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

Libraries

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

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

pipe = pipeline("text-generation", model="TensorMind/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("TensorMind/TensorMind-0.5B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

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

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

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.

Downloads last month: 5

Safetensors

Model size

0.5B params

Tensor type

F32

Evaluation results

C-Eval (0-shot) on C-Eval
self-reported

27.270
CMMLU (0-shot) on CMMLU
self-reported

25.260
A-CLUE (0-shot) on A-CLUE
self-reported

25.430
TMMLU+ (0-shot) on TMMLU+
self-reported

24.960