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 / THIRD_PARTY_NOTICES.md

ddwang2000

Upload EchoChat model components

b6d2bc8 verified about 1 month ago

preview code

Raw History Blame Contribute Delete

586 Bytes

# Third-party notices

Model components include code and materials from Moonshot AI / Kimi-Audio, Qwen and Hugging Face. Original source notices are preserved. The upstream distribution declares MIT for its model-specific materials and Apache 2.0 for code derived from Qwen.

The audio encoder uses OpenAI Whisper materials and Hugging Face implementation components under Apache 2.0. Tokenizer code preserves its NVIDIA copyright notice.

Public Python entry points were renamed for this distribution; numeric weight and token interfaces are retained. No waveform decoder is included.

	# Third-party notices

	Model components include code and materials from Moonshot AI / Kimi-Audio, Qwen and Hugging Face. Original source notices are preserved. The upstream distribution declares MIT for its model-specific materials and Apache 2.0 for code derived from Qwen.

	The audio encoder uses OpenAI Whisper materials and Hugging Face implementation components under Apache 2.0. Tokenizer code preserves its NVIDIA copyright notice.

	Public Python entry points were renamed for this distribution; numeric weight and token interfaces are retained. No waveform decoder is included.