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, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

LM-Eval-Harness Evaluation

by GuanCL - opened Sep 26, 2024

Discussion

GuanCL

Sep 26, 2024

Hi, I tested the activation-sparse models with lm-eval:

lm_eval --model hf \
    --model_args pretrained=${MODEL},parallelize=True,trust_remote_code=True \
    --tasks arc_easy,arc_challenge,boolq,hellaswag,openbookqa,lambada_openai,mmlu,piqa,truthfulqa_mc1,winogrande \
    --batch_size auto

the outcomes seem bad on prosparse-llama models (sparse mini-CPM is sense), is there any insights?

GuanCL

Sep 26, 2024

metric 'acc' reported, 0-shot

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