Text Generation
Transformers
Safetensors
Chinese
English
qwen3
conversational
tensorplay
tensormind
preview
text-generation-inference
Instructions to use AATensorPlay/TensorMind-1.5-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AATensorPlay/TensorMind-1.5-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AATensorPlay/TensorMind-1.5-preview") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AATensorPlay/TensorMind-1.5-preview") model = AutoModelForCausalLM.from_pretrained("AATensorPlay/TensorMind-1.5-preview", device_map="auto") 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 AATensorPlay/TensorMind-1.5-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AATensorPlay/TensorMind-1.5-preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AATensorPlay/TensorMind-1.5-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AATensorPlay/TensorMind-1.5-preview
- SGLang
How to use AATensorPlay/TensorMind-1.5-preview 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 "AATensorPlay/TensorMind-1.5-preview" \ --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": "AATensorPlay/TensorMind-1.5-preview", "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 "AATensorPlay/TensorMind-1.5-preview" \ --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": "AATensorPlay/TensorMind-1.5-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AATensorPlay/TensorMind-1.5-preview with Docker Model Runner:
docker model run hf.co/AATensorPlay/TensorMind-1.5-preview
| { | |
| "model": "TensorMind 1.5 Preview", | |
| "protocol": { | |
| "lm_eval": "0.4.12", | |
| "serving": "SGLang 0.5.14", | |
| "shots": 0, | |
| "dataset_scope": "full", | |
| "batch_size": 48, | |
| "seeds": "fixed", | |
| "metric": "accuracy (%)" | |
| }, | |
| "results": { | |
| "cmmlu": 24.8834, | |
| "agieval_cn": 32.3822, | |
| "a_clue": 24.7282, | |
| "c_eval": 23.2541, | |
| "tmmlu_plus": 24.7272, | |
| "five_suite_macro": 25.9950 | |
| }, | |
| "note": "Scores are reported for this public release only. Compare models only under a matched evaluation protocol." | |
| } | |