Instructions to use Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B")
model = AutoModelForCausalLM.from_pretrained("Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B

SGLang

How to use Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B with Docker Model Runner:
```
docker model run hf.co/Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B
```

SCOPE-Deepseek-R1-Distill-Qwen-1.5B / README.md

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metadata

language:
  - en
license: apache-2.0
tags:
  - text-generation
  - nlp
datasets:
  - DeepMath103K
metrics:
  - avg@1 / pass@k
base_model:
  - Deepseek-R1-Distill-Qwen-1.5B
pipeline_tag: text-generation
library_name: transformers
arxiv: 2604.10688

Model Name

SCOPE-Deepseek-R1-Distill-Qwen-1.5B

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: Deepseek-R1-Distill-Qwen-1.5B
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:

from transformers import AutoTokenizer, AutoModelForCausalLM  # adjust as needed

tokenizer = AutoTokenizer.from_pretrained("Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B")
model = AutoModelForCausalLM.from_pretrained("Machine981/SCOPE-Deepseek-R1-Distill-Qwen-1.5B")

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