diff --git a/packages/tasks/src/model-libraries-snippets.ts b/packages/tasks/src/model-libraries-snippets.ts
index 15e1774fe7..dad22a8277 100644
--- a/packages/tasks/src/model-libraries-snippets.ts
+++ b/packages/tasks/src/model-libraries-snippets.ts
@@ -65,7 +65,7 @@ from audioseal import AudioSeal
 model = AudioSeal.load_generator("${model.id}")
 # pass a tensor (tensor_wav) of shape (batch, channels, samples) and a sample rate
 wav, sr = tensor_wav, 16000
-	
+
 watermark = model.get_watermark(wav, sr)
 watermarked_audio = wav + watermark`;
 
@@ -73,7 +73,7 @@ watermarked_audio = wav + watermark`;
 from audioseal import AudioSeal
 
 detector = AudioSeal.load_detector("${model.id}")
-	
+
 result, message = detector.detect_watermark(watermarked_audio, sr)`;
 	return [watermarkSnippet, detectorSnippet];
 };
@@ -580,7 +580,7 @@ export const cartesia_mlx = (model: ModelData): string[] => [
 import cartesia_mlx as cmx
 
 model = cmx.from_pretrained("${model.id}")
-model.set_dtype(mx.float32)   
+model.set_dtype(mx.float32)
 
 prompt = "Rene Descartes was"
 
@@ -703,7 +703,7 @@ export const keras = (model: ModelData): string[] => [
 	`# Available backend options are: "jax", "torch", "tensorflow".
 import os
 os.environ["KERAS_BACKEND"] = "jax"
-	
+
 import keras
 
 model = keras.saving.load_model("hf://${model.id}")
@@ -867,7 +867,7 @@ model.score("query", ["doc1", "doc2", "doc3"])`,
 from lightning_ir import BiEncoderModule, CrossEncoderModule
 
 # depending on the model type, use either BiEncoderModule or CrossEncoderModule
-model = BiEncoderModule("${model.id}") 
+model = BiEncoderModule("${model.id}")
 # model = CrossEncoderModule("${model.id}")
 
 model.score("query", ["doc1", "doc2", "doc3"])`,
@@ -916,7 +916,7 @@ pip install -e .[smolvla]`,
 			`# Launch finetuning on your dataset
 python lerobot/scripts/train.py \\
 --policy.path=${model.id} \\
---dataset.repo_id=lerobot/svla_so101_pickplace \\ 
+--dataset.repo_id=lerobot/svla_so101_pickplace \\
 --batch_size=64 \\
 --steps=20000 \\
 --output_dir=outputs/train/my_smolvla \\
@@ -927,7 +927,7 @@ python lerobot/scripts/train.py \\
 		if (model.id !== "lerobot/smolvla_base") {
 			// Inference snippet (only if not base model)
 			smolvlaSnippets.push(
-				`# Run the policy using the record function	
+				`# Run the policy using the record function
 python -m lerobot.record \\
   --robot.type=so101_follower \\
   --robot.port=/dev/ttyACM0 \\ # <- Use your port
@@ -1067,7 +1067,7 @@ for res in output:
 	}
 
 	return [
-		`# Please refer to the document for information on how to use the model. 
+		`# Please refer to the document for information on how to use the model.
 # https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/module_overview.html`,
 	];
 };
@@ -1103,7 +1103,7 @@ wan_i2v = WanI2V(
 
 export const pyannote_audio_pipeline = (model: ModelData): string[] => [
 	`from pyannote.audio import Pipeline
-  
+
 pipeline = Pipeline.from_pretrained("${model.id}")
 
 # inference on the whole file
@@ -1142,7 +1142,7 @@ export const pyannote_audio = (model: ModelData): string[] => {
 
 export const relik = (model: ModelData): string[] => [
 	`from relik import Relik
- 
+
 relik = Relik.from_pretrained("${model.id}")`,
 ];
 
@@ -1326,7 +1326,7 @@ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
 	const video_predictor = `# Use SAM2 with videos
 import torch
 from sam2.sam2_video_predictor import SAM2VideoPredictor
-	
+
 predictor = SAM2VideoPredictor.from_pretrained(${model.id})
 
 with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
@@ -1885,6 +1885,115 @@ export const model2vec = (model: ModelData): string[] => [
 model = StaticModel.from_pretrained("${model.id}")`,
 ];
 
+// Main entry point for generating Pruna code snippets based on the model's library
+export const pruna = (model: ModelData): string[] => {
+	// If the model uses diffusers, use the diffusers-specific snippet
+	if (model.library_name === "diffusers") {
+		return pruna_diffusers(model);
+	// If the model uses transformers, use the transformers-specific snippet
+	} else if (model.library_name === "transformers") {
+		return pruna_transformers(model);
+	}
+	// Fallback to a default snippet
+	return pruna_default(model);
+};
+
+// Generates Pruna code snippets for diffusers models
+export const pruna_diffusers = (model: ModelData): string[] => {
+	const diffusersSnippets = diffusers(model);
+
+	// Replace all pipeline class names with PrunaModel
+	const rewrittenSnippets = diffusersSnippets.map(snippet =>
+		snippet
+			// First, replace ALL pipeline class references (including in imports)
+			// Handle classes that end with "Pipeline" (like DiffusionPipeline)
+			.replace(/\b(\w+Pipeline)\b/g, "PrunaModel")
+			// Handle classes that contain "Pipeline" but don't end with it (like AutoPipelineForInpainting)
+			.replace(/\b(\w+Pipeline\w+)\b/g, "PrunaModel")
+			// Then handle imports - remove pipeline imports completely
+			.replace(/from diffusers import ([^,\n]*PrunaModel[^,\n]*)/g, "")
+			.replace(/from diffusers import ([^,\n]+),?\s*([^,\n]*PrunaModel[^,\n]*)/g, "from diffusers import $1")
+			// Clean up any empty import lines
+			.replace(/from diffusers import\s*\n/g, "")
+			.replace(/from diffusers import\s*$/g, "")
+			// Clean up any double newlines
+			.replace(/\n\n+/g, "\n")
+			// Fix the PrunaModel import issue - replace any remaining "from diffusers import PrunaModel"
+			.replace(/from diffusers import PrunaModel/g, "from pruna import PrunaModel")
+			// Handle cases where PrunaModel is imported alongside other classes
+			.replace(/from diffusers import ([^,\n]+), PrunaModel/g, "from diffusers import $1")
+			.replace(/from diffusers import PrunaModel, ([^,\n]+)/g, "from diffusers import $1")
+			.trim()
+	);
+
+	// Always add the PrunaModel import at the beginning of each snippet
+	const finalSnippets = rewrittenSnippets.map(snippet => {
+		if (!/^from pruna import PrunaModel/m.test(snippet)) {
+			return `from pruna import PrunaModel\n${snippet}`;
+		}
+		return snippet;
+	});
+
+	return finalSnippets;
+};
+
+// Generates Pruna code snippets for transformers models
+export const pruna_transformers = (model: ModelData): string[] => {
+	const info = model.transformersInfo;
+	const transformersSnippets = transformers(model);
+
+	// Replace pipeline import and usage with PrunaModel
+	const rewrittenSnippets = transformersSnippets.map(snippet =>
+		snippet.replace(/from transformers import pipeline/g, "from pruna import PrunaModel")
+	);
+	const prunaSnippets = rewrittenSnippets.map(snippet =>
+		snippet.replace(/pipeline\([^\)]*\)/g, `PrunaModel.from_pretrained("${model.id}")`)
+	);
+
+	// If transformersInfo is not available, just return the basic replacements
+	if (!info) {
+		return prunaSnippets;
+	}
+
+	// Further clean up the snippet to remove references to the original auto_model
+	const cleanedSnippets = prunaSnippets.map(snippet => {
+		let s = snippet
+			// Remove any import statements for the original auto_model
+			.replace(new RegExp(`from transformers import ${info.auto_model}\\n?`, "g"), "")
+			// Replace original from_pretrained calls with Pruna's
+			.replace(new RegExp(`${info.auto_model}.from_pretrained`, "g"), "PrunaModel.from_pretrained")
+			// Remove any extra auto_model arguments in function imports
+			// Only remove ", auto_model" if it's in a line with both "from" and "import"
+			.replace(
+				new RegExp(
+					`^.*from.*import.*(, *${info.auto_model})+.*$`,
+					"gm"
+				),
+				line => line.replace(new RegExp(`, *${info.auto_model}`, "g"), "")
+			);
+		return s;
+	});
+
+	// Add 'from pruna import PrunaModel' at the top if not present
+	const finalSnippets = cleanedSnippets.map(snippet => {
+		if (!/^from pruna import PrunaModel/m.test(snippet)) {
+			return `from pruna import PrunaModel\n${snippet}`;
+		}
+		return snippet;
+	});
+
+	return finalSnippets;
+};
+
+// Default Pruna snippet for unsupported or unknown libraries
+export const pruna_default = (model: ModelData): string[] => [
+	`from pruna import PrunaModel
+
+model = PrunaModel.from_pretrained("${model.id}")
+`,
+];
+
+
 export const nemo = (model: ModelData): string[] => {
 	let command: string[] | undefined = undefined;
 	// Resolve the tag to a nemo domain/sub-domain
@@ -1896,7 +2005,7 @@ export const nemo = (model: ModelData): string[] => {
 };
 
 export const outetts = (model: ModelData): string[] => {
-	// Don’t show this block on GGUF / ONNX mirrors
+	// Don't show this block on GGUF / ONNX mirrors
 	const t = model.tags ?? [];
 	if (t.includes("gguf") || t.includes("onnx")) return [];
 
@@ -1904,11 +2013,11 @@ export const outetts = (model: ModelData): string[] => {
 	return [
 		`
   import outetts
-  
+
   enum = outetts.Models("${model.id}".split("/", 1)[1])       # VERSION_1_0_SIZE_1B
   cfg  = outetts.ModelConfig.auto_config(enum, outetts.Backend.HF)
   tts  = outetts.Interface(cfg)
-  
+
   speaker = tts.load_default_speaker("EN-FEMALE-1-NEUTRAL")
   tts.generate(
 	  outetts.GenerationConfig(
@@ -1943,7 +2052,7 @@ wav = model.generate(descriptions)  # generates 3 samples.`,
 
 const magnet = (model: ModelData): string[] => [
 	`from audiocraft.models import MAGNeT
-	
+
 model = MAGNeT.get_pretrained("${model.id}")
 
 descriptions = ['disco beat', 'energetic EDM', 'funky groove']
@@ -1952,7 +2061,7 @@ wav = model.generate(descriptions)  # generates 3 samples.`,
 
 const audiogen = (model: ModelData): string[] => [
 	`from audiocraft.models import AudioGen
-	
+
 model = AudioGen.get_pretrained("${model.id}")
 model.set_generation_params(duration=5)  # generate 5 seconds.
 descriptions = ['dog barking', 'sirene of an emergency vehicle', 'footsteps in a corridor']
@@ -1985,7 +2094,7 @@ brew install whisperkit-cli
 
 # View all available inference options
 whisperkit-cli transcribe --help
-	
+
 # Download and run inference using whisper base model
 whisperkit-cli transcribe --audio-path /path/to/audio.mp3
 
diff --git a/packages/tasks/src/model-libraries.ts b/packages/tasks/src/model-libraries.ts
index d267b199dd..bdf55ccb95 100644
--- a/packages/tasks/src/model-libraries.ts
+++ b/packages/tasks/src/model-libraries.ts
@@ -776,6 +776,14 @@ export const MODEL_LIBRARIES_UI_ELEMENTS = {
 		filter: false,
 		countDownloads: `path_extension:"pth"`,
 	},
+	"pruna-ai": {
+		prettyLabel: "Pruna AI",
+		repoName: "Pruna AI",
+		repoUrl: "https://github.com/Pruna-AI/pruna-ai",
+		snippets: snippets.pruna,
+		docsUrl: "https://docs.pruna.ai",
+		filter: false,
+	},
 	pxia: {
 		prettyLabel: "pxia",
 		repoName: "pxia",