Instructions to use SparseLLM/prosparse-llama-2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SparseLLM/prosparse-llama-2-7b with Transformers:

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

pipe = pipeline("text-generation", model="SparseLLM/prosparse-llama-2-7b", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SparseLLM/prosparse-llama-2-7b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SparseLLM/prosparse-llama-2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SparseLLM/prosparse-llama-2-7b"
# 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-llama-2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SparseLLM/prosparse-llama-2-7b

SGLang

How to use SparseLLM/prosparse-llama-2-7b 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-llama-2-7b" \
    --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-llama-2-7b",
		"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-llama-2-7b" \
        --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-llama-2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SparseLLM/prosparse-llama-2-7b with Docker Model Runner:
```
docker model run hf.co/SparseLLM/prosparse-llama-2-7b
```

Raincleared commited on Feb 19, 2024

Commit

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@@ -119,7 +119,7 @@ The acceleration effects of LLMs with different sparsity are displayed as follow
 | **ProSparse-13B**\* |        87.97        |        91.02         |         77.93         |        8.67         |      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**: Fixed \\(L_1\\) suffers from severe performance degradation. ProSparse with Activation Threshold Shifting is not supported by PowerInfer. "Time" means the average wall-clock time (us) cost by each step with our sparse GPU operators, and "Speedup" is the speedup ratio to the setting without operators. For reference, the average number of tokens generated by llama.cpp per second is about 3.67 for 7B and 1.92 for 13B. The average time for step (2) and (3) without sparse GPU operators is about **90.55 and 82.92 (us) for 7B, 131.36 and 113.68 (us) for 13B** respectively under all sparsity.
 ### License Disclaimer

 | **ProSparse-13B**\* |        87.97        |        91.02         |         77.93         |        8.67         |      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**: Fixed \\(L_1\\) suffers from severe performance degradation. ProSparse with Activation Threshold Shifting is not supported by PowerInfer. "Time" means the average wall-clock time (us) cost by each step with our sparse GPU operators, and "Speedup" is the speedup ratio to the setting without operators. For reference, the average number of tokens generated by [llama.cpp](https://github.com/ggerganov/llama.cpp) per second is about 3.67 for 7B and 1.92 for 13B. The average time for step (2) and (3) without sparse GPU operators is about **90.55 and 82.92 (us) for 7B, 131.36 and 113.68 (us) for 13B** respectively under all sparsity.
 ### License Disclaimer