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

f2cd96e

verified ·

1 Parent(s): d040dc2

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -115,7 +115,7 @@ where \\(\mathbf{s}\\), \\(\mathbf{x}\\), \\(\mathbf{x}_1\\), and \\(\odot\\) de
 The acceleration effects of LLMs with different sparsity are displayed as follows. ProSparse, which reaches a high sparsity without performance degradation, can gain the most benefits among all the settings concerned. Refer to Section 4.3 of [paper](https://arxiv.org/pdf/2402.13516.pdf) for more details.
-|        Setting        | Average<br>Sparsity | Activation<br>Recall | Predicted<br>Sparsity | PowerInfer<br>Speed | Speedup<br>to Dense | `S2`<br>Time \\((\downarrow)\\) | Speedup<br>to Dense | `S3`<br/>Time \\((\downarrow)\\) | Speedup<br/>to Dense |
 | :-------------------: | :-----------------: | :------------------: | :-------------------: | :-----------------: | :-----------------: | :--------------: | :-----------------: | :---------------: | :------------------: |
 | Dense-7B | - | - | - | 3.67 | 1.00 | 90.55 | 1.00 | 82.92 | 1.00 |
 |     ReluLLaMA-7B      |        66.98        |        90.89         |         58.95         |        11.37        | 3.10 |      67.12       |        1.35         |       63.00       |         1.32         |

 The acceleration effects of LLMs with different sparsity are displayed as follows. ProSparse, which reaches a high sparsity without performance degradation, can gain the most benefits among all the settings concerned. Refer to Section 4.3 of [paper](https://arxiv.org/pdf/2402.13516.pdf) for more details.
+|        Setting        | Average<br>Sparsity | Activation<br>Recall | Predicted<br>Sparsity | PowerInfer<br>Speed | Speedup<br>to Dense | `S2`<br>Time | Speedup<br>to Dense | `S3`<br/>Time | Speedup<br/>to Dense |
 | :-------------------: | :-----------------: | :------------------: | :-------------------: | :-----------------: | :-----------------: | :--------------: | :-----------------: | :---------------: | :------------------: |
 | Dense-7B | - | - | - | 3.67 | 1.00 | 90.55 | 1.00 | 82.92 | 1.00 |
 |     ReluLLaMA-7B      |        66.98        |        90.89         |         58.95         |        11.37        | 3.10 |      67.12       |        1.35         |       63.00       |         1.32         |