How to use Qiskit/mistral-small-3.2-24b-qiskit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qiskit/mistral-small-3.2-24b-qiskit") 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("Qiskit/mistral-small-3.2-24b-qiskit") model = AutoModelForMultimodalLM.from_pretrained("Qiskit/mistral-small-3.2-24b-qiskit", device_map="auto") 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]:]))
How to use Qiskit/mistral-small-3.2-24b-qiskit with vLLM:
# Install vLLM from pip: pip install vllm # Install mistral-common: pip install --upgrade mistral-common # Start the vLLM server: vllm serve "Qiskit/mistral-small-3.2-24b-qiskit" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qiskit/mistral-small-3.2-24b-qiskit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/Qiskit/mistral-small-3.2-24b-qiskit
How to use Qiskit/mistral-small-3.2-24b-qiskit with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Qiskit/mistral-small-3.2-24b-qiskit" \ --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": "Qiskit/mistral-small-3.2-24b-qiskit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
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 "Qiskit/mistral-small-3.2-24b-qiskit" \ --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": "Qiskit/mistral-small-3.2-24b-qiskit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use Qiskit/mistral-small-3.2-24b-qiskit with Docker Model Runner:
