ThingAI/ARK-135M-Bilingual

Overview

Quark-135m-Bilingual is a compact bilingual language model designed for Italian and English, built entirely from scratch by ThingsAI. It represents the second generation of the Quark model family, featuring a custom bilingual BPE tokenizer and a modern transformer architecture.

This is the base pretrained model. An SFT (instruction-tuned) version trained on bilingual conversational data is available for chat applications.

Model Details

Architecture

Quark-135m follows a SmolLM-inspired design optimized for efficiency at small scale:

Training

Pretraining Data

Quark-135m v0.2 was pretrained on 15.7B tokens from a curated bilingual mix:

Chat Format

The model uses a simple chat template:

<|user|>
{user message}
<|end|>
<|assistant|>
{model response}
<|end|>

Tokenizer

Quark-135m v0.2 uses a custom bilingual BPE tokenizer (ThingAI/QuarkTokenizer) specifically designed for Italian and English:

• —Vocabulary: 65,536 tokens
• —Type: Byte-Pair Encoding (BPE)
• —Languages: Balanced Italian + English coverage
• —Published: ThingAI/QuarkTokenizer

Usage

Loading the Model

Quark uses a custom architecture. To load and run inference:

import torch
import json
from safetensors.torch import load_file
from transformers import AutoTokenizer

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("ThingAI/Quark-135m-v0.2")

# Load model (requires custom architecture classes — see repository)
# Full architecture code available in the model repository

Generation Example

prompt = "<|user|>\nCos'è l'intelligenza artificiale?\n<|end|>\n<|assistant|>\n"
ids = tokenizer.encode(prompt, return_tensors="pt").to("cuda")

# Token-by-token generation with sampling
with torch.no_grad():
    for _ in range(200):
        logits = model(ids)[:, -1, :] / 0.7  # temperature
        topk = torch.topk(logits, 40)
        probs = torch.softmax(topk.values, -1)
        idx = topk.indices.gather(-1, torch.multinomial(probs, 1))
        ids = torch.cat([ids, idx], -1)
        if idx.item() == tokenizer.eos_token_id:
            break

print(tokenizer.decode(ids[0], skip_special_tokens=False))

Limitations

• —Scale: At 135M parameters, the model has limited factual knowledge and reasoning capacity
• —Hallucination: The model frequently generates plausible but incorrect information
• —Mathematics: Cannot reliably perform arithmetic beyond simple operations
• —Code: Generates syntactically plausible but often non-functional code
• —Vocabulary overhead: The 65k vocabulary consumes ~26% of model parameters in the embedding layer, reducing transformer capacity — a key lesson for v0.3
• —Pretraining plateau: Loss plateaued at ~4.6 due to the vocab/parameter ratio imbalance

Comparison with v0.1

Citation

@misc{quark2026,
  title={Quark: A Family of Compact Bilingual Language Models},
  author={Di Nicola, Michelangelo},
  year={2026},
  publisher={ThingsAI},
  url={https://huggingface.co/ThingAI/Quark-135m-v0.2}
}

Links

• —🌐 ThingsAI Website
• —💬 Things Chat
• —🔤 QuarkTokenizer
• —📊 Open SLM Leaderboard

Built from scratch by ThingsAI 🇮🇹