Instructions to use amazon/MistralLite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amazon/MistralLite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="amazon/MistralLite")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("amazon/MistralLite") model = AutoModelForCausalLM.from_pretrained("amazon/MistralLite") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use amazon/MistralLite with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "amazon/MistralLite" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amazon/MistralLite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/amazon/MistralLite
- SGLang
How to use amazon/MistralLite 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 "amazon/MistralLite" \ --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": "amazon/MistralLite", "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 "amazon/MistralLite" \ --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": "amazon/MistralLite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use amazon/MistralLite with Docker Model Runner:
docker model run hf.co/amazon/MistralLite
Question Answering and Sentence completion format
#13
by codegood - opened
Hello,
I'm trying to build a model where I'll be fine tuning this model using sentences to increase it's knowledge base and then training it on Question Answering pairs for better performance.
I've the following questions:
- Is Sentence completion an ideal way to improve knowledge base of the model?
- What is the format for question answering? I know see how to ask question, but want to know how to add the ground truth answer which I think will be after tag.
Thanks
Hi @codegood
Thanks for your interest!
- Yep point 1 is a reasonable idea
- The format is suggested as
<|prompter|>YOUR QUESTION<|assistant|>YOUR ANSWER
But theoretically you can use whatever format you like to fine tune the model. Hope this helps. Cheers!
yinsong1986 changed discussion status to closed