Instructions to use mlabonne/Beyonder-4x7B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/Beyonder-4x7B-v2 with Transformers:

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

pipe = pipeline("text-generation", model="mlabonne/Beyonder-4x7B-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlabonne/Beyonder-4x7B-v2")
model = AutoModelForCausalLM.from_pretrained("mlabonne/Beyonder-4x7B-v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mlabonne/Beyonder-4x7B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlabonne/Beyonder-4x7B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlabonne/Beyonder-4x7B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlabonne/Beyonder-4x7B-v2

SGLang

How to use mlabonne/Beyonder-4x7B-v2 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 "mlabonne/Beyonder-4x7B-v2" \
    --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": "mlabonne/Beyonder-4x7B-v2",
		"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 "mlabonne/Beyonder-4x7B-v2" \
        --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": "mlabonne/Beyonder-4x7B-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlabonne/Beyonder-4x7B-v2 with Docker Model Runner:
```
docker model run hf.co/mlabonne/Beyonder-4x7B-v2
```

Which merge method was used ? Linear ?

by aaagggddd - opened Jan 16, 2024

Discussion

aaagggddd

Jan 16, 2024

https://github.com/cg123/mergekit/tree/mixtral?tab=readme-ov-file#merge-methods
there's many method here.

mlabonne

Owner Jan 16, 2024

Hi, none of those because they're not frankenMoE methods. I used the hidden technique you can find here: https://github.com/cg123/mergekit/blob/mixtral/moe.md

aaagggddd

Jan 16, 2024

Hi, none of those because they're not frankenMoE methods. I used the hidden technique you can find here: https://github.com/cg123/mergekit/blob/mixtral/moe.md

1、Can you tell me the yml file you used to merge this model ?（ like https://github.com/cg123/mergekit/blob/mixtral/examples/gradient-slerp.yml ）
2、Which paper can I learn about frankenMoE ？（I search frankenMoE , but got nothing.）
3、Is there some blog showing more detail that you merge ?
4、Why not use other merge method, like SLERP ? (I found your blog : https://mlabonne.github.io/blog/posts/2024-01-08_Merge_LLMs_with_mergekit.html , this use SLERP as example.)

Thank you !

nachoaristimuno

Mar 31, 2024

Hi @aaagggddd , the merge config yml file can be found in the "File and Versions" tab of this model.

Different merge methods have different objectives. In this case, I guess Maxime wanted to select different top performing models on different tasks (chat, code, math and RP), and make a MoE model for wrapping all these four models into just one. SLERP wouldn't be useful as it can take only two models at a time, and other techniques such as Passthrough (Frankenmerge) could work but it seems less intuitive and more manual work than rather building a MoE.

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