Source: Ollama
WizardLM uses Evol-Instruct — a training methodology that progressively generates harder, more complex instructions — to fine-tune models that handle demanding, multi-step tasks better than standard instruction-tuned alternatives. Deploy privately via Ollama.
Source: Ollama
WizardLM is a series of instruction-tuned language models from Microsoft Research, built using a training technique called Evol-Instruct. Instead of training on a static dataset of human-written instructions, Evol-Instruct uses a language model to iteratively rewrite simple instructions into progressively more complex, constrained, and nuanced versions — generating training data that covers a far wider range of instruction difficulty than human annotation alone can produce.
The result is a model unusually capable of handling demanding, multi-step instructions: tasks with many constraints, detailed specifications, complex conditional logic, or multiple interdependent requirements. Where standard instruction-tuned models tend to simplify or partially ignore complex inputs, WizardLM was specifically trained to follow them faithfully.
WizardLM 2 is the current generation, available in 7B, 13B, and 8x22B (Mixtral-based MoE) variants — all deployable via Ollama for private, on-premise use.
What Evol-Instruct means for you: Most instruction-tuned models are good at simple commands and struggle as instructions get more specific and complex. WizardLM was trained specifically on progressively harder instructions — so tasks that would cause a standard Mistral Instruct to drop constraints or go off-spec are more reliably handled by WizardLM. This matters most for automated pipelines, structured output generation, and multi-step document workflows.
Built on Mistral 7B. The efficient entry point — fits on a consumer GPU, fast enough for interactive use, and handles complex instructions well relative to its size. The right starting point for most local deployments.
Built on Mixtral 8x22B. The full-power variant for complex reasoning, long-form generation, and demanding instruction-following tasks. Requires substantial GPU infrastructure but delivers output quality competitive with frontier models on structured tasks.
Give WizardLM a prompt with ten specific requirements and it follows all ten. Standard instruction-tuned models increasingly drop constraints as prompts grow more complex. WizardLM's Evol-Instruct training specifically targets this failure mode — making it reliably useful for workflows where precision matters.
When you need output in a specific format — structured JSON, formatted reports, tables, code with particular specifications — WizardLM adheres to format requirements more consistently than models without this specialized training. For document automation and data extraction pipelines, reliable formatting reduces the need for post-processing.
WizardLM maintains context and constraint adherence across multi-turn conversations better than many alternatives — relevant for interactive workflows where users refine and extend instructions across multiple exchanges rather than issuing everything in a single prompt.
Tasks that require working through multiple constraints simultaneously — planning, analysis with specific scope limitations, content generation with numerous style and content rules — benefit from WizardLM's training distribution. The model has seen this kind of problem more than models trained on simpler data.
| Model | Training Approach | Complex Instruction Handling | Best At |
|---|---|---|---|
| WizardLM 2 7B | Evol-Instruct (progressive complexity) | Strong | Multi-constraint tasks, structured output |
| Zephyr 7B | DPO on preference data | Good | Direct chat, honest responses |
| Alpaca 7B | SFT on simple instructions | Basic | Simple instruction following, historical reference |
| Llama 3.1 8B Instruct | RLHF + SFT | Good | General capability, broad task coverage |
| Model | VRAM Required | RAM (CPU) | Notes |
|---|---|---|---|
| WizardLM 2 7B | ~5–6 GB | ~12 GB | Consumer GPU — fast and responsive |
| WizardLM 2 8x22B | ~90 GB | ~180 GB | Multi-GPU server or high-memory workstation |
Generate structured reports, contracts, proposals, and formatted documents from complex specifications. WizardLM's reliable constraint following reduces the manual cleanup that simpler models require.
Extract structured information from unstructured text according to detailed schemas. More reliable format adherence means cleaner output for downstream data processing.
Legal teams drafting documents with many specific requirements — jurisdiction constraints
Artwork and source references Hero image Developer reference WizardLM is developed by Microsoft Research. NoCloudGPT is an independent deployment service not affiliated with Microsoft Research. Model catalog See Ollama library: wizardlm for current tags and sizes.