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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("itsliupeng/openllama-7b-icl")
model = AutoModelForCausalLM.from_pretrained("itsliupeng/openllama-7b-icl", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "itsliupeng/openllama-7b-icl"
# 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-icl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use itsliupeng/openllama-7b-icl with Docker Model Runner:
```
docker model run hf.co/itsliupeng/openllama-7b-icl
```

Train openllama-7b with in-context leanrning

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.

The sole distinction from the OpenLLaMA 7B Base lies in the organization of Falcon documents, which follows the methodology outlined in this arXiv paper.

Downloads last month: 66

Paper for itsliupeng/openllama-7b-icl

In-Context Pretraining: Language Modeling Beyond Document Boundaries

Paper • 2310.10638 • Published Oct 16, 2023 • 29