How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NovatasticRoScript/Atomight-V2.2-UltraThink-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NovatasticRoScript/Atomight-V2.2-UltraThink-0.5B")
model = AutoModelForCausalLM.from_pretrained("NovatasticRoScript/Atomight-V2.2-UltraThink-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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Uploaded finetuned model

  • Developed by: NovatasticRoScript
  • License: apache-2.0
  • Finetuned from model : unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit

1st teacher model for upcoming Atomight-V2.2, we'll be released soon. Future teacher mdoels: UltraMath, UltraCode, UltraBench, and more..

This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

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