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")

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
```

futureHQ commited on Mar 20, 2024

Commit

8080564

verified ·

1 Parent(s): 7b01eeb

Update README.md

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@@ -1,3 +1,31 @@
 ---
 license: mit
 ---

 ---
 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']}")
+```