Text-to-Speech
Transformers
Safetensors
qwen3_5_text
text-generation
tts
speech-synthesis
voice-cloning
autoregressive
vllm
grpo
Instructions to use nineninesix/gepard-1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nineninesix/gepard-1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="nineninesix/gepard-1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nineninesix/gepard-1.1") model = AutoModelForCausalLM.from_pretrained("nineninesix/gepard-1.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download gepard_techreport.pdf from nineninesix/gepard-1.1: direct link, hf CLI and curl.
- Browser
- Download file 1.88 MB
-
https://huggingface.co/nineninesix/gepard-1.1/resolve/main/gepard_techreport.pdf
- Command line
-
hf download hf://nineninesix/gepard-1.1/gepard_techreport.pdf
-
curl -L -o gepard_techreport.pdf https://huggingface.co/nineninesix/gepard-1.1/resolve/main/gepard_techreport.pdf
1.88 MB
- Xet hash:
- ede562ba157cfe93e251c6cd8b038fe0d899f7fe97722b899d005fb57355ad89
- Size of remote file:
- 1.88 MB
- SHA256:
- 0f8dc388ac3bb836df4dd73a8a7e814ba97fcdd67cb299678248c7be741e77d1
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