Instructions to use Uni-MoE/Uni-MoE-speech-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Uni-MoE/Uni-MoE-speech-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Uni-MoE/Uni-MoE-speech-base")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("Uni-MoE/Uni-MoE-speech-base")
model = AutoModelForCausalLM.from_pretrained("Uni-MoE/Uni-MoE-speech-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Uni-MoE/Uni-MoE-speech-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Uni-MoE/Uni-MoE-speech-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Uni-MoE/Uni-MoE-speech-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Uni-MoE/Uni-MoE-speech-base

SGLang

How to use Uni-MoE/Uni-MoE-speech-base 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 "Uni-MoE/Uni-MoE-speech-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Uni-MoE/Uni-MoE-speech-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Uni-MoE/Uni-MoE-speech-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Uni-MoE/Uni-MoE-speech-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Uni-MoE/Uni-MoE-speech-base with Docker Model Runner:
```
docker model run hf.co/Uni-MoE/Uni-MoE-speech-base
```

Uni-MoE-speech-base / 8config.json

Uni-MoE

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	{
	"_name_or_path": "/path/to/Uni-MoE-speech-base",
	"architectures": [
	"LlavaLlamaForCausalLM"
	],
	"bos_token_id": 1,
	"eos_token_id": 2,
	"freeze_mm_mlp_adapter": false,
	"freeze_mm_vision_resampler": false,
	"hidden_act": "silu",
	"hidden_size": 4096,
	"image_aspect_ratio": "pad",
	"initializer_range": 0.02,
	"intermediate_size": 11008,
	"language": "English",
	"local_files_only": false,
	"max_length": 4096,
	"max_position_embeddings": 4096,
	"mm_audio_hidden_size": 768,
	"mm_audio_select_feature": "patch",
	"mm_audio_select_layer": 0,
	"mm_audio_tower": "/path/to/whisper-small",
	"mm_hidden_size": 1024,
	"mm_projector_type": "mlp2x_gelu",
	"mm_query_tokens": true,
	"mm_resampler_type": null,
	"mm_use_im_patch_token": false,
	"mm_use_im_start_end": false,
	"mm_vision_select_feature": "patch",
	"mm_vision_select_layer": -2,
	"mm_vision_tower": "/path/to/clip-vit-large-patch14-336",
	"model_type": "llava_llama",
	"moe": true,
	"num_attention_heads": 32,
	"num_experts": 8,
	"num_experts_per_tok": 2,
	"moe_type": "dense",
	"ep_size": 8,
	"capacity_factor": 1.5,
	"lora_r": 8,
	"lora_alpha": 16,
	"image_expert_indices": [2, 3, 4, 6],
	"video_expert_indices": [2, 3, 4, 6],
	"audio_expert_indices": [0, 4, 5, 7],
	"use_fm_block": false,
	"use_flash_attn": false,
	"aux_balance_loss": true,
	"aux_balance_loss_coef": 0.01,
	"aux_multimodal_loss": false,
	"aux_multimodal_loss_coef": 0.01,
	"aux_kl_loss": false,
	"aux_kl_loss_coef": 0.01,
	"lora_dropout": 0.05,
	"num_hidden_layers": 32,
	"num_key_value_heads": 32,
	"pad_token_id": 0,
	"pretraining_tp": 1,
	"query_tokens_size": 50,
	"rms_norm_eps": 1e-05,
	"rope_scaling": null,
	"task": "transcribe",
	"tie_word_embeddings": false,
	"torch_dtype": "float32",
	"transformers_version": "4.31.0",
	"tune_mm_audio_aligner": true,
	"tune_mm_mlp_adapter": false,
	"tune_mm_vision_resampler": false,
	"unfreeze_mm_vision_tower": false,
	"use_cache": true,
	"use_mm_proj": true,
	"vocab_size": 32000
	}