🧠
Kiseki Tts 1.1 model by telecomadm1145
⭐ 39.5
🧠
Model

Kiseki Tts 1.1

by telecomadm1145 telecomadm1145/kiseki-tts-1.1
Free2AITools Nexus Index
39.5
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 5
R: Recency 99
Q: Quality 65
Tech Context
0.38B Params
4.096K Ctx
Vital Performance
46 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39.5 FNI Score
Tiny 0.38B Params
4k Context
46 Downloads
8G GPU ~2GB Est. VRAM
Dense MAMBA2SEQ2SEQFORCONDITIONALGENERATION Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID telecomadm1145/kiseki-tts-1.1
License MIT
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.6GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{telecomadm1145_kiseki_tts_1_1,
  author = {telecomadm1145},
  title = {Kiseki Tts 1.1 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/telecomadm1145/Kiseki-TTS-1.1}},
  note = {Accessed via Free2AITools.}
}
APA Style
telecomadm1145. (2026). Kiseki Tts 1.1 [Model]. Free2AITools. https://huggingface.co/telecomadm1145/Kiseki-TTS-1.1

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run kiseki-tts-1.1
πŸ€— HF Download
huggingface-cli download telecomadm1145/kiseki-tts-1.1
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 5
Recency (R) 99
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Kiseki Tts 1.1: Authority (A:0), Popularity (P:5), Recency (R:99), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Technical Deep Dive

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.

Social Proof

HuggingFace Hub
46Downloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--telecomadm1145--kiseki-tts-1.1
slug
telecomadm1145--kiseki-tts-1.1
source
huggingface
author
telecomadm1145
license
MIT
tags
transformers, safetensors, mamba2_s2s, text2text-generation, text-to-speech, mamba2, state-space-model, japanese, neural-audio-codec, custom_code, ja, dataset:telecomadm1145/test14, dataset:telecomadm1145/test13, base_model:telecomadm1145/kiseki-1.1-0.3b, license:mit, region:us

βš™οΈ Technical Specs

architecture
Mamba2Seq2SeqForConditionalGeneration
params billions
0.38
context length
4,096
pipeline tag
text-to-speech
vram gb
1.6
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
46

Data indexed from public sources. Updated daily.