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SCOPE-Qwen3-1.7B – AI Model by Machine981 | AlphaNeural AI

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Machine981/
SCOPE-Qwen3-1.7B

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text-generation

text-generation-inference

endpoints_compatible

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Model Name

SCOPE-Qwen3-1.7B-Base

This model is introduced in the paper SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting and is developed by the Longcat Interaction Team.

Model Details

Model Description

Developed by: Longcat Interaction Team
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: Qwen3-1.7B-Base
Paper: arxiv.org/abs/2604.10688

Model Sources

Repository: https://github.com/machine981/SCOPE
Paper: https://arxiv.org/abs/2604.10688

Uses

Direct Use

This model can be used directly for text generation (like MATH reasoning) without any additional fine-tuning.

How to Get Started with the Model

Use the code below to get started with the model:

python

from transformers import AutoTokenizer, AutoModelForCausalLM  # adjust as needed

tokenizer = AutoTokenizer.from_pretrained("Machine981/SCOPE-Qwen3-1.7B")
model = AutoModelForCausalLM.from_pretrained("Machine981/SCOPE-Qwen3-1.7B")

inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))