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Kani TTS ( en)

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+ ---
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+ base_model: nineninesix/kani-tts-450m-0.1-ft
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+ datasets:
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+ - nytopop/expresso-conversational
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+ - nineninesix/expresso-conversational-en-nano-codec-dataset
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+ language:
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+ - en
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+ library_name: transformers
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+ license: apache-2.0
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+ mradermacher:
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+ readme_rev: 1
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+ quantized_by: mradermacher
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+ ---
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+ ## About
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+
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+ <!-- ### quantize_version: 2 -->
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+ <!-- ### output_tensor_quantised: 1 -->
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+ <!-- ### convert_type: hf -->
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+ <!-- ### vocab_type: -->
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+ <!-- ### tags: -->
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+ <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
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+ <!-- ### quants_skip: -->
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+ <!-- ### skip_mmproj: -->
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+ static quants of https://huggingface.co/nineninesix/kani-tts-450m-0.1-ft
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+
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+ <!-- provided-files -->
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+
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+ ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#kani-tts-450m-0.1-ft-GGUF).***
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+
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+ weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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+ ## Usage
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+
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+ If you are unsure how to use GGUF files, refer to one of [TheBloke's
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+ READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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+ more details, including on how to concatenate multi-part files.
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+
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+ ## Provided Quants
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+
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+ (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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+
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+ | Link | Type | Size/GB | Notes |
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+ |:-----|:-----|--------:|:------|
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+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q2_K.gguf) | Q2_K | 0.3 | |
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+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
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+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
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+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
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+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
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+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
51
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
52
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
53
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
54
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.f16.gguf) | f16 | 0.8 | 16 bpw, overkill |
55
+
56
+ Here is a handy graph by ikawrakow comparing some lower-quality quant
57
+ types (lower is better):
58
+
59
+ ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
60
+
61
+ And here are Artefact2's thoughts on the matter:
62
+ https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
63
+
64
+ ## FAQ / Model Request
65
+
66
+ See https://huggingface.co/mradermacher/model_requests for some answers to
67
+ questions you might have and/or if you want some other model quantized.
68
+
69
+ ## Thanks
70
+
71
+ I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
72
+ me use its servers and providing upgrades to my workstation to enable
73
+ this work in my free time.
74
+
75
+ <!-- end -->
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en/kani-tts-450m-0.1-ft/README.md ADDED
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1
+ ---
2
+ license: apache-2.0
3
+ datasets:
4
+ - nytopop/expresso-conversational
5
+ - nineninesix/expresso-conversational-en-nano-codec-dataset
6
+ language:
7
+ - en
8
+ pipeline_tag: text-to-speech
9
+ library_name: transformers
10
+ base_model:
11
+ - nineninesix/kani-tts-450m-0.1-pt
12
+ ---
13
+
14
+ <p>
15
+ <img src="https://www.nineninesix.ai/kitty.png" alt="Logo" width="200" height="200">
16
+ </p>
17
+
18
+ # KaniTTS Finetuned
19
+
20
+ Text-to-Speech (TTS) model designed for high-speed, high-fidelity audio generation.
21
+
22
+ KaniTTS is built on a novel architecture that combines a powerful language model with a highly efficient audio codec, enabling it to deliver exceptional performance for real-time applications.
23
+
24
+
25
+ ## Model Details
26
+
27
+ KaniTTS operates on a two-stage pipeline, leveraging a large foundation model for token generation and a compact, efficient codec for waveform synthesis.
28
+
29
+ The two-stage design of KaniTTS provides a significant advantage in terms of speed and efficiency. The backbone LLM generates a compressed token representation, which is then rapidly expanded into an audio waveform by the NanoCodec. This architecture bypasses the computational overhead associated with generating waveforms directly from large-scale language models, resulting in extremely low latency.
30
+
31
+
32
+ ## Features
33
+ This model trained primarily on English for robust core capabilities and the tokenizer supports these languages: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
34
+
35
+ The base model can be continually pretrained on the multilingual dataset producing high-fidelity audio at sample rates 22kHz.
36
+
37
+ This model powers voice interactions in the modern agentic systems, enabling seamless, human-like conversations.
38
+
39
+ - Model Size: 450M parameters (pretrained version)
40
+ - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt)
41
+
42
+ ## Examples
43
+
44
+ | Text | Audio |
45
+ |---|---|
46
+ | I do believe Marsellus Wallace, MY husband, YOUR boss, told you to take me out and do WHATEVER I WANTED. | <audio controls><source src="https://github.com/nineninesix-ai/kani-tts/raw/refs/heads/main/public/mia.wav" type="audio/mpeg"></audio> |
47
+ | What do we say the the god of death? Not today! | <audio controls><source src="https://github.com/nineninesix-ai/kani-tts/raw/refs/heads/main/public/arya.wav" type="audio/wav"></audio> |
48
+ | What do you call a lawyer with an IQ of 60? Your honor | <audio controls><source src="https://github.com/nineninesix-ai/kani-tts/raw/refs/heads/main/public/saul.wav" type="audio/wav"></audio> |
49
+ | You mean, let me understand this cause, you know maybe it's me, it's a little fucked up maybe, but I'm funny how, I mean funny like I'm a clown, I amuse you? I make you laugh, I'm here to fucking amuse you? | <audio controls><source src="https://github.com/nineninesix-ai/kani-tts/raw/refs/heads/main/public/tommy.wav" type="audio/wav"></audio> |
50
+
51
+
52
+
53
+ ### Sources
54
+
55
+ - Website: [nineninesix.ai](https://www.nineninesix.ai/)
56
+ - GitHub Repo: [https://github.com/nineninesix-ai/kani-tts](https://github.com/nineninesix-ai/kani-tts)
57
+ - Base Model Card on HF: [nineninesix/kani-tts-450m-0.1-pt](https://huggingface.co/nineninesix/kani-tts-450m-0.1-pt)
58
+ - FT Model Card on HuggingFace: [nineninesix/kani-tts-450m-0.2-ft](https://huggingface.co/nineninesix/kani-tts-450m-0.2-ft)
59
+ - Link to HF Space: [nineninesix/KaniTTS](https://huggingface.co/spaces/nineninesix/KaniTTS)
60
+ - Inference Example: [Colab Notebook](https://colab.research.google.com/drive/1mvzGs7jtAMSUz8wvNlL5uFmgFEyAPjDh?usp=sharing)
61
+ - Finetuning Example: [Colab Notebook](https://colab.research.google.com/drive/1oDIPOSHW2kUoP3CGafvh9lM6j03Z-vE6?usp=sharing)
62
+ - Example Dataset for Fine-tuning: [Expresso Conversational](https://huggingface.co/datasets/nineninesix/expresso-conversational-en-nano-codec-dataset)
63
+ - [Waiting List](https://airtable.com/appX2G2TpoRk4M5Bf/pagO2xbIOjiwulPcP/form) for Pro Version
64
+
65
+ ## Recommended Uses
66
+ - Conversational AI: Integrate into chatbots, virtual assistants, or voice-enabled apps for real-time speech output.
67
+ - Edge and Server Deployment: Optimized for low-latency inference on edge devices or affordable servers, enabling scalable, resource-efficient voice applications.
68
+ - Accessibility Tools: Support screen readers or language learning apps with expressive prosody.
69
+ - Research: Fine-tune for domain-specific voices (e.g., accents, emotions) or benchmark against other TTS systems.
70
+
71
+ ## Limitations
72
+ - Performance may vary with finetuned variants, long inputs ( > 2000 tokens) or rare languages/accents.
73
+ - Emotion control is basic; advanced expressivity requires fine-tuning.
74
+ - Trained on public datasets; may inherit biases in prosody or pronunciation from training data.
75
+
76
+
77
+ ## Training Data
78
+
79
+ - Dataset: Curated from LibriTTS, Common Voice and Emilia (~50k hours).
80
+ - Pretrained mostly on English speech for robust core capabilities, with multilingual fine-tuning for supported languages.
81
+ - Metrics: MOS (Mean Opinion Score) 4.3/5 for naturalness; WER (Word Error Rate) < 5% on benchmark texts.
82
+ - Hardware: Pretrained on 8x H200 over 8 hours.
83
+
84
+ ## Inference on Nvidia RTX 5080:
85
+ - **Latency**: < 1s to generate 15 seconds of audio
86
+ - **Memory Usage**: 2GB GPU VRAM
87
+
88
+ > This performance makes KaniTTS suitable for real-time conversational AI applications and low-latency voice synthesis.
89
+
90
+
91
+ ## Tips & Tricks
92
+
93
+ - Language Optimization: For the best results in non-English languages, continually pretrain this model on datasets from your desired language set to improve prosody, accents, and pronunciation accuracy. Additionally, finetune NanoCodec for desired set of languages.
94
+ - Batch Processing: For high-throughput applications, process texts in batches of 8-16 to leverage parallel computation, reducing per-sample latency.
95
+ - **Blackwell GPU Optimization**: This model runs efficiently on NVIDIA's Blackwell architecture GPUs for faster inference and reduced latency in real-time applications.
96
+
97
+
98
+ ## Credits
99
+ - This project was inspired by the works of [Orpheus TTS](https://huggingface.co/canopylabs/orpheus-3b-0.1-pretrained) and [Sesame CSM](https://huggingface.co/sesame/csm-1b).
100
+ - It utilizes the [LiquidAI LFM2 350M](https://huggingface.co/LiquidAI/LFM2-350M) as its core backbone and
101
+ - [Nvidia NanoCodec](https://huggingface.co/nvidia/nemo-nano-codec-22khz-0.6kbps-12.5fps) for efficient audio processing.
102
+
103
+ ## Responsible Use and Prohibited Activities
104
+ The model is designed for ethical and responsible use. The following activities are strictly prohibited:
105
+ - The model may not be used for any illegal purposes or to create content that is harmful, threatening, defamatory, or obscene. This includes, but is not limited to, the generation of hate speech, harassment, or incitement of violence.
106
+ - You may not use the model to generate or disseminate false or misleading information. This includes creating deceptive audio content that impersonates individuals without their consent or misrepresents facts.
107
+ - The model is not to be used for any malicious activities, such as spamming, phishing, or the creation of content intended to deceive or defraud.
108
+
109
+ By using this model, you agree to abide by these restrictions and all applicable laws and regulations.
110
+
111
+
112
+ ## Contact
113
+ Have a question, feedback, or need support? Please fill out our [contact form](https://airtable.com/appX2G2TpoRk4M5Bf/pagO2xbIOjiwulPcP/form) and we'll get back to you as soon as possible.
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+ {{- bos_token -}}
2
+ {%- set system_prompt = "" -%}
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+ {%- set ns = namespace(system_prompt="") -%}
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+ {%- if messages[0]["role"] == "system" -%}
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+ {%- set ns.system_prompt = messages[0]["content"] -%}
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+ {%- set messages = messages[1:] -%}
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+ {%- endif -%}
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+ {%- if tools -%}
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+ {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: <|tool_list_start|>[" -%}
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+ {%- for tool in tools -%}
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+ {%- if tool is not string -%}
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+ {%- set tool = tool | tojson -%}
13
+ {%- endif -%}
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+ {%- set ns.system_prompt = ns.system_prompt + tool -%}
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+ {%- if not loop.last -%}
16
+ {%- set ns.system_prompt = ns.system_prompt + ", " -%}
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+ {%- endif -%}
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+ {%- endfor -%}
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+ {%- set ns.system_prompt = ns.system_prompt + "]<|tool_list_end|>" -%}
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+ {%- endif -%}
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+ {%- if ns.system_prompt -%}
22
+ {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
23
+ {%- endif -%}
24
+ {%- for message in messages -%}
25
+ {{- "<|im_start|>" + message["role"] + "\n" -}}
26
+ {%- set content = message["content"] -%}
27
+ {%- if content is not string -%}
28
+ {%- set content = content | tojson -%}
29
+ {%- endif -%}
30
+ {%- if message["role"] == "tool" -%}
31
+ {%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%}
32
+ {%- endif -%}
33
+ {{- content + "<|im_end|>\n" -}}
34
+ {%- endfor -%}
35
+ {%- if add_generation_prompt -%}
36
+ {{- "<|im_start|>assistant\n" -}}
37
+ {%- endif -%}
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@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "Lfm2ForCausalLM"
4
+ ],
5
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6
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7
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8
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9
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10
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20
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21
+ "dtype": "float32",
22
+ "eos_token_id": 7,
23
+ "hidden_size": 1024,
24
+ "initializer_range": 0.02,
25
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26
+ "layer_types": [
27
+ "conv",
28
+ "conv",
29
+ "full_attention",
30
+ "conv",
31
+ "conv",
32
+ "full_attention",
33
+ "conv",
34
+ "conv",
35
+ "full_attention",
36
+ "conv",
37
+ "full_attention",
38
+ "conv",
39
+ "full_attention",
40
+ "conv",
41
+ "full_attention",
42
+ "conv"
43
+ ],
44
+ "max_position_embeddings": 128000,
45
+ "model_type": "lfm2",
46
+ "norm_eps": 1e-05,
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+ "num_attention_heads": 16,
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+ "num_heads": 16,
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+ "num_hidden_layers": 16,
50
+ "num_key_value_heads": 8,
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+ "pad_token_id": 0,
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+ "rope_theta": 1000000.0,
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54
+ "use_cache": true,
55
+ "use_pos_enc": true,
56
+ "vocab_size": 80539
57
+ }
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@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
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+ "_from_model_config": true,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.56.0"
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+ }
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1
+ ---
2
+ base_model: nineninesix/kani-tts-450m-0.1-pt
3
+ language:
4
+ - en
5
+ library_name: transformers
6
+ license: apache-2.0
7
+ mradermacher:
8
+ readme_rev: 1
9
+ quantized_by: mradermacher
10
+ ---
11
+ ## About
12
+
13
+ <!-- ### quantize_version: 2 -->
14
+ <!-- ### output_tensor_quantised: 1 -->
15
+ <!-- ### convert_type: hf -->
16
+ <!-- ### vocab_type: -->
17
+ <!-- ### tags: -->
18
+ <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
19
+ <!-- ### quants_skip: -->
20
+ <!-- ### skip_mmproj: -->
21
+ static quants of https://huggingface.co/nineninesix/kani-tts-450m-0.1-pt
22
+
23
+ <!-- provided-files -->
24
+
25
+ ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#kani-tts-450m-0.1-pt-GGUF).***
26
+
27
+ weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
28
+ ## Usage
29
+
30
+ If you are unsure how to use GGUF files, refer to one of [TheBloke's
31
+ READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
32
+ more details, including on how to concatenate multi-part files.
33
+
34
+ ## Provided Quants
35
+
36
+ (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
37
+
38
+ | Link | Type | Size/GB | Notes |
39
+ |:-----|:-----|--------:|:------|
40
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q2_K.gguf) | Q2_K | 0.3 | |
41
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
42
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
43
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q3_K_L.gguf) | Q3_K_L | 0.4 | |
44
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.IQ4_XS.gguf) | IQ4_XS | 0.4 | |
45
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q4_K_S.gguf) | Q4_K_S | 0.4 | fast, recommended |
46
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended |
47
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
48
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
49
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q6_K.gguf) | Q6_K | 0.5 | very good quality |
50
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality |
51
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-pt-GGUF/resolve/main/kani-tts-450m-0.1-pt.f16.gguf) | f16 | 1.0 | 16 bpw, overkill |
52
+
53
+ Here is a handy graph by ikawrakow comparing some lower-quality quant
54
+ types (lower is better):
55
+
56
+ ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
57
+
58
+ And here are Artefact2's thoughts on the matter:
59
+ https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
60
+
61
+ ## FAQ / Model Request
62
+
63
+ See https://huggingface.co/mradermacher/model_requests for some answers to
64
+ questions you might have and/or if you want some other model quantized.
65
+
66
+ ## Thanks
67
+
68
+ I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
69
+ me use its servers and providing upgrades to my workstation to enable
70
+ this work in my free time.
71
+
72
+ <!-- end -->
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1
+ ---
2
+ base_model: nineninesix/kani-tts-450m-0.2-ft
3
+ datasets:
4
+ - nytopop/expresso-conversational
5
+ - nineninesix/expresso-conversational-en-nano-codec-dataset
6
+ language:
7
+ - en
8
+ library_name: transformers
9
+ license: apache-2.0
10
+ mradermacher:
11
+ readme_rev: 1
12
+ quantized_by: mradermacher
13
+ ---
14
+ ## About
15
+
16
+ <!-- ### quantize_version: 2 -->
17
+ <!-- ### output_tensor_quantised: 1 -->
18
+ <!-- ### convert_type: hf -->
19
+ <!-- ### vocab_type: -->
20
+ <!-- ### tags: -->
21
+ <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
22
+ <!-- ### quants_skip: -->
23
+ <!-- ### skip_mmproj: -->
24
+ static quants of https://huggingface.co/nineninesix/kani-tts-450m-0.2-ft
25
+
26
+ <!-- provided-files -->
27
+
28
+ ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#kani-tts-450m-0.2-ft-GGUF).***
29
+
30
+ weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
31
+ ## Usage
32
+
33
+ If you are unsure how to use GGUF files, refer to one of [TheBloke's
34
+ READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
35
+ more details, including on how to concatenate multi-part files.
36
+
37
+ ## Provided Quants
38
+
39
+ (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
40
+
41
+ | Link | Type | Size/GB | Notes |
42
+ |:-----|:-----|--------:|:------|
43
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q2_K.gguf) | Q2_K | 0.3 | |
44
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
45
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
46
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
47
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
48
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
49
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
50
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
51
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
52
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
53
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
54
+ | [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.2-ft-GGUF/resolve/main/kani-tts-450m-0.2-ft.f16.gguf) | f16 | 0.8 | 16 bpw, overkill |
55
+
56
+ Here is a handy graph by ikawrakow comparing some lower-quality quant
57
+ types (lower is better):
58
+
59
+ ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
60
+
61
+ And here are Artefact2's thoughts on the matter:
62
+ https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
63
+
64
+ ## FAQ / Model Request
65
+
66
+ See https://huggingface.co/mradermacher/model_requests for some answers to
67
+ questions you might have and/or if you want some other model quantized.
68
+
69
+ ## Thanks
70
+
71
+ I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
72
+ me use its servers and providing upgrades to my workstation to enable
73
+ this work in my free time.
74
+
75
+ <!-- end -->
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