Instructions to use AATensorPlay/TensorMind-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AATensorPlay/TensorMind-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="AATensorPlay/TensorMind-0.5B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AATensorPlay/TensorMind-0.5B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AATensorPlay/TensorMind-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AATensorPlay/TensorMind-0.5B"
# 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-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AATensorPlay/TensorMind-0.5B

SGLang

How to use AATensorPlay/TensorMind-0.5B 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-0.5B" \
    --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-0.5B",
		"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-0.5B" \
        --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-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AATensorPlay/TensorMind-0.5B with Docker Model Runner:
```
docker model run hf.co/AATensorPlay/TensorMind-0.5B
```

TensorMind-0.5B / README.md

TensorMind

Add files using upload-large-folder tool

69262b5 verified 5 months ago

preview code

Raw

History Blame Contribute Delete

2.98 kB

	---
	license: mit
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- tensormind
	- causal-lm
	- text-generation
	- chinese
	- custom-code
	language:
	- zh
	- en
	model-index:
	- name: TensorMind
	results:
	- task:
	type: text-generation
	name: Chinese Multiple-Choice Evaluation
	dataset:
	type: custom
	name: C-Eval
	metrics:
	- type: accuracy
	value: 27.27
	name: C-Eval (0-shot)
	- task:
	type: text-generation
	name: Chinese Multiple-Choice Evaluation
	dataset:
	type: custom
	name: CMMLU
	metrics:
	- type: accuracy
	value: 25.26
	name: CMMLU (0-shot)
	- task:
	type: text-generation
	name: Chinese Multiple-Choice Evaluation
	dataset:
	type: custom
	name: A-CLUE
	metrics:
	- type: accuracy
	value: 25.43
	name: A-CLUE (0-shot)
	- task:
	type: text-generation
	name: Chinese Multiple-Choice Evaluation
	dataset:
	type: custom
	name: TMMLU+
	metrics:
	- type: accuracy
	value: 24.96
	name: TMMLU+ (0-shot)
	---

	# TensorMind (0.5B)

	TensorMind is a 536.9M-parameter causal language model for lightweight Chinese/English text generation.

	## Model Details

	- Architecture: Decoder-only Transformer (`TensorMindForCausalLM`)
	- Layers: 32
	- Hidden size: 1024
	- Heads / KV heads: 16 / 8 (GQA)
	- Context length: 32,768
	- Vocab size: 32,768
	- Positional encoding: RoPE
	- Activation: SiLU
	- Parameters: 536,941,568 (~0.5B)

	## Quick Start

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM

	repo_id = "TensorMind/TensorMind"
	tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	repo_id,
	trust_remote_code=True,
	torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
	)

	prompt = "请用三句话介绍一下你自己。"
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=128, do_sample=True, temperature=0.7)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	## Benchmark Snapshot

	Evaluation time: 2026-03-07 00:40 (UTC+8), zero-shot (`n-shot=0`).

	\| Model \| Params \| C-Eval \| CMMLU \| A-CLUE \| TMMLU+ \| AGIEval \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| TensorMind \| 0.5B \| 27.27 \| 25.26 \| 25.43 \| 24.96 \| 33.56 \|


	![TensorMind benchmark table](./assets/compare_table_tensormind.png)


	![TensorMind benchmark radar](./assets/compare_radar_tensormind.png)

	## Intended Use

	- Lightweight chat and text generation
	- Local experimentation and teaching
	- Baseline model for research and fine-tuning

	## Limitations

	- This is a small model and can produce factual errors.
	- Benchmark numbers above are from multiple-choice style evaluations and do not fully represent open-ended generation quality.
	- Outputs may contain bias or unsafe content; apply filtering for production use.

	## License

	MIT License.