Instructions to use m-a-p/OpenLLaMA-Reproduce-973.08B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-a-p/OpenLLaMA-Reproduce-973.08B with Transformers:

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

pipe = pipeline("text-generation", model="m-a-p/OpenLLaMA-Reproduce-973.08B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("m-a-p/OpenLLaMA-Reproduce-973.08B")
model = AutoModelForCausalLM.from_pretrained("m-a-p/OpenLLaMA-Reproduce-973.08B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use m-a-p/OpenLLaMA-Reproduce-973.08B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "m-a-p/OpenLLaMA-Reproduce-973.08B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "m-a-p/OpenLLaMA-Reproduce-973.08B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/m-a-p/OpenLLaMA-Reproduce-973.08B

SGLang

How to use m-a-p/OpenLLaMA-Reproduce-973.08B 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 "m-a-p/OpenLLaMA-Reproduce-973.08B" \
    --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": "m-a-p/OpenLLaMA-Reproduce-973.08B",
		"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 "m-a-p/OpenLLaMA-Reproduce-973.08B" \
        --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": "m-a-p/OpenLLaMA-Reproduce-973.08B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use m-a-p/OpenLLaMA-Reproduce-973.08B with Docker Model Runner:
```
docker model run hf.co/m-a-p/OpenLLaMA-Reproduce-973.08B
```

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Check out the documentation for more information.

OpenLLaMA 7Bv2 Model Card

Model Description

OpenLLaMA 7Bv2 is a cutting-edge language model, trained with a focus on delivering high-quality, contextually relevant text predictions. It leverages a diverse composite dataset that includes web-crawled data, scholarly articles, and a wide range of literature and question-answer pairs to ensure broad domain coverage and applicability.

Training Data

The model was trained on a composite dataset that includes:

Falcon refined-web dataset
starcoder datasets
Contributions from Wikipedia for encyclopedic knowledge
Academic papers from arXiv for scientific understanding
A vast collection of books spanning multiple genres
Stack Exchange data curated by RedPajama

Training Procedure

Learning Rate: Utilized a maximum learning rate of 3e-4 and a minimum learning rate of 3e-5.
Batch Size: Employed a batch size of 4 million tokens, optimizing the training process for both efficiency and performance.
Learning Rate Scheduler: The model's learning rate scheduling closely follows the strategy used in Llama2, ensuring gradual adjustments for optimal convergence.

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