Instructions to use cloudyu/Mixtral_7Bx2_MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cloudyu/Mixtral_7Bx2_MoE with Transformers:

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

pipe = pipeline("text-generation", model="cloudyu/Mixtral_7Bx2_MoE")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cloudyu/Mixtral_7Bx2_MoE")
model = AutoModelForCausalLM.from_pretrained("cloudyu/Mixtral_7Bx2_MoE", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cloudyu/Mixtral_7Bx2_MoE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cloudyu/Mixtral_7Bx2_MoE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cloudyu/Mixtral_7Bx2_MoE",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cloudyu/Mixtral_7Bx2_MoE

SGLang

How to use cloudyu/Mixtral_7Bx2_MoE 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 "cloudyu/Mixtral_7Bx2_MoE" \
    --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": "cloudyu/Mixtral_7Bx2_MoE",
		"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 "cloudyu/Mixtral_7Bx2_MoE" \
        --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": "cloudyu/Mixtral_7Bx2_MoE",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cloudyu/Mixtral_7Bx2_MoE with Docker Model Runner:
```
docker model run hf.co/cloudyu/Mixtral_7Bx2_MoE
```

Mixtral_7Bx2_MoE / README.md

cloudyu

Update README.md

b3e9814 almost 3 years ago

preview code

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	---
	license: cc-by-nc-4.0
	---

	# Mixtral MOE 2x7B



	MoE of the following models :

	* [rwitz2/go-bruins-v2.1.1](https://huggingface.co/rwitz2/go-bruins-v2.1.1)
	* [NurtureAI/neural-chat-7b-v3-16k](https://huggingface.co/NurtureAI/neural-chat-7b-v3-16k)
	* [mncai/mistral-7b-dpo-v6](https://huggingface.co/mncai/mistral-7b-dpo-v6)


	metrics:
	Average 73.43
	ARC 71.25
	HellaSwag 87.45

	gpu code example

	```
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import math

	## v2 models
	model_path = "cloudyu/Mixtral_7Bx2_MoE"

	tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
	model = AutoModelForCausalLM.from_pretrained(
	model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True
	)
	print(model)
	prompt = input("please input prompt:")
	while len(prompt) > 0:
	input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")

	generation_output = model.generate(
	input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
	)
	print(tokenizer.decode(generation_output[0]))
	prompt = input("please input prompt:")
	```

	CPU example

	```
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import math

	## v2 models
	model_path = "cloudyu/Mixtral_7Bx2_MoE"

	tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
	model = AutoModelForCausalLM.from_pretrained(
	model_path, torch_dtype=torch.float32, device_map='cpu',local_files_only=False
	)
	print(model)
	prompt = input("please input prompt:")
	while len(prompt) > 0:
	input_ids = tokenizer(prompt, return_tensors="pt").input_ids

	generation_output = model.generate(
	input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
	)
	print(tokenizer.decode(generation_output[0]))
	prompt = input("please input prompt:")

	```