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

A compact, open-weight bilingual language model from TensorPlay AI.

536.9M parameters · BF16 · Qwen3 architecture · Apache-2.0

Overview

TensorMind 1.5 Preview is a lightweight Chinese/English conversational model for local experimentation, instruction-following research, and continued fine-tuning. It ships as a standard Transformers package with Safetensors weights, tokenizer, generation config, and chat template—no custom model code is required.

Highlights

Compact deployment: 536,941,568 parameters in a 1.00 GiB BF16 weight file.
Standard runtime: native Qwen3ForCausalLM support in Transformers.
Chat ready: bundled bilingual chat template with optional non-thinking generation.
Reproducible evaluation: exact score data, vector figures, and rendering source are included.
Open release: Apache-2.0 licensed weights for research and application prototyping.

Quick start

pip install "transformers>=4.51.0" accelerate torch

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AATensorPlay/TensorMind-1.5-preview"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "用三句话解释什么是强化学习。"},
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.7,
        top_p=0.8,
    )

new_tokens = output[0, inputs.input_ids.shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

For deterministic decoding, set do_sample=False and omit temperature and top_p.

Model details

Item	Value
Architecture	`Qwen3ForCausalLM`
Parameters	536,941,568
Precision	BF16
Layers	32
Hidden size	1,024
Intermediate size	4,096
Attention heads / KV heads	16 / 8
Vocabulary	32,768
Position configuration	10,240 positions¹
Attention	Full attention; no sliding window
Weight tying	Enabled
Weight format	Safetensors

¹ max_position_embeddings=10240 is the exported configuration ceiling. This preview has not been evaluated on Needle, RULER, LongBench, or another effective long-context benchmark, so it should not be interpreted as a validated long-context quality claim.

Evaluation

Scores use one matched protocol: lm-eval 0.4.12, SGLang 0.5.14, zero-shot evaluation, full datasets, batch size 48, and fixed seeds.

Model	CMMLU	AGIEval-CN	A-CLUE	C-Eval	TMMLU+	Macro
TensorMind 1.5 Preview	24.8834	32.3822	24.7282	23.2541	24.7272	25.9950

These are standalone release scores. Compare them with other models only when the harness, prompts, datasets, and decoding protocol are matched. The exact values are available in benchmark-results.json, with an additional benchmark scorecard.

Training snapshot

Item	Value
Final alignment packing length	512 tokens
Packed train / eval sequences	8,429 / 422
Optimization steps	50
Per-device train / eval batch	28 / 28
Peak learning rate	5e-7
Precision	BF16
Train loss	2.2760
Eval loss, start → finish	2.16935 → 2.16928

Intended use

Good fits include:

local and edge-oriented language-model experiments;
Chinese/English chat prototypes;
instruction-tuning and alignment research;
reproducible inference, quantization, and serving studies.

This preview is not a safety-tuned production assistant, a high-stakes decision system, or a substitute for domain experts. Evaluate it for your task before deployment.

Limitations

At 0.5B scale, the model can hallucinate, miss instructions, and produce incorrect reasoning or arithmetic.
Benchmark scores are protocol-specific and do not establish superiority over other releases.
Effective long-context quality has not been measured; the position setting is a configuration value only.
Safety, multilingual breadth, tool use, and production robustness have not received comprehensive evaluation.

Checksums

File	SHA-256
`model.safetensors`	`b99f6c0448831a6cb2012b93793dd72c9e64e94deee15fd8c4d8206c056817d9`
`tokenizer.json`	`2b31db3618982e4b4425535b563b4f12f8b62f82114abf3ef67e363a310bc44b`
`chat_template.jinja`	`ed2c59f23b9eb551f51d870b3a2aa7f9e431d424f8e34f27e4012049e85ba814`
`config.json`	`bf6d70f9520550eec8aef7961b3f240a06b69a46e8c2bd273b8cb18d61a494f1`

License

TensorMind 1.5 Preview is released under the Apache License 2.0.

Built by TensorPlay AI

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Safetensors

Model size

0.5B params

Tensor type

BF16