Instructions to use LiamCarter/icl-pruning-slicegpt-sparsity-0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiamCarter/icl-pruning-slicegpt-sparsity-0.4 with Transformers:

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

pipe = pipeline("text-generation", model="LiamCarter/icl-pruning-slicegpt-sparsity-0.4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiamCarter/icl-pruning-slicegpt-sparsity-0.4")
model = AutoModelForCausalLM.from_pretrained("LiamCarter/icl-pruning-slicegpt-sparsity-0.4")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LiamCarter/icl-pruning-slicegpt-sparsity-0.4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiamCarter/icl-pruning-slicegpt-sparsity-0.4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiamCarter/icl-pruning-slicegpt-sparsity-0.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LiamCarter/icl-pruning-slicegpt-sparsity-0.4

SGLang

How to use LiamCarter/icl-pruning-slicegpt-sparsity-0.4 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 "LiamCarter/icl-pruning-slicegpt-sparsity-0.4" \
    --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": "LiamCarter/icl-pruning-slicegpt-sparsity-0.4",
		"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 "LiamCarter/icl-pruning-slicegpt-sparsity-0.4" \
        --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": "LiamCarter/icl-pruning-slicegpt-sparsity-0.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LiamCarter/icl-pruning-slicegpt-sparsity-0.4 with Docker Model Runner:
```
docker model run hf.co/LiamCarter/icl-pruning-slicegpt-sparsity-0.4
```

slicegpt/sparsity_0.4

This repository was uploaded from a local experiment directory.

Summary

Method: slicegpt
Variant: sparsity_0.4
Format hint: custom-checkpoint-with-hf-config
Source path: models/slicegpt/sparsity_0.4
Repo id: LiamCarter/icl-pruning-slicegpt-sparsity-0.4
Base model: meta-llama/Llama-2-7b-hf

Notes

This upload preserves the original local files as-is. Some directories are standard Hugging Face checkpoints, while others are experiment bundles that may require custom loading code.

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