diff --git a/catgrad-llm/examples/llama/main.rs b/catgrad-llm/examples/llama/main.rs
index 650f088..b80946a 100644
--- a/catgrad-llm/examples/llama/main.rs
+++ b/catgrad-llm/examples/llama/main.rs
@@ -2,11 +2,14 @@ use anyhow::Result;
 use catgrad::interpreter::backend::candle::CandleBackend;
 use catgrad::interpreter::backend::ndarray::NdArrayBackend;
 use catgrad::prelude::*;
+use chrono::Local;
 use clap::{Parser, ValueEnum};
+use minijinja::{Environment, context};
+use minijinja_contrib::pycompat::unknown_method_callback;
 use std::io::Write;
 use std::path::PathBuf;
 
-use catgrad_llm::utils::{get_model, load_model};
+use catgrad_llm::utils::{get_model, get_model_chat_template, load_model};
 
 #[derive(Parser, Debug)]
 struct Args {
@@ -23,6 +26,9 @@ struct Args {
     /// Initial prompt
     #[arg(short = 'p', long, default_value = "Category theory is")]
     prompt: String,
+    /// Pass raw prompt without chat template
+    #[arg(long)]
+    raw: bool,
     /// Tokens to generate
     #[arg(short = 's', long, default_value_t = 1)]
     max_seq_len: usize,
@@ -41,6 +47,13 @@ struct Args {
     /// Load model from a previously dumped JSON graph
     #[arg(long)]
     load: Option<PathBuf>,
+    /// Benchmark
+    #[arg(
+        long,
+        num_args = 2,
+        value_names = ["PP", "TG"]
+    )]
+    bench: Option<Vec<usize>>,
 }
 
 #[derive(Copy, Clone, Debug, ValueEnum)]
@@ -49,6 +62,10 @@ enum BackendChoice {
     Candle,
 }
 
+fn strftime_now(format_str: String) -> String {
+    Local::now().format(&format_str).to_string()
+}
+
 /// Construct, shapecheck, and interpret the a given LLM using the selected backend.
 fn main() -> Result<()> {
     env_logger::init();
@@ -63,13 +80,43 @@ fn run_with_backend<B: interpreter::Backend>(args: &Args, backend: B) -> Result<
     let (parameter_values, parameter_types, config, tokenizer) =
         load_model(&args.model_name, &args.revision, &backend)?;
 
+    let chat_template =
+        get_model_chat_template(&args.model_name, &args.revision).unwrap_or_default();
+
+    let benchmarking = args.bench.is_some();
+    let mut pp = 0;
+    let mut tg = 0;
+    let mut max_seq_len = args.max_seq_len;
+
+    let prompt = if let Some(bench) = &args.bench {
+        pp = bench[0];
+        tg = bench[1];
+        max_seq_len = tg;
+        println!(
+            "Benchmarking {} with prefill size {} and sequence length {}",
+            args.model_name, pp, tg
+        );
+        "The".repeat(pp)
+    } else if chat_template.is_empty() || args.raw {
+        args.prompt.clone()
+    } else {
+        let mut env = Environment::new();
+        env.set_unknown_method_callback(unknown_method_callback);
+        env.add_function("strftime_now", strftime_now);
+        env.add_template("chat", &chat_template).unwrap();
+        let tmpl = env.get_template("chat").unwrap();
+        tmpl.render(
+            context!(messages => vec![ context!(role => "user",content => args.prompt)], add_generation_prompt => true),
+        )?
+    };
+
     let encoding = tokenizer
-        .encode(args.prompt.clone(), true)
+        .encode(prompt.clone(), true)
         .map_err(|err| anyhow::anyhow!("check error {:?}", err))?;
 
     let mut token_ids = encoding.get_ids().to_vec();
 
-    let max_sequence_length = args.max_seq_len + token_ids.len();
+    let max_sequence_length = max_seq_len + token_ids.len();
     let model = get_model(&config, max_sequence_length)?;
 
     let typed_term = if let Some(load_path) = &args.load {
@@ -101,29 +148,52 @@ fn run_with_backend<B: interpreter::Backend>(args: &Args, backend: B) -> Result<
             .map_err(|err| anyhow::anyhow!("check error {:?}", err))?;
     }
 
-    print!("{}", args.prompt);
-    let start_gen = std::time::Instant::now();
+    let mut generated_tokens = 0;
+    if !benchmarking {
+        print!("{}", prompt);
+    }
+    let mut start_gen = std::time::Instant::now();
+    let mut elapsed_pp = std::time::Duration::ZERO;
     let interpreter = interpreter::Interpreter::new(backend, env, parameter_values);
     // Run interpreter
-    for _ in 0..args.max_seq_len {
+    for i in 0..max_seq_len {
         let next_token_id = run_interpreter(&typed_term, &interpreter, &token_ids)?;
-        if config.get_eos_token_ids().contains(&(next_token_id as i32)) {
+        if i == 0 {
+            elapsed_pp = start_gen.elapsed();
+            start_gen = std::time::Instant::now();
+        }
+        generated_tokens += 1;
+        if config.get_eos_token_ids().contains(&(next_token_id as i32)) && !benchmarking {
             break;
         }
-        let decoded_token = tokenizer.decode(&[next_token_id], false).unwrap();
         token_ids.push(next_token_id);
-        print!("{}", decoded_token);
-        std::io::stdout().flush()?;
+        if !benchmarking {
+            let decoded_token = tokenizer.decode(&[next_token_id], false).unwrap();
+            print!("{}", decoded_token);
+            std::io::stdout().flush()?;
+        }
     }
 
     let elapsed_gen = start_gen.elapsed();
-    let generated_tokens = args.max_seq_len;
     println!(
         "\n{} tokens generated in {} seconds. ({:.2} tps)",
         generated_tokens,
-        elapsed_gen.as_secs(),
-        generated_tokens as f64 / elapsed_gen.as_secs_f64(),
+        (elapsed_pp + elapsed_gen).as_secs(),
+        generated_tokens as f64 / (elapsed_pp + elapsed_gen).as_secs_f64(),
     );
+
+    if benchmarking {
+        println!(
+            "PP {pp} in {} ms {:.2} tps",
+            elapsed_pp.as_millis(),
+            pp as f64 / elapsed_pp.as_secs_f64()
+        );
+        println!(
+            "TG {tg} in {} ms {:.2} tps",
+            elapsed_gen.as_millis(),
+            tg as f64 / elapsed_gen.as_secs_f64()
+        );
+    }
     Ok(())
 }