Instructions to use itsliupeng/openllama-7b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use itsliupeng/openllama-7b-base with Transformers:

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

pipe = pipeline("text-generation", model="itsliupeng/openllama-7b-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("itsliupeng/openllama-7b-base")
model = AutoModelForCausalLM.from_pretrained("itsliupeng/openllama-7b-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use itsliupeng/openllama-7b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "itsliupeng/openllama-7b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itsliupeng/openllama-7b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/itsliupeng/openllama-7b-base

SGLang

How to use itsliupeng/openllama-7b-base 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 "itsliupeng/openllama-7b-base" \
    --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": "itsliupeng/openllama-7b-base",
		"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 "itsliupeng/openllama-7b-base" \
        --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": "itsliupeng/openllama-7b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use itsliupeng/openllama-7b-base with Docker Model Runner:
```
docker model run hf.co/itsliupeng/openllama-7b-base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A Reproduction of OpenLLaMA using 128 H100 GPUs in Bfloat16.

The pretrain data consists of Falcon, Starcoder, and the wikipedia, arxiv, books, stackexchange from RedPajama. In total, this encompassed nearly 1 trillion tokens.

The model was trained over a single epoch, incorporating 2000 warm-up steps and a cosine learning rate schedule, starting at 3e-5 with 4M batch size.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	47.09
AI2 Reasoning Challenge (25-Shot)	46.16
HellaSwag (10-Shot)	76.40
MMLU (5-Shot)	42.82
TruthfulQA (0-shot)	36.65
Winogrande (5-shot)	70.88
GSM8k (5-shot)	9.63

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Model tree for itsliupeng/openllama-7b-base

Quantizations

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Datasets used to train itsliupeng/openllama-7b-base

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

46.160
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

76.400
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

42.820
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

36.650
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

70.880
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

9.630