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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mixtral MOE 2x7B

MoE of the following models :

NurtureAI/neural-chat-7b-v3-16k
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:")

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.43
AI2 Reasoning Challenge (25-Shot)	71.25
HellaSwag (10-Shot)	87.45
MMLU (5-Shot)	64.98
TruthfulQA (0-shot)	67.23
Winogrande (5-shot)	81.22
GSM8k (5-shot)	68.46

Downloads last month: 237

Safetensors

Model size

13B params

Tensor type

BF16

F16

Model tree for cloudyu/Mixtral_7Bx2_MoE

Quantizations

7 models

Spaces using cloudyu/Mixtral_7Bx2_MoE 22

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

71.250
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

87.450
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.980
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

67.230
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

81.220
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

68.460