Text Generation
Transformers
Safetensors
English
mixtral
MixtralForCausalLM
mixture-of-experts
Mixture of Experts
digit-recognition
pattern-recognition
toy-model
educational
text-generation-inference
Instructions to use junzzhu/atoMixtral-58K-5x5-DigitMesh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junzzhu/atoMixtral-58K-5x5-DigitMesh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="junzzhu/atoMixtral-58K-5x5-DigitMesh")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("junzzhu/atoMixtral-58K-5x5-DigitMesh") model = AutoModelForCausalLM.from_pretrained("junzzhu/atoMixtral-58K-5x5-DigitMesh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use junzzhu/atoMixtral-58K-5x5-DigitMesh with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "junzzhu/atoMixtral-58K-5x5-DigitMesh" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "junzzhu/atoMixtral-58K-5x5-DigitMesh", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/junzzhu/atoMixtral-58K-5x5-DigitMesh
- SGLang
How to use junzzhu/atoMixtral-58K-5x5-DigitMesh 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 "junzzhu/atoMixtral-58K-5x5-DigitMesh" \ --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": "junzzhu/atoMixtral-58K-5x5-DigitMesh", "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 "junzzhu/atoMixtral-58K-5x5-DigitMesh" \ --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": "junzzhu/atoMixtral-58K-5x5-DigitMesh", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use junzzhu/atoMixtral-58K-5x5-DigitMesh with Docker Model Runner:
docker model run hf.co/junzzhu/atoMixtral-58K-5x5-DigitMesh
| { | |
| "version": "1.0", | |
| "truncation": null, | |
| "padding": null, | |
| "added_tokens": [ | |
| { | |
| "id": 2, | |
| "content": "<SEP>", | |
| "single_word": false, | |
| "lstrip": false, | |
| "rstrip": false, | |
| "normalized": false, | |
| "special": true | |
| }, | |
| { | |
| "id": 13, | |
| "content": "<PAD>", | |
| "single_word": false, | |
| "lstrip": false, | |
| "rstrip": false, | |
| "normalized": false, | |
| "special": true | |
| } | |
| ], | |
| "normalizer": null, | |
| "pre_tokenizer": { | |
| "type": "WhitespaceSplit" | |
| }, | |
| "post_processor": null, | |
| "decoder": null, | |
| "model": { | |
| "type": "WordLevel", | |
| "vocab": { | |
| "0": 0, | |
| "1": 1, | |
| "<SEP>": 2, | |
| "D0": 3, | |
| "D1": 4, | |
| "D2": 5, | |
| "D3": 6, | |
| "D4": 7, | |
| "D5": 8, | |
| "D6": 9, | |
| "D7": 10, | |
| "D8": 11, | |
| "D9": 12, | |
| "<PAD>": 13 | |
| }, | |
| "unk_token": "<unk>" | |
| } | |
| } |