Instructions to use AATensorPlay/TensorMind-1.5-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

TensorMind-1.5-preview

File size: 530 Bytes

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{
  "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."
}