Instructions to use upstage/llama-30b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use upstage/llama-30b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="upstage/llama-30b-instruct")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("upstage/llama-30b-instruct")
model = AutoModelForCausalLM.from_pretrained("upstage/llama-30b-instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use upstage/llama-30b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "upstage/llama-30b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "upstage/llama-30b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/upstage/llama-30b-instruct

SGLang

How to use upstage/llama-30b-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 "upstage/llama-30b-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": "upstage/llama-30b-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 "upstage/llama-30b-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": "upstage/llama-30b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use upstage/llama-30b-instruct with Docker Model Runner:
```
docker model run hf.co/upstage/llama-30b-instruct
```

wonhosong commited on Jul 19, 2023

Commit

00a74a6

1 Parent(s): dfdd22e

Update README.md

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@@ -58,6 +58,11 @@ pipeline_tag: text-generation
 **Overview**
 - We conducted a performance evaluation based on the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA`. We used the lm-evaluation-harness repository, specifically commit `b281b0921b636bc36ad05c0b0b0763bd6dd43463`. We can reproduce the evaluation environments using the command below:
 **Main Results**
 | Model                                         | Average | ARC   | HellaSwag | MMLU  | TruthfulQA |

 **Overview**
 - We conducted a performance evaluation based on the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). We evaluated our model on four benchmark datasets, which include `ARC-Challenge`, `HellaSwag`, `MMLU`, and `TruthfulQA`. We used the lm-evaluation-harness repository, specifically commit `b281b0921b636bc36ad05c0b0b0763bd6dd43463`. We can reproduce the evaluation environments using the command below:
+```
+git clone https://github.com/EleutherAI/lm-evaluation-harness.git
+git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
+cd lm-evaluation-harness
+```
 **Main Results**
 | Model                                         | Average | ARC   | HellaSwag | MMLU  | TruthfulQA |