---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
language:
  - zh
  - en
tags:
  - qwen3
  - conversational
  - text-generation
  - safetensors
  - tensorplay
  - tensormind
  - preview
inference: true
---

<div align="center">
  <img src="./assets/poster.png" alt="TensorMind 1.5 Preview" width="100%" />
</div>

<h1 align="center">TensorMind 1.5 Preview</h1>

<p align="center">
  A compact, open-weight bilingual language model from <strong>TensorPlay AI</strong>.
</p>

<p align="center">
  <code>536.9M parameters</code> · <code>BF16</code> · <code>Qwen3 architecture</code> · <code>Apache-2.0</code>
</p>

## 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

```bash
pip install "transformers>=4.51.0" accelerate torch
```

```python
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

<div align="center">
  <img src="./assets/benchmark-suite.png" alt="TensorMind 1.5 Preview benchmark suite" width="100%" />
</div>

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`](./assets/benchmark-results.json), with an additional [benchmark scorecard](./assets/benchmark-matrix.png).

## 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](./LICENSE).

---

<div align="center">
  <img src="./assets/tensorplay-ai-logo.png" alt="TensorPlay AI" width="140" />
  <br />
  Built by TensorPlay AI
</div>