Automatic Speech Recognition
Transformers
Safetensors
VibeVoice
ASR
Transcriptoin
Diarization
Speech-to-Text
3-bit
Instructions to use beaupi/VibeVoice-ASR-oQ3.5-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beaupi/VibeVoice-ASR-oQ3.5-fp16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="beaupi/VibeVoice-ASR-oQ3.5-fp16")# Load model directly from transformers import VibeVoiceForASRTraining model = VibeVoiceForASRTraining.from_pretrained("beaupi/VibeVoice-ASR-oQ3.5-fp16", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload VibeVoice-ASR-oQ3.5-fp16 via oMLX
Browse files- README.md +121 -0
- config.json +218 -0
- model.safetensors +3 -0
README.md
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---
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language:
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- en # English
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- zh # Chinese
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- es # Spanish
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- pt # Portuguese
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- de # German
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- ja # Japanese
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- ko # Korean
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- fr # French
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- ru # Russian
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- id # Indonesian
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- sv # Swedish
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- it # Italian
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- he # Hebrew
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- nl # Dutch
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- pl # Polish
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- no # Norwegian
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- tr # Turkish
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- th # Thai
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- ar # Arabic
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- hu # Hungarian
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- ca # Catalan
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- cs # Czech
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- da # Danish
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- fa # Persian
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- af # Afrikaans
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- hi # Hindi
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- fi # Finnish
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- et # Estonian
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- aa # Afar
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- el # Greek
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- ro # Romanian
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- vi # Vietnamese
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- bg # Bulgarian
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- is # Icelandic
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- sl # Slovenian
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- sk # Slovak
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- lt # Lithuanian
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- sw # Swahili
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- uk # Ukrainian
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- kl # Kalaallisut
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- lv # Latvian
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- hr # Croatian
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- ne # Nepali
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- sr # Serbian
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- tl # Filipino (ISO 639-1; 常见工程别名: fil)
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- yi # Yiddish
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- ms # Malay
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- ur # Urdu
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- mn # Mongolian
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- hy # Armenian
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- jv # Javanese
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license: mit
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pipeline_tag: automatic-speech-recognition
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tags:
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- ASR
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- Transcriptoin
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- Diarization
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- Speech-to-Text
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library_name: transformers
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---
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## VibeVoice-ASR
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[](https://github.com/microsoft/VibeVoice)
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[](https://aka.ms/vibevoice-asr)
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[](https://arxiv.org/pdf/2601.18184)
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**VibeVoice-ASR** is a unified speech-to-text model designed to handle **60-minute long-form audio** in a single pass, generating structured transcriptions containing **Who (Speaker), When (Timestamps), and What (Content)**, with support for **Customized Hotwords** and over **50 languages**.
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➡️ **Code:** [microsoft/VibeVoice](https://github.com/microsoft/VibeVoice)<br>
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➡️ **Demo:** [VibeVoice-ASR-Demo](https://aka.ms/vibevoice-asr)<br>
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➡️ **Report:** [VibeVoice-ASR Technical Report](https://arxiv.org/pdf/2601.18184)<br>
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➡️ **Finetuning:** [Finetuning](https://github.com/microsoft/VibeVoice/blob/main/finetuning-asr/README.md)<br>
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➡️ **vLLM:** [vLLM-VibeVoice-ASR](https://github.com/microsoft/VibeVoice/blob/main/docs/vibevoice-vllm-asr.md)<br>
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<p align="left">
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<img src="figures/VibeVoice_ASR_archi.png" alt="VibeVoice-ASR Architecture" height="250px">
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</p>
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## 🔥 Key Features
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- **🕒 60-minute Single-Pass Processing**:
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Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to **60 minutes** of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour.
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- **👤 Customized Hotwords**:
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Users can provide customized hotwords (e.g., specific names, technical terms, or background info) to guide the recognition process, significantly improving accuracy on domain-specific content.
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- **📝 Rich Transcription (Who, When, What)**:
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The model jointly performs ASR, diarization, and timestamping, producing a structured output that indicates *who* said *what* and *when*.
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- **🌍 Multilingual & Code-Switching Support**:
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It supports over 50 languages, requires no explicit language setting, and natively handles code-switching within and across utterances. Language distribution can be found [here](#language-distribution).
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## Evaluation
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<p align="center">
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<img src="figures/DER.jpg" alt="DER" width="70%">
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<img src="figures/cpWER.jpg" alt="cpWER" width="70%">
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<img src="figures/tcpWER.jpg" alt="tcpWER" width="70%">
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</p>
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## Installation and Usage
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Please refer to [GitHub README](https://github.com/microsoft/VibeVoice/blob/main/docs/vibevoice-asr.md#installation).
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| 111 |
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## Language Distribution
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| 112 |
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<p align="center">
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<img src="figures/language_distribution_horizontal.png" alt="Language Distribution" width="80%">
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</p>
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## License
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This project is licensed under the MIT License.
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## Contact
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This project was conducted by members of Microsoft Research. We welcome feedback and collaboration from our audience. If you have suggestions, questions, or observe unexpected/offensive behavior in our technology, please contact us at VibeVoice@microsoft.com.
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If the team receives reports of undesired behavior or identifies issues independently, we will update this repository with appropriate mitigations.
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config.json
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| 1 |
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{
|
| 2 |
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"_attn_implementation_autoset": false,
|
| 3 |
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"acoustic_tokenizer_config": {
|
| 4 |
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"causal": true,
|
| 5 |
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"channels": 1,
|
| 6 |
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"conv_bias": true,
|
| 7 |
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"conv_norm": "none",
|
| 8 |
+
"corpus_normalize": 0.0,
|
| 9 |
+
"decoder_depths": null,
|
| 10 |
+
"decoder_n_filters": 32,
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| 11 |
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"decoder_ratios": [
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| 12 |
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8,
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| 13 |
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5,
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| 14 |
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5,
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| 15 |
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4,
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| 16 |
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2,
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| 17 |
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2
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| 18 |
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],
|
| 19 |
+
"disable_last_norm": true,
|
| 20 |
+
"dtype": "bfloat16",
|
| 21 |
+
"encoder_depths": "3-3-3-3-3-3-8",
|
| 22 |
+
"encoder_n_filters": 32,
|
| 23 |
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"encoder_ratios": [
|
| 24 |
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8,
|
| 25 |
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5,
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| 26 |
+
5,
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| 27 |
+
4,
|
| 28 |
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2,
|
| 29 |
+
2
|
| 30 |
+
],
|
| 31 |
+
"fix_std": 0.5,
|
| 32 |
+
"layer_scale_init_value": 1e-06,
|
| 33 |
+
"layernorm": "RMSNorm",
|
| 34 |
+
"layernorm_elementwise_affine": true,
|
| 35 |
+
"layernorm_eps": 1e-05,
|
| 36 |
+
"mixer_layer": "depthwise_conv",
|
| 37 |
+
"model_type": "vibevoice_acoustic_tokenizer",
|
| 38 |
+
"pad_mode": "constant",
|
| 39 |
+
"std_dist_type": "gaussian",
|
| 40 |
+
"vae_dim": 64,
|
| 41 |
+
"weight_init_value": 0.01
|
| 42 |
+
},
|
| 43 |
+
"acoustic_vae_dim": 64,
|
| 44 |
+
"architectures": [
|
| 45 |
+
"VibeVoiceForASRTraining"
|
| 46 |
+
],
|
| 47 |
+
"decoder_config": {
|
| 48 |
+
"attention_dropout": 0.0,
|
| 49 |
+
"dtype": "bfloat16",
|
| 50 |
+
"hidden_act": "silu",
|
| 51 |
+
"hidden_size": 3584,
|
| 52 |
+
"initializer_range": 0.02,
|
| 53 |
+
"intermediate_size": 18944,
|
| 54 |
+
"layer_types": [
|
| 55 |
+
"full_attention",
|
| 56 |
+
"full_attention",
|
| 57 |
+
"full_attention",
|
| 58 |
+
"full_attention",
|
| 59 |
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"full_attention",
|
| 60 |
+
"full_attention",
|
| 61 |
+
"full_attention",
|
| 62 |
+
"full_attention",
|
| 63 |
+
"full_attention",
|
| 64 |
+
"full_attention",
|
| 65 |
+
"full_attention",
|
| 66 |
+
"full_attention",
|
| 67 |
+
"full_attention",
|
| 68 |
+
"full_attention",
|
| 69 |
+
"full_attention",
|
| 70 |
+
"full_attention",
|
| 71 |
+
"full_attention",
|
| 72 |
+
"full_attention",
|
| 73 |
+
"full_attention",
|
| 74 |
+
"full_attention",
|
| 75 |
+
"full_attention",
|
| 76 |
+
"full_attention",
|
| 77 |
+
"full_attention",
|
| 78 |
+
"full_attention",
|
| 79 |
+
"full_attention",
|
| 80 |
+
"full_attention",
|
| 81 |
+
"full_attention",
|
| 82 |
+
"full_attention"
|
| 83 |
+
],
|
| 84 |
+
"max_position_embeddings": 131072,
|
| 85 |
+
"max_window_layers": 28,
|
| 86 |
+
"model_type": "qwen2",
|
| 87 |
+
"num_attention_heads": 28,
|
| 88 |
+
"num_hidden_layers": 28,
|
| 89 |
+
"num_key_value_heads": 4,
|
| 90 |
+
"rms_norm_eps": 1e-06,
|
| 91 |
+
"rope_scaling": null,
|
| 92 |
+
"rope_theta": 1000000.0,
|
| 93 |
+
"sliding_window": null,
|
| 94 |
+
"use_cache": true,
|
| 95 |
+
"use_mrope": false,
|
| 96 |
+
"use_sliding_window": false,
|
| 97 |
+
"vocab_size": 152064
|
| 98 |
+
},
|
| 99 |
+
"diffusion_head_config": {
|
| 100 |
+
"ddpm_batch_mul": 4,
|
| 101 |
+
"ddpm_beta_schedule": "cosine",
|
| 102 |
+
"ddpm_num_inference_steps": 20,
|
| 103 |
+
"ddpm_num_steps": 1000,
|
| 104 |
+
"diffusion_type": "ddpm",
|
| 105 |
+
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|
| 112 |
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|
| 113 |
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|
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|
| 116 |
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|
| 117 |
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|
| 118 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 218 |
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|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
|
|
|
|
|
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|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:2a9aee305c7685742d900881fc4d85bf1c03aa33742cc48f7d2c9b9ca4217d8c
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| 3 |
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size 4338610607
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