ön eğitimden geçti, ardından baştan hazırlanmış yeni bir SFT verisiyle sohbet için ince ayar yapıldı. Yukarıdaki sonuçlar ön eğitimli modele aittir ve 0-shot ölçülmüştür. Töz-1.5-Base, HellaSwag-tr'de %40,8, XCOPA-tr'de %60,6, TurBLiMP'te %93,6 aldı ve üç testte de karşılaştırılan modellerin önünde. Küçük bir model olduğu için bilgi uydurabilir, önemli konularda cevaplarını doğrulayın. Lisansı Apache-2.0'dır.

Parameters

604M

Architecture

Qwen3-style decoder (Qwen3ForCausalLM)

Hidden size / layers

1152 / 32

Attention

GQA: 9 query heads, 3 KV heads, head dim 128, QK-norm

FFN

SwiGLU, 4096

Normalization

RMSNorm

Positional encoding

RoPE (θ = 10,000)

Vocabulary

32k (tokenizer trained for Turkish)

Context length

1024 tokens

Embeddings

Tied input/output, no biases

Pre-training

15B tokens, JAX, TPU v5e-8

from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "TozAI/Toz-1.5"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16) messages = [{"role": "user", "content": "Fotosentez nedir?"}]inputs = tok.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", return_dict=True)out = model.generate(**inputs, max_new_tokens=256)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

import ast, operatorfrom fractions import Fractionimport torchfrom transformers import AutoTokenizer, AutoModelForCausalLM model_id = "TozAI/Toz-1.5"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16) # use torch.float32 on CPU OPS = {ast.Add: operator.add, ast.Sub: operator.sub, ast.Mult: operator.mul, ast.Div: operator.truediv, ast.Mod: operator.mod, ast.Pow: operator.pow, ast.USub: operator.neg, ast.UAdd: operator.pos} def calculate(expr: str) -> str: """Safe calculator: numbers, + - * / % ** and parentheses only. No eval().""" expr = expr.replace("×", "*").replace("÷", "/").replace("^", "**") def ev(n): if isinstance(n, ast.Expression): return ev(n.body) if isinstance(n, ast.Constant) and isinstance(n.value, (int, float)): return Fraction(str(n.value)) if isinstance(n, ast.BinOp) and type(n.op) in OPS: a, b = ev(n.left), ev(n.right) if isinstance(n.op, ast.Pow): if b.denominator != 1 or abs(b) > 1000: raise ValueError("unsupported exponent") b = int(b) return OPS[type(n.op)](a, b) if isinstance(n, ast.UnaryOp) and type(n.op) in OPS: return OPS[type(n.op)](ev(n.operand)) raise ValueError("unsupported expression") r = ev(ast.parse(expr, mode="eval")) return str(r.numerator) if r.denominator == 1 else f"{float(r):.10g}" def chat(question: str, max_new_tokens: int = 256, max_calls: int = 5) -> str: text = tok.apply_chat_template([{"role": "user", "content": question}], add_generation_prompt=True, tokenize=False) start = len(text) for _ in range(max_calls + 1): ids = tok(text, return_tensors="pt", add_special_tokens=False) out = model.generate(**ids, max_new_tokens=max_new_tokens, do_sample=False, stop_strings=["</hesap>"], tokenizer=tok) new = tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True) text += new if not new.rstrip().endswith("</hesap>"): break expr = text.rsplit("<hesap>", 1)[1].rsplit("</hesap>", 1)[0] try: result = calculate(expr) except Exception: result = "hata" # "error" text += f"\n<sonuc>{result}</sonuc>\n" return text[start:].strip() print(chat("12*7 kaç eder?"))# Hesaplayalım: <hesap>12*7</hesap># <sonuc>84</sonuc># Sonuç: 84.

Benchmark

Metric

Chance

Töz-1.5-Base (604M)

ufakzeka-1-base (182M)

Töz-1-Base (195M)

HellaSwag-tr

acc_norm

25%

40.8%

36.8%

35.0%

XCOPA-tr

acc_norm

50%

60.6%

59.8%

55.8%

TurBLiMP

acc

50%

93.6%

90.4%

91.8%

Wikitext-2 (English)

BPB ↓

—

1.377

1.362

1.520

Turkish-MMLU

acc

20%

chance level

Toz-1.5

README

Model details

Usage

Tool use: calculator

Pre-training

Supervised fine-tuning (SFT)

Evaluation

Limitations

Citation

Türkçe özet

Explore FriendliAI today

README

Model details

Usage

Tool use: calculator

Pre-training

Supervised fine-tuning (SFT)

Evaluation

Limitations

Citation

Türkçe özet