Instructions to use SparseLLM/ProSparse-MiniCPM-1B-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SparseLLM/ProSparse-MiniCPM-1B-sft with Transformers:

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

pipe = pipeline("text-generation", model="SparseLLM/ProSparse-MiniCPM-1B-sft", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SparseLLM/ProSparse-MiniCPM-1B-sft", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SparseLLM/ProSparse-MiniCPM-1B-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SparseLLM/ProSparse-MiniCPM-1B-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SparseLLM/ProSparse-MiniCPM-1B-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SparseLLM/ProSparse-MiniCPM-1B-sft

SGLang

How to use SparseLLM/ProSparse-MiniCPM-1B-sft 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 "SparseLLM/ProSparse-MiniCPM-1B-sft" \
    --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": "SparseLLM/ProSparse-MiniCPM-1B-sft",
		"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 "SparseLLM/ProSparse-MiniCPM-1B-sft" \
        --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": "SparseLLM/ProSparse-MiniCPM-1B-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SparseLLM/ProSparse-MiniCPM-1B-sft with Docker Model Runner:
```
docker model run hf.co/SparseLLM/ProSparse-MiniCPM-1B-sft
```

Raincleared commited on May 28, 2024

Commit

65b96b0

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@@ -126,7 +126,7 @@ The acceleration effects of LLMs with different sparsity are displayed as follow
 | **ProSparse-13B**\* |        87.97        |        91.02         |         77.93         |        **8.67**         | **4.52** |      55.29       |        2.38         |       67.50       |         1.68         |
 |   **ProSparse-13B**   |        **88.80**        |        **91.11**         |         **78.28**         |          -          | - |      **53.78**       |        **2.44**         |       **66.73**       |         **1.70**         |
-**Notes**: For "Dense" settings, the "Inference Speed" is obtained by [llama.cpp](https://github.com/ggerganov/llama.cpp), and the time for steps (2) and (3) is measured without sparse GPU operators. For other sparse settings, the "Inference Speed" is obtained by [PowerInfer](https://arxiv.org/pdf/2312.12456.pdf), and sparse GPU operators are applied. ProSparse settings with activation threshold shifting and the MiniCPM architecture are not supported by PowerInfer at present.
 ### Citation

 | **ProSparse-13B**\* |        87.97        |        91.02         |         77.93         |        **8.67**         | **4.52** |      55.29       |        2.38         |       67.50       |         1.68         |
 |   **ProSparse-13B**   |        **88.80**        |        **91.11**         |         **78.28**         |          -          | - |      **53.78**       |        **2.44**         |       **66.73**       |         **1.70**         |
+**Notes**: For "Dense" settings, the "Inference Speed" (token/sec) is obtained by [llama.cpp](https://github.com/ggerganov/llama.cpp), and the time (us) for steps (2) and (3) is measured without sparse GPU operators. For other sparse settings, the "Inference Speed" is obtained by [PowerInfer](https://arxiv.org/pdf/2312.12456.pdf), and sparse GPU operators are applied. ProSparse settings with activation threshold shifting and the MiniCPM architecture are not supported by PowerInfer at present.
 ### Citation