Instructions to use TencentARC/LLaMA-Pro-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TencentARC/LLaMA-Pro-8B with Transformers:

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

pipe = pipeline("text-generation", model="TencentARC/LLaMA-Pro-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TencentARC/LLaMA-Pro-8B")
model = AutoModelForCausalLM.from_pretrained("TencentARC/LLaMA-Pro-8B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TencentARC/LLaMA-Pro-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TencentARC/LLaMA-Pro-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TencentARC/LLaMA-Pro-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TencentARC/LLaMA-Pro-8B

SGLang

How to use TencentARC/LLaMA-Pro-8B 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 "TencentARC/LLaMA-Pro-8B" \
    --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": "TencentARC/LLaMA-Pro-8B",
		"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 "TencentARC/LLaMA-Pro-8B" \
        --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": "TencentARC/LLaMA-Pro-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TencentARC/LLaMA-Pro-8B with Docker Model Runner:
```
docker model run hf.co/TencentARC/LLaMA-Pro-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LLaMA-Pro-8B Model Card

Model Description

LLaMA-Pro is a progressive version of the original LLaMA model, enhanced by the addition of Transformer blocks. It specializes in integrating both general language understanding and domain-specific knowledge, particularly in programming and mathematics.

Development and Training

Developed by Tencent's ARC Lab, LLaMA-Pro is an 8.3 billion parameter model. It's an expansion of LLaMA2-7B, further trained on code and math corpora totaling 80 billion tokens.

Intended Use

This model is designed for a wide range of NLP tasks, with a focus on programming, mathematics, and general language tasks. It suits scenarios requiring integration of natural and programming languages.

Performance

LLaMA-Pro demonstrates advanced performance across various benchmarks. It outperforms existing models in the LLaMA series in handling diverse tasks, showcasing its capability as an intelligent language agent.

Overall Performance on Languages, math and code tasks

Model	ARC	Hellaswag	MMLU	TruthfulQA	Winogrande	GSM8K	GSM8K-PoT	HumanEval	MBPP	Avg
LLAMA PRO (8B)	54.10	77.94	47.88	39.04	73.95	17.89	25.42	28.66	33.20	44.2
LLaMA2-7B	53.07	78.59	46.87	38.76	74.03	14.48	17.68	13.05	20.09	39.62
CodeLLaMA-7B	39.93	60.80	31.12	37.82	64.01	5.16	25.20	33.50	41.40	37.66
LLAMA PRO-INSTRUCT	52.30	76.88	52.57	48.80	72.53	43.59	55.61	44.51	37.88	53.8

Performance on GPT4 Evaluation

Model	MT Bench
Alpaca-13B	4.53
CodeLLaMA-7B-Instruct	5.71
Vicuna-7B	6.17
LLaMA2-7B-Chat	6.27
LLAMA PRO-INSTRUCT	6.32

Limitations

While LLaMA-Pro addresses some limitations of previous models in the series, it may still encounter challenges specific to highly specialized domains or tasks.