Instructions to use ddwang2000/EchoChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ddwang2000/EchoChat with Transformers:

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

pipe = pipeline("text-generation", model="ddwang2000/EchoChat", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ddwang2000/EchoChat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ddwang2000/EchoChat", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ddwang2000/EchoChat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ddwang2000/EchoChat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ddwang2000/EchoChat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ddwang2000/EchoChat

SGLang

How to use ddwang2000/EchoChat 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 "ddwang2000/EchoChat" \
    --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": "ddwang2000/EchoChat",
		"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 "ddwang2000/EchoChat" \
        --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": "ddwang2000/EchoChat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ddwang2000/EchoChat with Docker Model Runner:
```
docker model run hf.co/ddwang2000/EchoChat
```

EchoChat / README.md

ddwang2000

Upload EchoChat model components

b6d2bc8 verified about 1 month ago

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---
pretty_name: EchoChat
language:
- en
tags:
- audio
- speech
- conversational
library_name: transformers
license: other
license_name: echochat-component-licenses
license_link: LICENSE
---
# EchoChat

EchoChat is an audio-conditioned conversational model with text and speech-token
outputs. This repository provides the language model, tokenizer, and audio
encoder weights.

## Files and loading

- Root: language model weights, tokenizer, configuration and custom model code.
- `audio_encoder/`: complete audio encoder weights, configuration and its custom implementation.
- `LICENSE`, `licenses/`, `THIRD_PARTY_NOTICES.md`: applicable licenses and notices.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "ddwang2000/EchoChat"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
).to("cuda").eval()
```

The custom audio encoder class is `WhisperEncoder` in
`audio_encoder/modeling_audio_encoder.py`. Instantiate it with the supplied
configuration and strictly load `audio_encoder/model.safetensors`. Its custom
attention implementation requires a compatible CUDA/FlashAttention runtime.

This is a component checkpoint, not a standard Transformers text-generation
pipeline. End-to-end audio use requires compatible feature extraction, prompting,
and text/speech-token generation logic. Waveform synthesis additionally requires
a separately licensed compatible decoder, which is not included here.

## Dependencies

The component configuration targets Transformers 4.51.3. Runtime dependencies
include PyTorch, Transformers, safetensors, tiktoken, einops, and a compatible
FlashAttention build for CUDA execution. Inspect the custom code before enabling
`trust_remote_code=True`.

	---
	pretty_name: EchoChat
	language:
	- en
	tags:
	- audio
	- speech
	- conversational
	library_name: transformers
	license: other
	license_name: echochat-component-licenses
	license_link: LICENSE
	---

	# EchoChat

	EchoChat is an audio-conditioned conversational model with text and speech-token
	outputs. This repository provides the language model, tokenizer, and audio
	encoder weights.

	## Files and loading

	- Root: language model weights, tokenizer, configuration and custom model code.
	- `audio_encoder/`: complete audio encoder weights, configuration and its custom implementation.
	- `LICENSE`, `licenses/`, `THIRD_PARTY_NOTICES.md`: applicable licenses and notices.

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo = "ddwang2000/EchoChat"
	tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	repo, trust_remote_code=True,
	torch_dtype=torch.bfloat16,
	attn_implementation="flash_attention_2",
	).to("cuda").eval()
	```

	The custom audio encoder class is `WhisperEncoder` in
	`audio_encoder/modeling_audio_encoder.py`. Instantiate it with the supplied
	configuration and strictly load `audio_encoder/model.safetensors`. Its custom
	attention implementation requires a compatible CUDA/FlashAttention runtime.

	This is a component checkpoint, not a standard Transformers text-generation
	pipeline. End-to-end audio use requires compatible feature extraction, prompting,
	and text/speech-token generation logic. Waveform synthesis additionally requires
	a separately licensed compatible decoder, which is not included here.

	## Dependencies

	The component configuration targets Transformers 4.51.3. Runtime dependencies
	include PyTorch, Transformers, safetensors, tiktoken, einops, and a compatible
	FlashAttention build for CUDA execution. Inspect the custom code before enabling
	`trust_remote_code=True`.