AuroraSystem/Clary-0.6-0.6B

Aurora Clary 0.6 Safetensors

Файлы

Файл	Описание
config.json	конфиг LLM
model.safetensors	Qwen3-0.6B + LoRA (merged)
tokenizer.json / tokenizer_config.json	токенизатор
projector.safetensors	vision-проектор (768→2048→2048→1024)
clip_vision/	CLIP ViT-B/32 vision encoder

Быстрый старт (text only)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "AuroraSystem/Aurora-Clary-0.6",
    subfolder="merged", torch_dtype="auto", device_map="auto"
)
tok = AutoTokenizer.from_pretrained("AuroraSystem/Aurora-Clary-0.6", subfolder="merged")

prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Напиши факториал на Python"}],
    tokenize=False, add_generation_prompt=True
)
out = model.generate(tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=200)
print(tok.decode(out[0], skip_special_tokens=True))

Быстрый старт (Vision)

Для картинок нужен CLIP + проектор. Архитектура:

Image -> CLIP ViT-B/32 (frozen) -> Projector (768→2048→2048→1024) -> [49 tokens]
Text  -> Qwen3 embeddings                                        -> [tokens]
       [vision_tokens + text_tokens] -> Qwen3-0.6B -> ответ

Возможности

• Текст: инструкции, знания, русский + английский
• Код: генерация Python
• Математика
• Суммаризация (EN + RU)
• Vision: описание изображений, цвета
• Режим /think (Qwen3 thinking)

Ограничения

• Вижн OCR слабый (мелкий текст не читает)
• Системные промпты не обучались
• База — Qwen3-0.6B, потолок соответствует 0.6B-классу

Альтернативные форматы

• GGUF (для llama.cpp / LM Studio / Ollama): AuroraSystem/Clary-0.6-0.6B-GGUF

Лицензия

Apache-2.0. Base — Qwen3-0.6B (Apache-2.0).

---

English

Files

File	Description
config.json	LLM config
model.safetensors	Qwen3-0.6B + LoRA (merged)
tokenizer.json / tokenizer_config.json	tokenizer
projector.safetensors	vision projector (768→2048→2048→1024)
clip_vision/	CLIP ViT-B/32 vision encoder

Quick start (text only)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "AuroraSystem/Aurora-Clary-0.6",
    subfolder="merged", torch_dtype="auto", device_map="auto"
)
tok = AutoTokenizer.from_pretrained("AuroraSystem/Aurora-Clary-0.6", subfolder="merged")

prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Write a Python factorial function"}],
    tokenize=False, add_generation_prompt=True
)
out = model.generate(tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=200)
print(tok.decode(out[0], skip_special_tokens=True))

Quick start (Vision)

For images you need CLIP + projector. Architecture:

Image -> CLIP ViT-B/32 (frozen) -> Projector (768→2048→2048→1024) -> [49 tokens]
Text  -> Qwen3 embeddings                                       -> [tokens]
       [vision_tokens + text_tokens] -> Qwen3-0.6B -> answer

Capabilities

• Text: instructions, knowledge, Russian + English
• Code: Python generation
• Math
• Summarization (EN + RU)
• Vision: image description, colors
• /think mode (Qwen3 thinking)

Limitations

• OCR is weak (small text not recognized)
• System prompts were not trained
• Base is Qwen3-0.6B, ceiling matches 0.6B class

Alternative formats

• GGUF (for llama.cpp / LM Studio / Ollama): AuroraSystem/Clary-0.6-0.6B-GGUF

License

Apache-2.0. Base — Qwen3-0.6B (Apache-2.0).