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 May 17, 2024

Commit

4b55046

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1 Parent(s): 178909c

Update README.md

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@@ -64,8 +64,8 @@ The 7B model is trained on 8 A100 GPUs. The learning rate (LR) is controlled by
 |        1        |   \\(5e-3\\)    | 6,000  |         12.58          |
 |        2        |   \\(5e-2\\)    | 10,000 |         20.97          |
 |        3        |   \\(5e-2\\)    | 12,000 |         25.17          |
-|        4        |   \\(5e-1\\)    | 16,000 |         33.55          |
-|        5        |   \\(5e-1\\)    | 16,500 |         34.60          |
 ### Evaluation Results

 |        1        |   \\(5e-3\\)    | 6,000  |         12.58          |
 |        2        |   \\(5e-2\\)    | 10,000 |         20.97          |
 |        3        |   \\(5e-2\\)    | 12,000 |         25.17          |
+|        4        |   \\(2e-1\\)    | 16,000 |         33.55          |
+|        5        |   \\(2e-1\\)    | 16,500 |         34.60          |
 ### Evaluation Results