Understanding the WizardLM-70B-V1.0 model

Model overview

Attribute	Details
Provider	WizardLM
Model Name	`wizardlm-70B-V1.0`
Compatible Instances	H100 (FP8) - H100-2 (FP16)
Context size	4,096 tokens

Model names

wizardlm/wizardlm-70b-v1.0:fp8
wizardlm/wizardlm-70b-v1.0:fp16

WizardLM-70B-V1.0, developed by WizardLM, is specifically designed for content creation platforms and writing assistants. With its extensive training in diverse textual data, WizardLM-70B-V1.0 generates high-quality content and assists writers in various creative and professional endeavors.

Why you will love it

WizardLM-70B-V1.0 offers unparalleled versatility and creativity in content generation. Whether you are a writer seeking inspiration or a content platform looking to automate content creation, this model delivers exceptional performance. Its adaptability and natural language fluency make it valuable for enhancing productivity and creativity.

How to use it

Sending Inference requests

To perform inference tasks with your WizardLM model deployed at Scaleway, use the following command:

curl -s \
-H "X-Auth-Token: <IAM API key>" \
-H "Content-Type: application/json" \
--request POST \
--url "https://<Deployment UUID>.ifr.fr-par.scw.cloud/" \
--data '{"text_input": "Scaleway LLM Inference. Say hello", "max_tokens": 200, "temperature": 0.2, "random_seed": 1, "top_p": 0.9}' | jq -r .text_output

Make sure to replace <IAM API key> and <Deployment UUID> with your actual IAM API key and the Deployment UUID you are targeting.

Note

Ensure that the input data is properly formatted according to the model’s input requirements.

Prompt engineering

WizardLM adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following:

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. 
USER: Hi 
ASSISTANT: Hello.</s>
USER: Who are you? 
ASSISTANT: I am WizardLM.</s>

Receiving LLM Inference responses

Upon sending the HTTP request to the public or private endpoints exposed by the server, you will receive inference responses from the managed LLM Inference server. Process the output data according to your application’s needs. The response will contain the output generated by the LLM model based on the input provided in the request.

Note

Despite efforts for accuracy, the possibility of generated text containing inaccuracies or hallucinations exists. Always verify the content generated independently.