IlyaGusev/saiga_scored
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How to use wexyyyyyy/Ru-Gemma3-1B with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for wexyyyyyy/Ru-Gemma3-1B to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for wexyyyyyy/Ru-Gemma3-1B to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for wexyyyyyy/Ru-Gemma3-1B to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="wexyyyyyy/Ru-Gemma3-1B",
max_seq_length=2048,
)Это экспериментальная версия модели Gemma 3 1B, дообученная на русскоязычном датасете Saiga-scored. Цель дообучения — подтянуть качество общения на русском языке и адаптировать модель под формат "Assistant/User".
Внимание:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "wexyyyyyy/Ru-Gemma3-1B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{"role": "user", "content": "Привет! Расскажи, почему небо голубое?"}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=512,
temperature=0.6,
top_p=0.9
)
print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))