Text Generation
Transformers
Safetensors
mixtral
yi
Mixture of Experts
Eval Results (legacy)
text-generation-inference
Instructions to use cloudyu/Mixtral_34Bx2_MoE_60B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudyu/Mixtral_34Bx2_MoE_60B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cloudyu/Mixtral_34Bx2_MoE_60B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cloudyu/Mixtral_34Bx2_MoE_60B") model = AutoModelForCausalLM.from_pretrained("cloudyu/Mixtral_34Bx2_MoE_60B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use cloudyu/Mixtral_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/Mixtral_34Bx2_MoE_60B" # 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_34Bx2_MoE_60B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cloudyu/Mixtral_34Bx2_MoE_60B
- SGLang
How to use cloudyu/Mixtral_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/Mixtral_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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cloudyu/Mixtral_34Bx2_MoE_60B", "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_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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cloudyu/Mixtral_34Bx2_MoE_60B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cloudyu/Mixtral_34Bx2_MoE_60B with Docker Model Runner:
docker model run hf.co/cloudyu/Mixtral_34Bx2_MoE_60B
From your work, I find a new way to do model ensemble
1
#14 opened about 2 years ago
by
xxx1
Adding Evaluation Results
#12 opened about 2 years ago
by
leaderboard-pr-bot
The function_calling and translation abilities are weaker than Mixtral 8x7b
1
#11 opened over 2 years ago
by
bingw5
Add mixture of experts tag
#10 opened over 2 years ago
by
davanstrien
how this model goes work,can you share you idea or traning process? thanks
#9 opened over 2 years ago
by
zachzhou
Add merge tag
π 2
2
#8 opened over 2 years ago
by
osanseviero
Vram
2
#7 opened over 2 years ago
by
DKRacingFan
source code and paper?
π 2
8
#6 opened over 2 years ago
by
josephykwang
How does the MoE work?
π 1
3
#5 opened over 2 years ago
by
PacmanIncarnate
Quant pls?
6
#4 opened over 2 years ago
by
Yhyu13
What is your config?
π 1
1
#3 opened over 2 years ago
by
Weyaxi
Should not be called mixtral, the models made into the moe are yi based
π€π 18
9
#2 opened over 2 years ago
by
teknium
Add merge tags
π 3
#1 opened over 2 years ago
by
JusticeDike