Instructions to use openlm-research/open_llama_3b_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openlm-research/open_llama_3b_v2 with Transformers:

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

pipe = pipeline("text-generation", model="openlm-research/open_llama_3b_v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openlm-research/open_llama_3b_v2")
model = AutoModelForCausalLM.from_pretrained("openlm-research/open_llama_3b_v2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openlm-research/open_llama_3b_v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openlm-research/open_llama_3b_v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openlm-research/open_llama_3b_v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openlm-research/open_llama_3b_v2

SGLang

How to use openlm-research/open_llama_3b_v2 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 "openlm-research/open_llama_3b_v2" \
    --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": "openlm-research/open_llama_3b_v2",
		"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 "openlm-research/open_llama_3b_v2" \
        --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": "openlm-research/open_llama_3b_v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openlm-research/open_llama_3b_v2 with Docker Model Runner:
```
docker model run hf.co/openlm-research/open_llama_3b_v2
```

Add tokenizer.json

by bianchidotdev - opened Sep 8, 2023

base: refs/heads/main

←

from: refs/pr/5

Discussion Files changed

+92965

-1

bianchidotdev

Sep 8, 2023

•

edited Sep 8, 2023

Adding tokenizer.json for easier consumption. The issue with fast tokenizers referenced in the README is fixed now so we should be good to include this file.

If this change sounds good, I can also generate the tokenizer.json for the 7B model as well.

Generated with:

#!/usr/bin/env python3

import os
from transformers import AutoTokenizer

path = os.path.join(os.getcwd(),"open_llama_3b_v2")

tokenizer = AutoTokenizer.from_pretrained(path)
tokenizer.save_pretrained(path)

Add tokenizer.json8f47b0fd

bianchidotdev changed pull request status to open Sep 8, 2023

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