Instructions to use princeton-nlp/Sheared-LLaMA-1.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use princeton-nlp/Sheared-LLaMA-1.3B with Transformers:

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

pipe = pipeline("text-generation", model="princeton-nlp/Sheared-LLaMA-1.3B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/Sheared-LLaMA-1.3B")
model = AutoModelForCausalLM.from_pretrained("princeton-nlp/Sheared-LLaMA-1.3B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use princeton-nlp/Sheared-LLaMA-1.3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "princeton-nlp/Sheared-LLaMA-1.3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "princeton-nlp/Sheared-LLaMA-1.3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/princeton-nlp/Sheared-LLaMA-1.3B

SGLang

How to use princeton-nlp/Sheared-LLaMA-1.3B 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 "princeton-nlp/Sheared-LLaMA-1.3B" \
    --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": "princeton-nlp/Sheared-LLaMA-1.3B",
		"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 "princeton-nlp/Sheared-LLaMA-1.3B" \
        --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": "princeton-nlp/Sheared-LLaMA-1.3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use princeton-nlp/Sheared-LLaMA-1.3B with Docker Model Runner:
```
docker model run hf.co/princeton-nlp/Sheared-LLaMA-1.3B
```

Can provide a use sample?

#13

by wenine - opened Jul 2, 2024

Discussion

wenine

Jul 2, 2024

Hello, thank you for such a nice work, but can you provide a sample code, including how to load the model, the detailed process of providing the prompts and generating the results, and how to set some parameters that control the generation？

wenine changed discussion title from How to use？ to Can provide a use sample? Jul 2, 2024

oddlyspaced

Aug 3, 2024

from transformers import AutoModelForCausalLM, AutoTokenizer

Load model and tokenizer

model = "Sheared-LLaMA-1.3B/" # Replace with the actual path
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model)

Input prompt

input_text = "Once upon a time"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

Generate text

output = model.generate(
input_ids,
max_length=100,
num_return_sequences=1,
no_repeat_ngram_size=2,
temperature=0.7,
top_p=0.9,
do_sample=True
)

Decode and print the generated text

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

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