--- description: Upload and use LoRA adapters to get fine-tuned inference on Workers AI. title: Using LoRA adapters image: https://developers.cloudflare.com/og-docs.png --- [Skip to content](#main-content) > Documentation Index > Fetch the complete documentation index at: https://developers.cloudflare.com/workers-ai/llms.txt > Use this file to discover all available pages before exploring further. # Using LoRA adapters Last updated Aug 24, 2026|Copy as Markdown|[View as Markdown](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/features/fine-tunes/loras/index.md)|[Agent setup](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/agent-setup/) Workers AI supports fine-tuned inference with adapters trained with [Low-Rank Adaptation โ†—](https://blog.cloudflare.com/fine-tuned-inference-with-loras). This feature is in open beta and free during this period. ## Limitations * We only support LoRAs for a [variety of models](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/models/?capabilities=LoRA) (must not be quantized) * Adapter must be trained with rank `r <=8` as well as larger ranks if up to 32\. You can check the rank of a pre-trained LoRA adapter through the adapter's `config.json` file * LoRA adapter file must be < 300MB * LoRA adapter files must be named `adapter_config.json` and `adapter_model.safetensors` exactly * You can test up to 100 LoRA adapters per account --- ## Choosing compatible LoRA adapters ### Finding open-source LoRA adapters We have started a [Hugging Face Collection โ†—](https://huggingface.co/collections/Cloudflare/workers-ai-compatible-loras-6608dd9f8d305a46e355746e) that lists a few LoRA adapters that are compatible with Workers AI. Generally, any LoRA adapter that fits our limitations above should work. ### Training your own LoRA adapters To train your own LoRA adapter, follow the [tutorial](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/workers-ai/guides/tutorials/fine-tune-models-with-autotrain/). --- ## Uploading LoRA adapters In order to run inference with LoRAs on Workers AI, you'll need to create a new fine tune on your account and upload your adapter files. You should have a `adapter_model.safetensors` file with model weights and `adapter_config.json` with your config information. _Note that we only accept adapter files in these types._ Right now, you can't edit a fine tune's asset files after you upload it. We will support this soon, but for now you will need to create a new fine tune and upload files again if you would like to use a new LoRA. Before you upload your LoRA adapter, you'll need to edit your `adapter_config.json` file to include `model_type` as one of `mistral`, `gemma` or `llama` like below. ```json { "alpha_pattern": {}, "auto_mapping": null, ... "target_modules": [ "q_proj", "v_proj" ], "task_type": "CAUSAL_LM", "model_type": "mistral", } ``` ### Wrangler You can create a finetune and upload your LoRA adapter via wrangler with the following commands: ```bash npx wrangler ai finetune create #๐ŸŒ€ Creating new finetune "test-lora" for model "@cf/mistral/mistral-7b-instruct-v0.2-lora"... #๐ŸŒ€ Uploading file "/Users/abcd/Downloads/adapter_config.json" to "test-lora"... #๐ŸŒ€ Uploading file "/Users/abcd/Downloads/adapter_model.safetensors" to "test-lora"... #โœ… Assets uploaded, finetune "test-lora" is ready to use. npx wrangler ai finetune list โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ finetune_id โ”‚ name โ”‚ description โ”‚ โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค โ”‚ 00000000-0000-0000-0000-000000000000 โ”‚ test-lora โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ ``` ### REST API Alternatively, you can use our REST API to create a finetune and upload your adapter files. You will need a Cloudflare API Token with `Workers AI: Edit` permissions to make calls to our REST API, which you can generate via the Cloudflare Dashboard. #### Creating a fine-tune on your account Required API token permissions At least one of the following [token permissions](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/fundamentals/api/reference/permissions/) is required: * `Workers AI Write` ```bash curl "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/ai/finetunes" \ --request POST \ --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \ --json '{ "model": "SUPPORTED_MODEL_NAME", "name": "FINETUNE_NAME", "description": "OPTIONAL_DESCRIPTION" }' ``` #### Uploading your adapter weights and config You have to call the upload endpoint each time you want to upload a new file, so you usually run this once for `adapter_model.safetensors` and once for `adapter_config.json`. Make sure you include the `@` before your path to files. You can either use the finetune `name` or `id` that you used when you created the fine tune. ```bash ## Input: finetune_id, adapter_model.safetensors, then adapter_config.json ## Output: success true/false curl -X POST https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/finetunes/{FINETUNE_ID}/finetune-assets/ \ -H 'Authorization: Bearer {API_TOKEN}' \ -H 'Content-Type: multipart/form-data' \ -F 'file_name=adapter_model.safetensors' \ -F 'file=@{PATH/TO/adapter_model.safetensors}' ``` #### List fine-tunes in your account You can call this method to confirm what fine-tunes you have created in your account Required API token permissions At least one of the following [token permissions](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/fundamentals/api/reference/permissions/) is required: * `Workers AI Write` * `Workers AI Read` ```bash curl "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/ai/finetunes" \ --request GET \ --header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" ``` ```json { "success": true, "result": [ [ { "id": "00000000-0000-0000-0000-000000000", "model": "@cf/meta-llama/llama-2-7b-chat-hf-lora", "name": "llama2-finetune", "description": "test" }, { "id": "00000000-0000-0000-0000-000000000", "model": "@cf/mistralai/mistral-7b-instruct-v0.2-lora", "name": "mistral-finetune", "description": "test" } ] ] } ``` --- ## Running inference with LoRAs To make inference requests and apply the LoRA adapter, you will need your model and finetune `name` or `id`. You should use the chat template that your LoRA was trained on, but you can try running it with `raw: true` and the messages template like below. ```javascript const response = await env.AI.run( "@cf/mistralai/mistral-7b-instruct-v0.2-lora", //the model supporting LoRAs { messages: [{ role: "user", content: "Hello world" }], raw: true, //skip applying the default chat template lora: "00000000-0000-0000-0000-000000000", //the finetune id OR name }, ); ``` ```bash curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/mistral/mistral-7b-instruct-v0.2-lora \ -H 'Authorization: Bearer {API_TOKEN}' \ -d '{ "messages": [{"role": "user", "content": "Hello world"}], "raw": "true", "lora": "00000000-0000-0000-0000-000000000" }' ``` Was this helpful? YesNo ## On this page [![](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/_astro/logo.te5VL_aD.svg)Docs](https://docs-durable-objects-instance-replaced-errors.previews.developers.cloudflare.com/) ```json {"@context":"https://schema.org","@type":"TechArticle","@id":"https://developers.cloudflare.com/workers-ai/features/fine-tunes/loras/#page","headline":"Fine-tuned inference with LoRA adapters ยท Cloudflare Workers AI docs","description":"Upload and use LoRA adapters to get fine-tuned inference on Workers AI.","url":"https://developers.cloudflare.com/workers-ai/features/fine-tunes/loras/","inLanguage":"en","image":"https://developers.cloudflare.com/og-docs.png","dateModified":"2026-08-24","publisher":{"@type":"Organization","name":"Cloudflare","description":"One platform for your apps, agents, and workforce. 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