Instructions to use Machine981/SCOPE-Qwen3-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Machine981/SCOPE-Qwen3-1.7B with Transformers:

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

pipe = pipeline("text-generation", model="Machine981/SCOPE-Qwen3-1.7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

vLLM

How to use Machine981/SCOPE-Qwen3-1.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Machine981/SCOPE-Qwen3-1.7B"
# 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-Qwen3-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Machine981/SCOPE-Qwen3-1.7B

SGLang

How to use Machine981/SCOPE-Qwen3-1.7B 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-Qwen3-1.7B" \
    --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-Qwen3-1.7B",
		"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-Qwen3-1.7B" \
        --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-Qwen3-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Machine981/SCOPE-Qwen3-1.7B with Docker Model Runner:
```
docker model run hf.co/Machine981/SCOPE-Qwen3-1.7B
```

Machine981 commited on Apr 27

Commit

4ebd0d8

verified ·

1 Parent(s): 5985f3b

Update README.md

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README.md +2 -16

README.md CHANGED Viewed

@@ -29,7 +29,6 @@ and is developed by the **Longcat Interaction Team**.
 ### Model Description
 - **Developed by:** Longcat Interaction Team
-- **Model type:** Transformer-based language model
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
 - **Finetuned from model:** Qwen3-1.7B-Base
@@ -46,19 +45,6 @@ and is developed by the **Longcat Interaction Team**.
 This model can be used directly for text generation (like MATH reasoning) without any additional fine-tuning.
-### Downstream Use (Optional)
-The model can be fine-tuned on domain-specific data for tasks such as:
-- Math Reasoning
-### Recommendations
-Users should be aware of the above limitations and apply appropriate
-safeguards when deploying this model in production environments.
 ## How to Get Started with the Model
 Use the code below to get started with the model:
@@ -66,8 +52,8 @@ Use the code below to get started with the model:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM  # adjust as needed
-tokenizer = AutoTokenizer.from_pretrained("your-org/your-model-name")
-model = AutoModelForCausalLM.from_pretrained("your-org/your-model-name")
 inputs = tokenizer("Your input text here", return_tensors="pt")
 outputs = model.generate(**inputs)

 ### Model Description
 - **Developed by:** Longcat Interaction Team
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
 - **Finetuned from model:** Qwen3-1.7B-Base
 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)