- Libraries
- Transformers
How to use spidyun/kmoshi with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="spidyun/kmoshi")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("spidyun/kmoshi", device_map="auto") - Moshi
How to use spidyun/kmoshi with Moshi:
# pip install moshi
# Run the interactive web server
python -m moshi.server --hf-repo "spidyun/kmoshi"
# Then open https://localhost:8998 in your browser
# pip install moshi
import torch
from moshi.models import loaders
# Load checkpoint info from HuggingFace
checkpoint = loaders.CheckpointInfo.from_hf_repo("spidyun/kmoshi")
# Load the Mimi audio codec
mimi = checkpoint.get_mimi(device="cuda")
mimi.set_num_codebooks(8)
# Encode audio (24kHz, mono)
wav = torch.randn(1, 1, 24000 * 10) # [batch, channels, samples]
with torch.no_grad():
codes = mimi.encode(wav.cuda())
decoded = mimi.decode(codes) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use spidyun/kmoshi with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "spidyun/kmoshi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "spidyun/kmoshi",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/spidyun/kmoshi
- SGLang
How to use spidyun/kmoshi with SGLang:
Install from pip and serve model
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "spidyun/kmoshi" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "spidyun/kmoshi",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "spidyun/kmoshi" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "spidyun/kmoshi",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' - Docker Model Runner
How to use spidyun/kmoshi with Docker Model Runner:
docker model run hf.co/spidyun/kmoshi
