Instructions to use cloudyu/Yi-34Bx2-MoE-60B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cloudyu/Yi-34Bx2-MoE-60B with Transformers:

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

pipe = pipeline("text-generation", model="cloudyu/Yi-34Bx2-MoE-60B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cloudyu/Yi-34Bx2-MoE-60B")
model = AutoModelForCausalLM.from_pretrained("cloudyu/Yi-34Bx2-MoE-60B")
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

vLLM

How to use cloudyu/Yi-34Bx2-MoE-60B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cloudyu/Yi-34Bx2-MoE-60B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cloudyu/Yi-34Bx2-MoE-60B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cloudyu/Yi-34Bx2-MoE-60B

SGLang

How to use cloudyu/Yi-34Bx2-MoE-60B 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/Yi-34Bx2-MoE-60B" \
    --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": "cloudyu/Yi-34Bx2-MoE-60B",
		"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 "cloudyu/Yi-34Bx2-MoE-60B" \
        --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": "cloudyu/Yi-34Bx2-MoE-60B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cloudyu/Yi-34Bx2-MoE-60B with Docker Model Runner:
```
docker model run hf.co/cloudyu/Yi-34Bx2-MoE-60B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

UPDATE! GGUF Format is ready at cloudyu/Yi-34Bx2-MoE-60B-GGUF

Yi based MOE 2x34B with mixtral architecture

Highest score Model ranked by Open LLM Leaderboard (2024-01-11)

Average Score 76.72

This is an English & Chinese MoE Model , slightly different with cloudyu/Mixtral_34Bx2_MoE_60B, and also based on

[jondurbin/bagel-dpo-34b-v0.2]
[SUSTech/SUS-Chat-34B]

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	76.72
AI2 Reasoning Challenge (25-Shot)	71.08
HellaSwag (10-Shot)	85.23
MMLU (5-Shot)	77.47
TruthfulQA (0-shot)	66.19
Winogrande (5-shot)	84.85
GSM8k (5-shot)	75.51

gpu code example

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math

## v2 models
model_path = "cloudyu/Yi-34Bx2-MoE-60B"

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/Yi-34Bx2-MoE-60B"

tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
        model_path, torch_dtype=torch.bfloat16, device_map='cpu'
)
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:")

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Safetensors

Model size

61B params

Tensor type

BF16

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Quantizations

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cloudyu
/

Yi-34Bx2-MoE-60B

Yi based MOE 2x34B with mixtral architecture

Open LLM Leaderboard Evaluation Results

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