Instructions to use m-a-p/OpenLLaMA-Reproduce-503.32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use m-a-p/OpenLLaMA-Reproduce-503.32B with Transformers:

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

pipe = pipeline("text-generation", model="m-a-p/OpenLLaMA-Reproduce-503.32B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("m-a-p/OpenLLaMA-Reproduce-503.32B")
model = AutoModelForCausalLM.from_pretrained("m-a-p/OpenLLaMA-Reproduce-503.32B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use m-a-p/OpenLLaMA-Reproduce-503.32B 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-503.32B"
# 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-503.32B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/m-a-p/OpenLLaMA-Reproduce-503.32B

SGLang

How to use m-a-p/OpenLLaMA-Reproduce-503.32B 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-503.32B" \
    --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-503.32B",
		"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-503.32B" \
        --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-503.32B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use m-a-p/OpenLLaMA-Reproduce-503.32B with Docker Model Runner:
```
docker model run hf.co/m-a-p/OpenLLaMA-Reproduce-503.32B
```

Create README.md

by Chasell - opened Apr 2, 2024

base: refs/heads/main

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from: refs/pr/1

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+# 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.