Unsloth Inference

name: unsloth-inference description: Deploying fine-tuned models for production inference using native kernel optimization, vLLM, or SGLang. Triggers: inference, serving, vllm, sglang, for_inference, model merging, openai api.

Overview

Unsloth models can be deployed using native optimized inference or through production serving engines like vLLM and SGLang. Native inference is accelerated 2x via for_inference(), while production serving requires merging LoRA weights into the base model.

When to Use

• When performing local testing or simple application deployment.
• When building high-throughput production endpoints using vLLM or SGLang.
• When creating OpenAI-compatible APIs for drop-in replacement in existing apps.

Decision Tree

1. Is throughput the priority?
   • Yes: Merge to 16-bit and use vLLM.
2. Is VRAM for serving extremely limited?
   • Yes: Merge to 4-bit or use GGUF via Ollama.
3. Running simple Python inference?
   • Yes: Call FastLanguageModel.for_inference(model).

Workflows

Native Optimized Inference

1. Load fine-tuned model and tokenizer using FastLanguageModel.
2. Call FastLanguageModel.for_inference(model) to enable optimized kernels.
3. Use model.generate() with inputs formatted via the chat template.

Merging LoRA for vLLM Serving

1. Select export method: 'merged_16bit' (quality) or 'merged_4bit' (VRAM).
2. Run model.save_pretrained_merged("serving_model", tokenizer, save_method='merged_16bit').
3. Start vLLM server pointing to the 'serving_model' directory.

Non-Obvious Insights

• Calling FastLanguageModel.for_inference(model) is mandatory for speed; it performs on-the-fly weight fusion and enables specialized Triton kernels for the forward pass.
• Most production serving engines (vLLM, SGLang) cannot use LoRA adapters directly without performance hits; merging them into the base model during export is the standard best practice.
• The unsloth-cli provides a pre-built OpenAI-compatible endpoint, making it easy to serve models locally and test with standard API clients.

Evidence

• "Unsloth itself provides 2x faster inference natively as well, so always do not forget to call FastLanguageModel.for_inference(model)." Source
• "model.save_pretrained_merged("output", tokenizer, save_method = "merged_16bit")" Source

Scripts

• scripts/unsloth-inference_tool.py: Script for running local optimized inference.
• scripts/unsloth-inference_tool.js: Helper to test OpenAI-compatible endpoints.

Dependencies

• unsloth
• torch
• vllm (optional for production)
• sglang (optional for production)

References

• [[references/README.md]]