WizardLM: The Complete Guide to Download, Use, and Compare WizardLM vs ChatGPT

Estimated reading time: 7 minutes

 

Key Takeaways

• WizardLM is an open-source model known for complex instruction following.
• You can also download a simple WizardLM that allows you to run models locally for more control.
• Timely engineering greatly increases the quality and relevance of output.
• In a head-to-head WizardLM vs ChatGPT comparison test, WizardLM excels in privacy and customization while ChatGPT edges out with accessibility.
• An energetic community provides instant feedback and support.
• Table of Contents

 

 

WizardLM is a state-of-the-art open-sourced model, LLM utilizing the pretrained architectures like Llama-2 based foundation model and Mixtral-8x7B. Its signature is Evol-Instruct: a training strategy that auto-generates challenging prompts and delivers higher-quality instruction following. The model consistently outperforms state-of-the-art baselines including a wide range of peers in context reasoning among benchmarks as MT-Bench, AlpacaEval, and WizardEval.

“WizardLM unlocks the power of big language models to people who need transparency, fine-grained customization and top-flight accuracy.”

Key features include:

Heavy long-distance communication in multi-turn conversational context.

Dynamic tone and style guidance to accommodate any type of writing.

Support operations, summarization and more.

It all starts with the correct WizardLM download. The model family are available in three widely used repositories:

Dataloop for the common 13B checkpoints.

Hugging Face mirrors were fantastic (For jobs involving Transformer workflows).

WizardLM-2 8x22B article chronology of performance test index for relevance AI for enterprise class.

Installation steps:

Clone the model files (PyTorch or Safetensors) or download them.

Step 2: Installation Make sure you have installed Python v3.8+, PyTorch, and Transformers.

Check GPU Compatibility—7B will require roughly 12 GB VRAM; 13B requires ~24 GB; and 8x22B needs multiple expensive GPUs.

Run a simple inference script to validate everything is setup correctly.

Troubleshooting Tip: if you have CUDA errors, update drivers and make sure that PyTorch is compatible with your CUDA version.

Realizing the full potential of WizardLM requires engineered prompts:

Be explicit — let me know what is required, in which format and with what tone.

You’ll want a few-shot examples so WizardLM can mimic style perfectly.

Use multi-turn context, put related queries into one session.

Popular applications:

Technical writing & code generation.

Medical, legal or business type domain-centric analysis.

Creative stories, poems, and dialogue.

Internal bots with confidentiality of data being a top concern.

For a more thorough examination of the method, see Evol-Instruct paper.

Want enterprise-grade performance? Compare WizardLM to other options for scale and latency, such as the Mistral enterprise model.

So the query here is, “WizardLM vs ChatGPT—who wins?” The answer depends on priorities.

FeatureWizardLMChatGPTInstruction FollowingEvol-Instruct, best at complicated tasksStrong, but frozen in OpenAI trainingEnvironmentAccess ModelSelf-hosted on-premise, offlineCloud API onlyCustomizationFine-tune locallyLimited by system promptsPrivacyData does not leave your serversDepends on third party infrastructureHardware NeedsGPU neededZero hardware needed

WizardLM has an edge for transparency-conscious companies, but everywhre else there’s still ChatGPT again for instant and hassle free interaction.

For some broader context, look at how WizardLM’s scores compare to Falcon open models in open-source rankings.

A thriving ecosystem surrounds WizardLM. Join their heading on Discourse for prompt tips, bug fixes and to showcase your project! There are live tutorials, fine tuning scripts and trouble shooting sessions on Dataloop’s discord meet-ups and forums regularly.

Benefits of participation:

Build checkpoints and quantizations faster.

The sharing of best practices in latency and cost reduction.

Working together on research papers and hackathons.

WizardLM is both open-source like Sia and performant and has community momentum of its own. WizardLM has you covered regardless of whether you build private chatbots, write technical docs, or research AI:

Accuracy thanks to Evol-Instruct training.

Customizability by deployment and tuning at local.

Scalable in multiple model sizes to best trade speed and accuracy for your hardware.

Ready to try it? Grab a model, write your first prompt and experience language generation at the next level.

Q: How big is the smallest WizardLM model?

A: The 7B model is approximately 14 GB, FP16 and can be run on a GPU with 12–16 GB.

Q: Is it possible to fine-tune WizardLM on other data?

A: Yes. And you can train it on niche domains by fine-tuning with full parameters, because it is open-source.

Q: Can WizardLM handle multi-turn dialogues?

A: Absolutely. It keeps track of context across long dialogues and can be used for chatbots or interactive agents.

Q: What if I don’t have a GPU?

A: Yes, you can still run smaller quantized models on CPU but inference will be slower. Alternatively, deploy to cloud GPUs.

Enjoyed this guide? Sound off in the comments, community members and let us know how WizardLM is driving your projects!