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
```

What is the difference between this model and OpenLLaMA 7Bv2?

by weiyucheng - opened Jan 11, 2024

Discussion

weiyucheng

Jan 11, 2024

The training dataset seems to be the same, but this model's performance is much better.

itsliupeng

Owner Jan 12, 2024

•

edited Jan 12, 2024

The training dataset seems to be the same, but this model's performance is much better.

The sole difference lies in the training framework, which has been shifted from using Jax on TPU to employing MegatronLM on GPU. The traning loss is more lower.

Overbite1741

Jan 23, 2024

@itsliupeng Are the hyperparameters the same?

itsliupeng

Owner Jan 23, 2024

@itsliupeng Are the hyperparameters the same?

Yes, cosinle lr 3e-4, batch_size 4M tokens, the same with llama2-7B

itsliupeng changed discussion status to closed Jan 30, 2024

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