Instructions to use OpenGenerativeAI/FoundationModel-1.1B-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGenerativeAI/FoundationModel-1.1B-LLM with Transformers:

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

pipe = pipeline("text-generation", model="OpenGenerativeAI/FoundationModel-1.1B-LLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenGenerativeAI/FoundationModel-1.1B-LLM")
model = AutoModelForCausalLM.from_pretrained("OpenGenerativeAI/FoundationModel-1.1B-LLM", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenGenerativeAI/FoundationModel-1.1B-LLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGenerativeAI/FoundationModel-1.1B-LLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGenerativeAI/FoundationModel-1.1B-LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenGenerativeAI/FoundationModel-1.1B-LLM

SGLang

How to use OpenGenerativeAI/FoundationModel-1.1B-LLM 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 "OpenGenerativeAI/FoundationModel-1.1B-LLM" \
    --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": "OpenGenerativeAI/FoundationModel-1.1B-LLM",
		"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 "OpenGenerativeAI/FoundationModel-1.1B-LLM" \
        --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": "OpenGenerativeAI/FoundationModel-1.1B-LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenGenerativeAI/FoundationModel-1.1B-LLM with Docker Model Runner:
```
docker model run hf.co/OpenGenerativeAI/FoundationModel-1.1B-LLM
```

FoundationModel-1.1B-LLM / README.md

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	---
	license: mit
	---

	#### How to use
	You will need the transformers>=4.31
	```python
	from transformers import AutoTokenizer
	import transformers
	import torch
	model = "OpenGenerativeAI/FoundationModel-1.1B-LLM"
	tokenizer = AutoTokenizer.from_pretrained(model)
	pipeline = transformers.pipeline(
	"text-generation",
	model=model,
	torch_dtype=torch.float16,
	device_map="auto",
	)

	sequences = pipeline(
	'The FoundationModel project aims to pretrain a 1.1B Llama model on 3 trillion tokens.',
	do_sample=True,
	top_k=10,
	num_return_sequences=1,
	repetition_penalty=1.5,
	eos_token_id=tokenizer.eos_token_id,
	max_length=500,
	)
	for seq in sequences:
	print(f"Result: {seq['generated_text']}")
	```