Instructions to use VMware/open-llama-7b-v2-open-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VMware/open-llama-7b-v2-open-instruct with Transformers:

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

pipe = pipeline("text-generation", model="VMware/open-llama-7b-v2-open-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VMware/open-llama-7b-v2-open-instruct")
model = AutoModelForCausalLM.from_pretrained("VMware/open-llama-7b-v2-open-instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VMware/open-llama-7b-v2-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-7b-v2-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-7b-v2-open-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VMware/open-llama-7b-v2-open-instruct

SGLang

How to use VMware/open-llama-7b-v2-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-7b-v2-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-7b-v2-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-7b-v2-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-7b-v2-open-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use VMware/open-llama-7b-v2-open-instruct with Docker Model Runner:
```
docker model run hf.co/VMware/open-llama-7b-v2-open-instruct
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Nov 17, 2023

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+14

-0

Files changed (1) hide show

README.md +14 -0

README.md CHANGED Viewed

@@ -139,3 +139,17 @@ The finetuning scripts will be available in our [RAIL Github Repository](https:/
 ## Evaluation
 **TODO**

 ## Evaluation
 **TODO**
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Vmware__open-llama-7b-v2-open-instruct)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 40.34   |
+| ARC (25-shot)         | 39.76          |
+| HellaSwag (10-shot)   | 70.31    |
+| MMLU (5-shot)         | 35.16         |
+| TruthfulQA (0-shot)   | 39.53   |
+| Winogrande (5-shot)   | 64.33   |
+| GSM8K (5-shot)        | 7.43        |
+| DROP (3-shot)         | 25.88         |