Instructions to use spidyun/kmoshi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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)

# 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
```

kmoshi

File size: 2,713 Bytes

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{
  "architectures": [
    "KmoshiForConditionalGeneration"
  ],
  "attention_dropout": 0.0,
  "audio_encoder_config": {
    "_frame_rate": null,
    "attention_bias": false,
    "attention_dropout": 0.0,
    "audio_channels": 1,
    "codebook_dim": 256,
    "codebook_size": 2048,
    "compress": 2,
    "dilation_growth_rate": 2,
    "dtype": "bfloat16",
    "head_dim": 64,
    "hidden_act": "gelu",
    "hidden_size": 512,
    "initializer_range": 0.02,
    "intermediate_size": 2048,
    "kernel_size": 7,
    "last_kernel_size": 3,
    "layer_scale_initial_scale": 0.01,
    "max_position_embeddings": 8000,
    "model_type": "mimi",
    "norm_eps": 1e-05,
    "num_attention_heads": 8,
    "num_filters": 64,
    "num_hidden_layers": 8,
    "num_key_value_heads": 8,
    "num_quantizers": 32,
    "num_residual_layers": 1,
    "num_semantic_quantizers": 1,
    "pad_mode": "constant",
    "residual_kernel_size": 3,
    "rope_parameters": {
      "rope_theta": 10000.0,
      "rope_type": "default"
    },
    "sampling_rate": 24000,
    "sliding_window": 250,
    "tie_word_embeddings": true,
    "trim_right_ratio": 1.0,
    "upsample_groups": 512,
    "upsampling_ratios": [
      8,
      6,
      5,
      4
    ],
    "use_cache": false,
    "use_causal_conv": true,
    "use_conv_shortcut": false,
    "use_streaming": false,
    "vector_quantization_hidden_dimension": 256
  },
  "audio_vocab_size": 2048,
  "bos_token_id": null,
  "depth_decoder_config": {
    "attention_dropout": 0.0,
    "audio_vocab_size": 2048,
    "bos_token_id": null,
    "dtype": "bfloat16",
    "eos_token_id": null,
    "ffn_dim": 5632,
    "head_dim": 64,
    "hidden_act": "silu",
    "hidden_size": 1024,
    "initializer_range": 0.02,
    "input_size": 4096,
    "max_position_embeddings": 17,
    "model_type": "kmoshi_depth",
    "num_attention_heads": 16,
    "num_codebooks": 16,
    "num_hidden_layers": 6,
    "num_key_value_heads": 16,
    "pad_token_id": null,
    "rms_norm_eps": 1e-08,
    "sliding_window": 8,
    "tie_word_embeddings": false,
    "use_cache": true,
    "vocab_size": 151936
  },
  "dtype": "bfloat16",
  "eos_token_id": null,
  "ffn_dim": 24576,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "max_position_embeddings": 3000,
  "model_type": "kmoshi",
  "num_attention_heads": 32,
  "num_codebooks": 8,
  "num_hidden_layers": 36,
  "num_key_value_heads": 8,
  "pad_token_id": null,
  "rms_norm_eps": 1e-06,
  "rope_parameters": {
    "rope_theta": 1000000.0,
    "rope_type": "default"
  },
  "sliding_window": 3000,
  "tie_word_embeddings": false,
  "transformers_version": "5.14.0.dev0",
  "use_cache": true,
  "vocab_size": 151936
}