Instructions to use Mar2Ding/songcomposer_pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mar2Ding/songcomposer_pretrain with Transformers:

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

pipe = pipeline("text-generation", model="Mar2Ding/songcomposer_pretrain", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Mar2Ding/songcomposer_pretrain", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mar2Ding/songcomposer_pretrain with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mar2Ding/songcomposer_pretrain"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mar2Ding/songcomposer_pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mar2Ding/songcomposer_pretrain

SGLang

How to use Mar2Ding/songcomposer_pretrain 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 "Mar2Ding/songcomposer_pretrain" \
    --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": "Mar2Ding/songcomposer_pretrain",
		"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 "Mar2Ding/songcomposer_pretrain" \
        --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": "Mar2Ding/songcomposer_pretrain",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mar2Ding/songcomposer_pretrain with Docker Model Runner:
```
docker model run hf.co/Mar2Ding/songcomposer_pretrain
```

songcomposer_pretrain

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Commit History

Nov 7, 2024

Adding `safetensors` variant of this model⁠

Apr 20, 2024

Update modeling_internlm2.py

Mar 31, 2024

Update README.md

Mar 27, 2024

Update config.json

Mar 21, 2024

Update README.md

Upload tokenizer

Upload InternLM2ForCausalLM

Upload 11 files

Update README.md

Update README.md

Update README.md

Mar 17, 2024

Update README.md

Update README.md

initial commit