Instructions to use Neko-Institute-of-Science/LLaMA-7B-4bit-32g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Neko-Institute-of-Science/LLaMA-7B-4bit-32g with Transformers:

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

pipe = pipeline("text-generation", model="Neko-Institute-of-Science/LLaMA-7B-4bit-32g")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Neko-Institute-of-Science/LLaMA-7B-4bit-32g")
model = AutoModelForCausalLM.from_pretrained("Neko-Institute-of-Science/LLaMA-7B-4bit-32g", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Neko-Institute-of-Science/LLaMA-7B-4bit-32g with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Neko-Institute-of-Science/LLaMA-7B-4bit-32g"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Neko-Institute-of-Science/LLaMA-7B-4bit-32g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Neko-Institute-of-Science/LLaMA-7B-4bit-32g

SGLang

How to use Neko-Institute-of-Science/LLaMA-7B-4bit-32g 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 "Neko-Institute-of-Science/LLaMA-7B-4bit-32g" \
    --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": "Neko-Institute-of-Science/LLaMA-7B-4bit-32g",
		"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 "Neko-Institute-of-Science/LLaMA-7B-4bit-32g" \
        --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": "Neko-Institute-of-Science/LLaMA-7B-4bit-32g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Neko-Institute-of-Science/LLaMA-7B-4bit-32g with Docker Model Runner:
```
docker model run hf.co/Neko-Institute-of-Science/LLaMA-7B-4bit-32g
```

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Check out the documentation for more information.

7B  (act-order true-sequential groupsize)
wikitext2 5.677095890045166 (stock 16bit)
wikitext2 5.768329620361328 (32g)
wikitext2 5.833956718444824 (128g)
ptb-new 10.10704231262207 (stock 16bit)
ptb-new 10.273148536682129 (32g)
ptb-new 10.347890853881836 (128g)
c4-new 7.343583106994629 (stock 16bit)
c4-new 7.443920612335205 (32g)
c4-new 7.5146918296813965 (128g)

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