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
```

Can I merge models using architectures other than Llama, such as Memba?

#10

by Jo1uck - opened Dec 7, 2023

Discussion

Jo1uck

Dec 7, 2023

Is it effective to merge models of different architectures using mergekit?

alpindale

Owner Dec 8, 2023

AFAIK mergekit only supports transformer models, as it imports from HF transformers and manually specifies the different layer names in its architecture.py. You can try inspecting all the module names for mamba, and map them appropriately in mergekit, and have it load from the mamba_ssm package if it encounters a mamba model. I grabbed the module names yesterday:

MAMBA_INFO = StaticTensorNames(
    name="MambaLMHeadModel",
    pre_weight_names=["backbone.embedding.weight"],
    post_weight_names=["backbone.norm_f.weight", "lm_head.weight"],
    embed_weight_names=["backbone.embedding.weight", "lm_head.weight"],
    layer_prefix_format="backbone.layers.{idx}",
    layer_weight_suffixes=[
        "mixer.A_log",
        "mixer.D",
        "mixer.in_proj.weight",
        "conv1d.weight",
        "conv1d.bias",
        "x_proj.weight",
        "dt_proj.weight",
        "dt_proj.bias",
        "out_proj.weight",
        "norm.weight",
    ],
)

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