Text Generation
Transformers
Safetensors
Chinese
English
qwen3
conversational
tensorplay
tensormind
preview
text-generation-inference
Instructions to use AATensorPlay/TensorMind-1.5-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 22,415 Bytes
8ce2c21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 | <?xml version="1.0" encoding="utf-8" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="1008pt" height="567pt" viewBox="0 0 1008 567" xmlns="http://www.w3.org/2000/svg" version="1.1">
<metadata>
<rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<cc:Work>
<dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
<dc:date>2026-08-02T02:24:49.164389</dc:date>
<dc:format>image/svg+xml</dc:format>
<dc:creator>
<cc:Agent>
<dc:title>Matplotlib v3.10.9, https://matplotlib.org/</dc:title>
</cc:Agent>
</dc:creator>
</cc:Work>
</rdf:RDF>
</metadata>
<defs>
<style type="text/css">*{stroke-linejoin: round; stroke-linecap: butt}</style>
</defs>
<g id="figure_1">
<g id="patch_1">
<path d="M 0 567
L 1008 567
L 1008 0
L 0 0
z
" style="fill: #07111f"/>
</g>
<g id="axes_1">
<g id="patch_2">
<path d="M 80.64 307.875701
L 345.24 307.875701
L 345.24 170.1
L 80.64 170.1
z
" style="fill: #0c1a2c"/>
</g>
<g id="patch_3">
<path d="M 80.64 307.875701
L 85.4028 307.875701
L 85.4028 170.1
L 80.64 170.1
z
" clip-path="url(#pc48552ebb2)" style="fill: #267bff"/>
</g>
<g id="matplotlib.axis_1"/>
<g id="matplotlib.axis_2"/>
<g id="text_1">
<text style="font-weight: 700; font-size: 9px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="104.454" y="211.43271" transform="rotate(-0 104.454 211.43271)">CMMLU</text>
</g>
<g id="text_2">
<text style="font-weight: 700; font-size: 27px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #267bff" x="104.454" y="267.920748" transform="rotate(-0 104.454 267.920748)">24.8834</text>
</g>
<g id="text_3">
<text style="font-size: 8.5px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="104.454" y="291.342617" transform="rotate(-0 104.454 291.342617)">accuracy</text>
</g>
</g>
<g id="axes_2">
<g id="patch_4">
<path d="M 371.7 307.875701
L 636.3 307.875701
L 636.3 170.1
L 371.7 170.1
z
" style="fill: #0c1a2c"/>
</g>
<g id="patch_5">
<path d="M 371.7 307.875701
L 376.4628 307.875701
L 376.4628 170.1
L 371.7 170.1
z
" clip-path="url(#p497ce62b42)" style="fill: #2ed7ff"/>
</g>
<g id="matplotlib.axis_3"/>
<g id="matplotlib.axis_4"/>
<g id="text_4">
<text style="font-weight: 700; font-size: 9px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="395.514" y="211.43271" transform="rotate(-0 395.514 211.43271)">AGIEVAL-CN</text>
</g>
<g id="text_5">
<text style="font-weight: 700; font-size: 27px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #2ed7ff" x="395.514" y="267.920748" transform="rotate(-0 395.514 267.920748)">32.3822</text>
</g>
<g id="text_6">
<text style="font-size: 8.5px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="395.514" y="291.342617" transform="rotate(-0 395.514 291.342617)">accuracy</text>
</g>
</g>
<g id="axes_3">
<g id="patch_6">
<path d="M 662.76 307.875701
L 927.36 307.875701
L 927.36 170.1
L 662.76 170.1
z
" style="fill: #0c1a2c"/>
</g>
<g id="patch_7">
<path d="M 662.76 307.875701
L 667.5228 307.875701
L 667.5228 170.1
L 662.76 170.1
z
" clip-path="url(#p0bbd4284d2)" style="fill: #267bff"/>
</g>
<g id="matplotlib.axis_5"/>
<g id="matplotlib.axis_6"/>
<g id="text_7">
<text style="font-weight: 700; font-size: 9px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="686.574" y="211.43271" transform="rotate(-0 686.574 211.43271)">A-CLUE</text>
</g>
<g id="text_8">
<text style="font-weight: 700; font-size: 27px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #267bff" x="686.574" y="267.920748" transform="rotate(-0 686.574 267.920748)">24.7282</text>
</g>
<g id="text_9">
<text style="font-size: 8.5px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="686.574" y="291.342617" transform="rotate(-0 686.574 291.342617)">accuracy</text>
</g>
</g>
<g id="axes_4">
<g id="patch_8">
<path d="M 80.64 464.94
L 345.24 464.94
L 345.24 327.164299
L 80.64 327.164299
z
" style="fill: #0c1a2c"/>
</g>
<g id="patch_9">
<path d="M 80.64 464.94
L 85.4028 464.94
L 85.4028 327.164299
L 80.64 327.164299
z
" clip-path="url(#p9de0fb1870)" style="fill: #ff9d42"/>
</g>
<g id="matplotlib.axis_7"/>
<g id="matplotlib.axis_8"/>
<g id="text_10">
<text style="font-weight: 700; font-size: 9px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="104.454" y="368.497009" transform="rotate(-0 104.454 368.497009)">C-EVAL</text>
</g>
<g id="text_11">
<text style="font-weight: 700; font-size: 27px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #ff9d42" x="104.454" y="424.985047" transform="rotate(-0 104.454 424.985047)">23.2541</text>
</g>
<g id="text_12">
<text style="font-size: 8.5px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="104.454" y="448.406916" transform="rotate(-0 104.454 448.406916)">accuracy</text>
</g>
</g>
<g id="axes_5">
<g id="patch_10">
<path d="M 371.7 464.94
L 636.3 464.94
L 636.3 327.164299
L 371.7 327.164299
z
" style="fill: #0c1a2c"/>
</g>
<g id="patch_11">
<path d="M 371.7 464.94
L 376.4628 464.94
L 376.4628 327.164299
L 371.7 327.164299
z
" clip-path="url(#p635785d11b)" style="fill: #2ed7ff"/>
</g>
<g id="matplotlib.axis_9"/>
<g id="matplotlib.axis_10"/>
<g id="text_13">
<text style="font-weight: 700; font-size: 9px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="395.514" y="368.497009" transform="rotate(-0 395.514 368.497009)">TMMLU+</text>
</g>
<g id="text_14">
<text style="font-weight: 700; font-size: 27px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #2ed7ff" x="395.514" y="424.985047" transform="rotate(-0 395.514 424.985047)">24.7272</text>
</g>
<g id="text_15">
<text style="font-size: 8.5px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="395.514" y="448.406916" transform="rotate(-0 395.514 448.406916)">accuracy</text>
</g>
</g>
<g id="axes_6">
<g id="patch_12">
<path d="M 662.76 464.94
L 927.36 464.94
L 927.36 327.164299
L 662.76 327.164299
z
" style="fill: #10243a"/>
</g>
<g id="patch_13">
<path d="M 662.76 464.94
L 667.5228 464.94
L 667.5228 327.164299
L 662.76 327.164299
z
" clip-path="url(#p13a9443a99)" style="fill: #f4f8ff"/>
</g>
<g id="matplotlib.axis_11"/>
<g id="matplotlib.axis_12"/>
<g id="text_16">
<text style="font-weight: 700; font-size: 9px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="686.574" y="368.497009" transform="rotate(-0 686.574 368.497009)">5-SUITE MACRO</text>
</g>
<g id="text_17">
<text style="font-weight: 700; font-size: 27px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #f4f8ff" x="686.574" y="424.985047" transform="rotate(-0 686.574 424.985047)">25.9950</text>
</g>
<g id="text_18">
<text style="font-size: 8.5px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="686.574" y="448.406916" transform="rotate(-0 686.574 448.406916)">accuracy</text>
</g>
</g>
<g id="AnnotationBbox_1">
<image xlink:href="data:image/png;base64,
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" id="image3d08965066" transform="scale(1 -1) translate(0 -95.4)" x="792.96" y="0.249" width="107.64" height="95.4"/>
</g>
<g id="text_19">
<text style="font-weight: 700; font-size: 13px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #2ed7ff" x="55.44" y="53.865" transform="rotate(-0 55.44 53.865)">TensorMind 1.5 Preview</text>
</g>
<g id="text_20">
<text style="font-weight: 700; font-size: 28px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #f4f8ff" x="55.44" y="89.019" transform="rotate(-0 55.44 89.019)">Benchmark scorecard</text>
</g>
<g id="text_21">
<text style="font-size: 11px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="55.44" y="117.936" transform="rotate(-0 55.44 117.936)">TensorMind 1.5 Preview 路 full-dataset accuracy (%)</text>
</g>
<g id="text_22">
<text style="font-size: 8.5px; font-family: 'DejaVu Sans'; text-anchor: start; fill: #8ea5c3" x="55.44" y="535.815" transform="rotate(-0 55.44 535.815)">Protocol lm-eval 0.4.12 路 SGLang 0.5.14 路 0-shot 路 full datasets 路 batch 48 路 fixed seeds</text>
</g>
<g id="text_23">
<text style="font-size: 8.5px; font-family: 'DejaVu Sans'; text-anchor: end; fill: #8ea5c3" x="962.64" y="535.815" transform="rotate(-0 962.64 535.815)">Accuracy, higher is better</text>
</g>
</g>
<defs>
<clipPath id="pc48552ebb2">
<rect x="80.64" y="170.1" width="264.6" height="137.775701"/>
</clipPath>
<clipPath id="p497ce62b42">
<rect x="371.7" y="170.1" width="264.6" height="137.775701"/>
</clipPath>
<clipPath id="p0bbd4284d2">
<rect x="662.76" y="170.1" width="264.6" height="137.775701"/>
</clipPath>
<clipPath id="p9de0fb1870">
<rect x="80.64" y="327.164299" width="264.6" height="137.775701"/>
</clipPath>
<clipPath id="p635785d11b">
<rect x="371.7" y="327.164299" width="264.6" height="137.775701"/>
</clipPath>
<clipPath id="p13a9443a99">
<rect x="662.76" y="327.164299" width="264.6" height="137.775701"/>
</clipPath>
</defs>
</svg>
|