Instructions to use yujiepan/mixtral-8xtiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yujiepan/mixtral-8xtiny-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yujiepan/mixtral-8xtiny-random") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yujiepan/mixtral-8xtiny-random") model = AutoModelForCausalLM.from_pretrained("yujiepan/mixtral-8xtiny-random") 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 yujiepan/mixtral-8xtiny-random with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yujiepan/mixtral-8xtiny-random" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yujiepan/mixtral-8xtiny-random", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/yujiepan/mixtral-8xtiny-random
- SGLang
How to use yujiepan/mixtral-8xtiny-random 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 "yujiepan/mixtral-8xtiny-random" \ --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": "yujiepan/mixtral-8xtiny-random", "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 "yujiepan/mixtral-8xtiny-random" \ --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": "yujiepan/mixtral-8xtiny-random", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use yujiepan/mixtral-8xtiny-random with Docker Model Runner:
docker model run hf.co/yujiepan/mixtral-8xtiny-random
Librarian Bot: Add moe tag to model
#1
by librarian-bot - opened
This pull request aims to enrich the metadata of your model by adding an moe (Mixture of Experts) tag in the YAML block of your model's README.md.
How did we find this information? We infered that this model is a moe model based on the following criteria:
- The model's name contains the string
moe. - The model indicates it uses a
moearchitecture - The model's base model is a
moemodel
Why add this? Enhancing your model's metadata in this way:
- Boosts Discoverability - It becomes easier to find mixture of experts models on the Hub
- Helping understand the ecosystem - It becomes easier to understand the ecosystem of mixture of experts models on the Hub and how they are used
This PR comes courtesy of Librarian Bot. If you have any feedback, queries, or need assistance, please don't hesitate to reach out to @davanstrien.