Instructions to use VMware/open-llama-13b-open-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VMware/open-llama-13b-open-instruct with Transformers:

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

pipe = pipeline("text-generation", model="VMware/open-llama-13b-open-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VMware/open-llama-13b-open-instruct")
model = AutoModelForCausalLM.from_pretrained("VMware/open-llama-13b-open-instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use VMware/open-llama-13b-open-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VMware/open-llama-13b-open-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VMware/open-llama-13b-open-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VMware/open-llama-13b-open-instruct

SGLang

How to use VMware/open-llama-13b-open-instruct 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 "VMware/open-llama-13b-open-instruct" \
    --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": "VMware/open-llama-13b-open-instruct",
		"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 "VMware/open-llama-13b-open-instruct" \
        --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": "VMware/open-llama-13b-open-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use VMware/open-llama-13b-open-instruct with Docker Model Runner:
```
docker model run hf.co/VMware/open-llama-13b-open-instruct
```

open-llama-13b-open-instruct

Commit History

Adding `safetensors` variant of this model (#4)

da8d6fa
verified

rbattle

SFconvertbot commited on Dec 16, 2024

Update README.md

4b07ffb

Teja-Gollapudi commited on Jul 10, 2023

Update tokenizer_config.json

8e7504e

Teja-Gollapudi commited on Jun 23, 2023

Update README.md

d0e642a

Teja-Gollapudi commited on Jun 19, 2023

Create README.md

fff4f61

Teja-Gollapudi commited on Jun 19, 2023

Upload tokenizer

3124762

Teja-Gollapudi commited on Jun 19, 2023

Upload LlamaForCausalLM

29c0730

Teja-Gollapudi commited on Jun 19, 2023

initial commit

dfff386

Teja-Gollapudi commited on Jun 19, 2023

Commit History

Adding `safetensors` variant of this model (#4) da8d6fa verified

Update README.md 4b07ffb

Update tokenizer_config.json 8e7504e

Update README.md d0e642a

Create README.md fff4f61

Upload tokenizer 3124762

Upload LlamaForCausalLM 29c0730

initial commit dfff386

Adding `safetensors` variant of this model (#4)

da8d6fa
verified

Update README.md

4b07ffb

Update tokenizer_config.json

8e7504e

Update README.md

d0e642a

Create README.md

fff4f61

Upload tokenizer

3124762

Upload LlamaForCausalLM

29c0730

initial commit

dfff386