Instructions to use yun2022/linux-meta-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use yun2022/linux-meta-llama with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf yun2022/linux-meta-llama # Run inference directly in the terminal: llama cli -hf yun2022/linux-meta-llama
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf yun2022/linux-meta-llama # Run inference directly in the terminal: llama cli -hf yun2022/linux-meta-llama
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf yun2022/linux-meta-llama # Run inference directly in the terminal: ./build/bin/llama cli -hf yun2022/linux-meta-llama
Use Docker
docker model run hf.co/yun2022/linux-meta-llama
- Llama new
- LM Studio
- Jan
- Ollama
How to use yun2022/linux-meta-llama with Ollama:
ollama run hf.co/yun2022/linux-meta-llama
- Unsloth Desktop
- Docker Model Runner
How to use yun2022/linux-meta-llama with Docker Model Runner:
docker model run hf.co/yun2022/linux-meta-llama
- Lemonade
How to use yun2022/linux-meta-llama with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull yun2022/linux-meta-llama
Run and chat with the model
lemonade run user.linux-meta-llama-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Upload minimax_h3_i2v_B8E9B6AEE6BEA4E5B2BFE58D.json
Browse files
json/minimax_h3_i2v_B8E9B6AEE6BEA4E5B2BFE58D.json
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|
| 1 |
+
{
|
| 2 |
+
"2": {
|
| 3 |
+
"inputs": {
|
| 4 |
+
"samples": [
|
| 5 |
+
"26",
|
| 6 |
+
1
|
| 7 |
+
],
|
| 8 |
+
"vae": [
|
| 9 |
+
"13",
|
| 10 |
+
0
|
| 11 |
+
]
|
| 12 |
+
},
|
| 13 |
+
"class_type": "VAEDecodeAudio",
|
| 14 |
+
"_meta": {
|
| 15 |
+
"title": "VAE解码(音频)"
|
| 16 |
+
}
|
| 17 |
+
},
|
| 18 |
+
"3": {
|
| 19 |
+
"inputs": {
|
| 20 |
+
"step": 4,
|
| 21 |
+
"sigmas": [
|
| 22 |
+
"8",
|
| 23 |
+
0
|
| 24 |
+
]
|
| 25 |
+
},
|
| 26 |
+
"class_type": "SplitSigmas",
|
| 27 |
+
"_meta": {
|
| 28 |
+
"title": "分离Sigma"
|
| 29 |
+
}
|
| 30 |
+
},
|
| 31 |
+
"4": {
|
| 32 |
+
"inputs": {
|
| 33 |
+
"filename_prefix": "video/MiniMax_H3",
|
| 34 |
+
"format": "auto",
|
| 35 |
+
"format.codec": "auto",
|
| 36 |
+
"codec": "auto",
|
| 37 |
+
"video": [
|
| 38 |
+
"36",
|
| 39 |
+
0
|
| 40 |
+
]
|
| 41 |
+
},
|
| 42 |
+
"class_type": "SaveVideo",
|
| 43 |
+
"_meta": {
|
| 44 |
+
"title": "保存视频"
|
| 45 |
+
}
|
| 46 |
+
},
|
| 47 |
+
"5": {
|
| 48 |
+
"inputs": {
|
| 49 |
+
"sigmas": "0.9231, 0.8780, 0.8000, 0.6316, 0.3158, 0.0000"
|
| 50 |
+
},
|
| 51 |
+
"class_type": "ManualSigmas",
|
| 52 |
+
"_meta": {
|
| 53 |
+
"title": "5 step Sigmas"
|
| 54 |
+
}
|
| 55 |
+
},
|
| 56 |
+
"6": {
|
| 57 |
+
"inputs": {
|
| 58 |
+
"samples": [
|
| 59 |
+
"26",
|
| 60 |
+
1
|
| 61 |
+
],
|
| 62 |
+
"vae": [
|
| 63 |
+
"7",
|
| 64 |
+
0
|
| 65 |
+
]
|
| 66 |
+
},
|
| 67 |
+
"class_type": "VAEDecode",
|
| 68 |
+
"_meta": {
|
| 69 |
+
"title": "VAE解码"
|
| 70 |
+
}
|
| 71 |
+
},
|
| 72 |
+
"7": {
|
| 73 |
+
"inputs": {
|
| 74 |
+
"vae_name": "minimax_h3_video_vae_fp16.safetensors"
|
| 75 |
+
},
|
| 76 |
+
"class_type": "VAELoader",
|
| 77 |
+
"_meta": {
|
| 78 |
+
"title": "加载VAE"
|
| 79 |
+
}
|
| 80 |
+
},
|
| 81 |
+
"8": {
|
| 82 |
+
"inputs": {
|
| 83 |
+
"scheduler": "simple",
|
| 84 |
+
"steps": 8,
|
| 85 |
+
"denoise": 1,
|
| 86 |
+
"model": [
|
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"26",
|
| 530 |
+
1
|
| 531 |
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]
|
| 532 |
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},
|
| 533 |
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"class_type": "LTXVSeparateAVLatent",
|
| 534 |
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"_meta": {
|
| 535 |
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"title": "分离 AV 潜空间"
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| 536 |
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}
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| 537 |
+
},
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| 538 |
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"40": {
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| 539 |
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| 540 |
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| 541 |
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| 542 |
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"42",
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| 543 |
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0
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| 544 |
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]
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| 545 |
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},
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| 546 |
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"class_type": "ModelAttentionBackend",
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| 547 |
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"_meta": {
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| 548 |
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"title": "模型注意力后端"
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| 549 |
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}
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| 550 |
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},
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| 551 |
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"41": {
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| 552 |
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"inputs": {
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| 553 |
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| 554 |
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| 555 |
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| 556 |
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"34",
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| 557 |
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0
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| 558 |
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]
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| 559 |
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},
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| 560 |
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"class_type": "LoraLoaderModelOnly",
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| 561 |
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"_meta": {
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| 562 |
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"title": "LoRA加载器(仅模型)"
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| 563 |
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}
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| 564 |
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| 565 |
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| 566 |
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| 567 |
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| 568 |
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| 569 |
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| 570 |
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| 571 |
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0
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| 572 |
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]
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| 573 |
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},
|
| 574 |
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|
| 575 |
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"_meta": {
|
| 576 |
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"title": "MiniMax H3 Sigma Shift"
|
| 577 |
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}
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| 578 |
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},
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| 579 |
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|
| 580 |
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| 581 |
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| 582 |
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| 583 |
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| 584 |
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|
| 585 |
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|
| 586 |
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|
| 587 |
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|
| 588 |
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},
|
| 589 |
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"class_type": "SeedVR2LoadDiTModel",
|
| 590 |
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"_meta": {
|
| 591 |
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"title": "SeedVR2 DiT (3B)"
|
| 592 |
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}
|
| 593 |
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},
|
| 594 |
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|
| 595 |
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"inputs": {
|
| 596 |
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"model": "ema_vae_fp16.safetensors",
|
| 597 |
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|
| 598 |
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|
| 599 |
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|
| 600 |
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|
| 601 |
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|
| 602 |
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|
| 603 |
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|
| 604 |
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|
| 605 |
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|
| 606 |
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|
| 607 |
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},
|
| 608 |
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|
| 609 |
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|
| 610 |
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"title": "SeedVR2 VAE"
|
| 611 |
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}
|
| 612 |
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},
|
| 613 |
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|
| 614 |
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|
| 615 |
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|
| 616 |
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|
| 617 |
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| 618 |
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|
| 619 |
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|
| 620 |
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|
| 621 |
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|
| 622 |
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|
| 623 |
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|
| 624 |
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|
| 625 |
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|
| 626 |
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"enable_debug": false,
|
| 627 |
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"image": [
|
| 628 |
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"11",
|
| 629 |
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0
|
| 630 |
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],
|
| 631 |
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"dit": [
|
| 632 |
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"43",
|
| 633 |
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0
|
| 634 |
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],
|
| 635 |
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| 636 |
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"44",
|
| 637 |
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0
|
| 638 |
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]
|
| 639 |
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},
|
| 640 |
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"class_type": "SeedVR2VideoUpscaler",
|
| 641 |
+
"_meta": {
|
| 642 |
+
"title": "SeedVR2 放大 (短边1080)"
|
| 643 |
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}
|
| 644 |
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},
|
| 645 |
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"46": {
|
| 646 |
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|
| 647 |
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|
| 648 |
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|
| 649 |
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|
| 650 |
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|
| 651 |
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|
| 652 |
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|
| 653 |
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0
|
| 654 |
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]
|
| 655 |
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},
|
| 656 |
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"class_type": "ImageScale",
|
| 657 |
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"_meta": {
|
| 658 |
+
"title": "缩放到 1920x1080"
|
| 659 |
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}
|
| 660 |
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}
|
| 661 |
+
}
|