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 Settings

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
```

EXTENDED LENGTH FRANKENSTEIN

by ChuckMcSneed - opened Nov 27, 2023

Discussion

ChuckMcSneed

Nov 27, 2023

Is it possible to merge this fun model with yarn 70b for extended context? Or maybe to create a new frankenmodel with yarn?

alpindale

Owner Nov 27, 2023

You can try and find out. You only need enough RAM to load two 70B models at a time, can even swap to disk (would just be slower).

ChuckMcSneed

Nov 28, 2023

•

edited Nov 28, 2023

Do you have any suggstions on which layers and models to use?

ChuckMcSneed

Nov 28, 2023

UPDATE: I don't think that mergekit works with yarn.
ValueError: rope_scaling must be a dictionary with with two fields, type and factor, got {'factor': 8.0, 'finetuned': True, 'original_max_position_embeddings': 4096, 'type': 'yarn'}
And after modifying config of yarn:
ValueError: rope_scaling's type field must be one of ['linear', 'dynamic'], got yarn

ChuckMcSneed

Nov 28, 2023

Is it maybe possible to somehow merge llama and yi yi ass model or are they too different?

ChuckMcSneed

Jan 5, 2024

I finally made it. Merged 70b 32k model with itself. It actually works!
https://huggingface.co/ChuckMcSneed/DoubleGold-v0.1-123b-32k

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