Instructions to use mncai/Mistral-7B-v0.1-orca_platy-2k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mncai/Mistral-7B-v0.1-orca_platy-2k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mncai/Mistral-7B-v0.1-orca_platy-2k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mncai/Mistral-7B-v0.1-orca_platy-2k") model = AutoModelForCausalLM.from_pretrained("mncai/Mistral-7B-v0.1-orca_platy-2k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mncai/Mistral-7B-v0.1-orca_platy-2k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mncai/Mistral-7B-v0.1-orca_platy-2k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mncai/Mistral-7B-v0.1-orca_platy-2k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mncai/Mistral-7B-v0.1-orca_platy-2k
- SGLang
How to use mncai/Mistral-7B-v0.1-orca_platy-2k 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 "mncai/Mistral-7B-v0.1-orca_platy-2k" \ --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": "mncai/Mistral-7B-v0.1-orca_platy-2k", "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 "mncai/Mistral-7B-v0.1-orca_platy-2k" \ --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": "mncai/Mistral-7B-v0.1-orca_platy-2k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mncai/Mistral-7B-v0.1-orca_platy-2k with Docker Model Runner:
docker model run hf.co/mncai/Mistral-7B-v0.1-orca_platy-2k
Download special_tokens_map.json from mncai/Mistral-7B-v0.1-orca_platy-2k: direct link, hf CLI and curl.
- Browser
- Download file 182 Bytes
-
https://huggingface.co/mncai/Mistral-7B-v0.1-orca_platy-2k/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://mncai/Mistral-7B-v0.1-orca_platy-2k/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/mncai/Mistral-7B-v0.1-orca_platy-2k/resolve/main/special_tokens_map.json
182 Bytes
| { | |
| "additional_special_tokens": [ | |
| "<unk>", | |
| "<s>", | |
| "</s>", | |
| "[PAD]" | |
| ], | |
| "bos_token": "<s>", | |
| "eos_token": "</s>", | |
| "pad_token": "[PAD]", | |
| "unk_token": "<unk>" | |
| } | |