Libraries Transformers How to use Jackrong/Qwopus3.5-9B-v3.5 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Jackrong/Qwopus3.5-9B-v3.5")
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
{"type": "text", "text": "What animal is on the candy?"}
]
},
]
pipe(text=messages) # Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("Jackrong/Qwopus3.5-9B-v3.5")
model = AutoModelForMultimodalLM.from_pretrained("Jackrong/Qwopus3.5-9B-v3.5")
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
{"type": "text", "text": "What animal is on the candy?"}
]
},
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) Notebooks Google Colab Kaggle Local Apps Settings vLLM How to use Jackrong/Qwopus3.5-9B-v3.5 with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/Qwopus3.5-9B-v3.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Jackrong/Qwopus3.5-9B-v3.5",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Use Docker docker model run hf.co/Jackrong/Qwopus3.5-9B-v3.5 SGLang How to use Jackrong/Qwopus3.5-9B-v3.5 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 "Jackrong/Qwopus3.5-9B-v3.5" \
--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": "Jackrong/Qwopus3.5-9B-v3.5",
"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 "Jackrong/Qwopus3.5-9B-v3.5" \
--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": "Jackrong/Qwopus3.5-9B-v3.5",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' Unsloth Studio How to use Jackrong/Qwopus3.5-9B-v3.5 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL) 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 Jackrong/Qwopus3.5-9B-v3.5 to start chatting Install Unsloth Studio (Windows) 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 Jackrong/Qwopus3.5-9B-v3.5 to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/Qwopus3.5-9B-v3.5 to start chatting Load model with FastModel pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="Jackrong/Qwopus3.5-9B-v3.5",
max_seq_length=2048,
) Docker Model Runner How to use Jackrong/Qwopus3.5-9B-v3.5 with Docker Model Runner:
docker model run hf.co/Jackrong/Qwopus3.5-9B-v3.5 
