Instructions to use alpindale/goliath-120b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alpindale/goliath-120b with Transformers:

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

pipe = pipeline("text-generation", model="alpindale/goliath-120b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alpindale/goliath-120b")
model = AutoModelForCausalLM.from_pretrained("alpindale/goliath-120b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alpindale/goliath-120b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alpindale/goliath-120b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpindale/goliath-120b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alpindale/goliath-120b

SGLang

How to use alpindale/goliath-120b 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 "alpindale/goliath-120b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpindale/goliath-120b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "alpindale/goliath-120b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpindale/goliath-120b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alpindale/goliath-120b with Docker Model Runner:
```
docker model run hf.co/alpindale/goliath-120b
```

Great work! Question about the merge specifics.

by UMCU - opened Nov 17, 2023

Discussion

UMCU

Nov 17, 2023

Thank you for this awesome contribution! I am interested in exploring the merging strategy for our projects; I see that you have overlapping and non-overlapping layers. I assume that the non-overlapping layers are simply appended, and that the overlapping layers use linear interpolation with 50/50 weights?

Thank you for your time.

HenkPoley

Nov 17, 2023

You can probably find your answer near the used 'mergekit' software: https://github.com/cg123/mergekit

UMCU

Nov 17, 2023

Mergekit gives several options and has some settable parameters , I cannot derive from the model card what these settings are :).

alpindale

Owner Nov 23, 2023

I explained it a bit in #3, but the TL;DR is that I'm taking slices from each model and stacking those on top of each other. I've mentioned the slice ranges in the README.

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