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 27, 2024

Commit

48ed02e

verified ·

1 Parent(s): cab5d7d

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@@ -66,16 +66,6 @@ The 7B model is trained on 8 A100 GPUs. The learning rate (LR) is controlled by
 |        4        |   \\(5e-1\\)    | 16,000 |         33.55          |
 |        5        |   \\(5e-1\\)    | 16,500 |         34.60          |
-### Evaluation Benckmarks
-- **Code Generation**: We compute the average pass@1 scores on HumanEval (0-shot) and MBPP (3-shot).
-- **Commonsense Reasoning**: We report the average 0-shot perplexity (PPL) on PIQA, SIQA, HellaSwag, WinoGrande, and COPA.
-- **Reading Comprehension**: We compute the average 0-shot PPL on BoolQ, 0-shot accuracy on LAMBADA and TyDi QA.
-- **Other Popular Benchmarks**: We report the average accuracies on GSM8K (8-shot), MMLU (5-shot), Big Bench Hard (BBH) (3-shot), and the average PPL on AGI-Eval (0-shot).
 ### Evaluation Results
 The evaluation results on the above benchmarks demonstrate the advantage of ProSparse, which is the only method achieving high sparsity and comparable performance to the original Swish-activated LLaMA2. Note that models under all settings are trained with the same number of tokens on the same mixed dataset. Our evaluation is based on the framework [UltraEval](https://github.com/OpenBMB/UltraEval). The evaluation details are listed as follows:
@@ -86,7 +76,7 @@ The evaluation results on the above benchmarks demonstrate the advantage of ProS
 - **Reading Comprehension**: We compute the average 0-shot accuracies on BoolQ, 0-shot accuracy on LAMBADA and TyDi QA.
-- **Other Popular Benchmarks**: We report the average accuracies on GSM8K (8-shot), MMLU (5-shot), Big Bench Hard (BBH) (3-shot), and AGI-Eval (0-shot). Refer to Appendix~\ref{sec:eval-details} for more details.
 **Notes**: For PIQA, SIQA, HellaSwag, WinoGrande, COPA, BoolQ, LAMBADA, TyDi QA, and AGI-Eval, we obtain the predicted answers based on maximized perplexity. For GSM8K, MMLU, and BBH, the predicted answers are directly generated.

 |        4        |   \\(5e-1\\)    | 16,000 |         33.55          |
 |        5        |   \\(5e-1\\)    | 16,500 |         34.60          |
 ### Evaluation Results
 The evaluation results on the above benchmarks demonstrate the advantage of ProSparse, which is the only method achieving high sparsity and comparable performance to the original Swish-activated LLaMA2. Note that models under all settings are trained with the same number of tokens on the same mixed dataset. Our evaluation is based on the framework [UltraEval](https://github.com/OpenBMB/UltraEval). The evaluation details are listed as follows:
 - **Reading Comprehension**: We compute the average 0-shot accuracies on BoolQ, 0-shot accuracy on LAMBADA and TyDi QA.
+- **Other Popular Benchmarks**: We report the average accuracies on GSM8K (8-shot), MMLU (5-shot), Big Bench Hard (BBH) (3-shot), and AGI-Eval (0-shot).
 **Notes**: For PIQA, SIQA, HellaSwag, WinoGrande, COPA, BoolQ, LAMBADA, TyDi QA, and AGI-Eval, we obtain the predicted answers based on maximized perplexity. For GSM8K, MMLU, and BBH, the predicted answers are directly generated.