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
Request: Would the amazon team be willing to train a model on my high quality dataset?
I have created and refined open source dataset named "LosslessmegacodeV3" (Linked at the end) which I believe could create one of the best open source ai models if trained on the right base model. However seeing as I am severally lacking in funding (Aka im broke af) I haven't been able to do the training myself. I'm curious if your team would be willing to take on the challenge of training one ai model on my dataset for coding and non-coding tasks (the dataset is made for both) to create possibly one of the best ai models available. If you are up for the challenge, here is a list of the top models I would recommend training with my dataset in order or highest priority (Note that I would only ask you to train 1 model, I am merely giving multiple options):
1: WizardLM/WizardCoder-Python-13B-V1.0
2: amazon/MistralLite
3: WizardLM/WizardCoder-Python-34B-V1.0 (#3 and #4 are equal in priority)
4: Phind/Phind-CodeLlama-34B-v2 (#3 and #4 are equal in priority)
5: jondurbin/airoboros-l2-c70b-3.1.2
If you agree I have some names for the model that would release if you would allow me. I've listed them bellow. Lossless and V3 referring to the datasets That were used to train the models on.
1: LosslessWizardCoder-Python-13B-V3
2: LosslessMistralLitecoderV3
3: LosslessWizardCoder-Python-34B-V3
4: LoesslessPhind-LlamaCoder-34B-V3
5: LosslessAiroborosCoder-l2-c70b-V3
Dataset link:
Hi @rombodawg Thanks for the advice!
Unfortunately, we have our internal process to work on this topic.
Based on your description, codewhisperer might be sth you are interested. Please have a try :)