diff --git a/candle-core/src/display.rs b/candle-core/src/display.rs
new file mode 100644
index 00000000..e9053a1d
--- /dev/null
+++ b/candle-core/src/display.rs
@@ -0,0 +1,448 @@
+/// Pretty printing of tensors
+/// This implementation should be in line with the PyTorch version.
+/// https://github.com/pytorch/pytorch/blob/7b419e8513a024e172eae767e24ec1b849976b13/torch/_tensor_str.py
+use crate::{DType, Tensor, WithDType};
+use half::{bf16, f16};
+
+impl Tensor {
+    fn fmt_dt<T: WithDType + std::fmt::Display>(
+        &self,
+        f: &mut std::fmt::Formatter,
+    ) -> std::fmt::Result {
+        write!(f, "Tensor[")?;
+        match self.dims() {
+            [] => {
+                if let Ok(v) = self.to_scalar::<T>() {
+                    write!(f, "{v}")?
+                }
+            }
+            [s] if *s < 10 => {
+                if let Ok(vs) = self.to_vec1::<T>() {
+                    for (i, v) in vs.iter().enumerate() {
+                        if i > 0 {
+                            write!(f, ", ")?;
+                        }
+                        write!(f, "{v}")?;
+                    }
+                }
+            }
+            dims => {
+                write!(f, "dims ")?;
+                for (i, d) in dims.iter().enumerate() {
+                    if i > 0 {
+                        write!(f, ", ")?;
+                    }
+                    write!(f, "{d}")?;
+                }
+            }
+        }
+        write!(f, "; {}]", self.dtype().as_str())
+    }
+}
+
+impl std::fmt::Debug for Tensor {
+    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
+        match self.dtype() {
+            DType::U32 => self.fmt_dt::<u32>(f),
+            DType::BF16 => self.fmt_dt::<bf16>(f),
+            DType::F16 => self.fmt_dt::<f16>(f),
+            DType::F32 => self.fmt_dt::<f32>(f),
+            DType::F64 => self.fmt_dt::<f64>(f),
+        }
+    }
+}
+
+/*
+#[allow(dead_code)]
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+enum BasicKind {
+    Float,
+    Int,
+    Bool,
+    Complex,
+}
+
+impl BasicKind {
+    fn for_tensor(t: &Tensor) -> BasicKind {
+        match t.dtype() {
+            DType::U32 => BasicKind::Int,
+            DType::BF16 | DType::F16 | DType::F32 | DType::F64 => BasicKind::Float,
+        }
+    }
+}
+
+
+/// Options for Tensor pretty printing
+pub struct PrinterOptions {
+    precision: usize,
+    threshold: usize,
+    edge_items: usize,
+    line_width: usize,
+    sci_mode: Option<bool>,
+}
+
+lazy_static! {
+    static ref PRINT_OPTS: std::sync::Mutex<PrinterOptions> =
+        std::sync::Mutex::new(Default::default());
+}
+
+pub fn set_print_options(options: PrinterOptions) {
+    *PRINT_OPTS.lock().unwrap() = options
+}
+
+pub fn set_print_options_default() {
+    *PRINT_OPTS.lock().unwrap() = Default::default()
+}
+
+pub fn set_print_options_short() {
+    *PRINT_OPTS.lock().unwrap() = PrinterOptions {
+        precision: 2,
+        threshold: 1000,
+        edge_items: 2,
+        line_width: 80,
+        sci_mode: None,
+    }
+}
+
+pub fn set_print_options_full() {
+    *PRINT_OPTS.lock().unwrap() = PrinterOptions {
+        precision: 4,
+        threshold: usize::MAX,
+        edge_items: 3,
+        line_width: 80,
+        sci_mode: None,
+    }
+}
+
+impl Default for PrinterOptions {
+    fn default() -> Self {
+        Self {
+            precision: 4,
+            threshold: 1000,
+            edge_items: 3,
+            line_width: 80,
+            sci_mode: None,
+        }
+    }
+}
+
+trait TensorFormatter {
+    type Elem;
+
+    fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result;
+
+    fn value(tensor: &Tensor) -> Self::Elem;
+
+    fn values(tensor: &Tensor) -> Vec<Self::Elem>;
+
+    fn max_width(&self, to_display: &Tensor) -> usize {
+        let mut max_width = 1;
+        for v in Self::values(to_display) {
+            let mut fmt_size = FmtSize::new();
+            let _res = self.fmt(v, 1, &mut fmt_size);
+            max_width = usize::max(max_width, fmt_size.final_size())
+        }
+        max_width
+    }
+
+    fn write_newline_indent(i: usize, f: &mut std::fmt::Formatter) -> std::fmt::Result {
+        writeln!(f)?;
+        for _ in 0..i {
+            write!(f, " ")?
+        }
+        Ok(())
+    }
+
+    fn fmt_tensor(
+        &self,
+        t: &Tensor,
+        indent: usize,
+        max_w: usize,
+        summarize: bool,
+        po: &PrinterOptions,
+        f: &mut std::fmt::Formatter,
+    ) -> std::fmt::Result {
+        let size = t.size();
+        let edge_items = po.edge_items as i64;
+        write!(f, "[")?;
+        match size.as_slice() {
+            [] => self.fmt(Self::value(t), max_w, f)?,
+            [v] if summarize && *v > 2 * edge_items => {
+                for v in Self::values(&t.slice(0, None, Some(edge_items), 1)).into_iter() {
+                    self.fmt(v, max_w, f)?;
+                    write!(f, ", ")?;
+                }
+                write!(f, "...")?;
+                for v in Self::values(&t.slice(0, Some(-edge_items), None, 1)).into_iter() {
+                    write!(f, ", ")?;
+                    self.fmt(v, max_w, f)?
+                }
+            }
+            [_] => {
+                let elements_per_line = usize::max(1, po.line_width / (max_w + 2));
+                for (i, v) in Self::values(t).into_iter().enumerate() {
+                    if i > 0 {
+                        if i % elements_per_line == 0 {
+                            write!(f, ",")?;
+                            Self::write_newline_indent(indent, f)?
+                        } else {
+                            write!(f, ", ")?;
+                        }
+                    }
+                    self.fmt(v, max_w, f)?
+                }
+            }
+            _ => {
+                if summarize && size[0] > 2 * edge_items {
+                    for i in 0..edge_items {
+                        self.fmt_tensor(&t.get(i), indent + 1, max_w, summarize, po, f)?;
+                        write!(f, ",")?;
+                        Self::write_newline_indent(indent, f)?
+                    }
+                    write!(f, "...")?;
+                    Self::write_newline_indent(indent, f)?;
+                    for i in size[0] - edge_items..size[0] {
+                        self.fmt_tensor(&t.get(i), indent + 1, max_w, summarize, po, f)?;
+                        if i + 1 != size[0] {
+                            write!(f, ",")?;
+                            Self::write_newline_indent(indent, f)?
+                        }
+                    }
+                } else {
+                    for i in 0..size[0] {
+                        self.fmt_tensor(&t.get(i), indent + 1, max_w, summarize, po, f)?;
+                        if i + 1 != size[0] {
+                            write!(f, ",")?;
+                            Self::write_newline_indent(indent, f)?
+                        }
+                    }
+                }
+            }
+        }
+        write!(f, "]")?;
+        Ok(())
+    }
+}
+
+struct FloatFormatter {
+    int_mode: bool,
+    sci_mode: bool,
+    precision: usize,
+}
+
+struct FmtSize {
+    current_size: usize,
+}
+
+impl FmtSize {
+    fn new() -> Self {
+        Self { current_size: 0 }
+    }
+
+    fn final_size(self) -> usize {
+        self.current_size
+    }
+}
+
+impl std::fmt::Write for FmtSize {
+    fn write_str(&mut self, s: &str) -> std::fmt::Result {
+        self.current_size += s.len();
+        Ok(())
+    }
+}
+
+impl FloatFormatter {
+    fn new(t: &Tensor, po: &PrinterOptions) -> Self {
+        let mut int_mode = true;
+        let mut sci_mode = false;
+
+        let _guard = crate::no_grad_guard();
+        let t = t.to_device(crate::Device::Cpu);
+
+        // Rather than containing all values, this should only include
+        // values that end up being displayed according to [threshold].
+        let nonzero_finite_vals = {
+            let t = t.reshape([-1]);
+            t.masked_select(&t.isfinite().logical_and(&t.ne(0.)))
+        };
+
+        let values = Vec::<f64>::try_from(&nonzero_finite_vals).unwrap();
+        if nonzero_finite_vals.numel() > 0 {
+            let nonzero_finite_abs = nonzero_finite_vals.abs();
+            let nonzero_finite_min = nonzero_finite_abs.min().double_value(&[]);
+            let nonzero_finite_max = nonzero_finite_abs.max().double_value(&[]);
+
+            for &value in values.iter() {
+                if value.ceil() != value {
+                    int_mode = false;
+                    break;
+                }
+            }
+
+            sci_mode = nonzero_finite_max / nonzero_finite_min > 1000.
+                || nonzero_finite_max > 1e8
+                || nonzero_finite_min < 1e-4
+        }
+
+        match po.sci_mode {
+            None => {}
+            Some(v) => sci_mode = v,
+        }
+        Self {
+            int_mode,
+            sci_mode,
+            precision: po.precision,
+        }
+    }
+}
+
+impl TensorFormatter for FloatFormatter {
+    type Elem = f64;
+
+    fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result {
+        if self.sci_mode {
+            write!(
+                f,
+                "{v:width$.prec$e}",
+                v = v,
+                width = max_w,
+                prec = self.precision
+            )
+        } else if self.int_mode {
+            if v.is_finite() {
+                write!(f, "{v:width$.0}.", v = v, width = max_w - 1)
+            } else {
+                write!(f, "{v:max_w$.0}")
+            }
+        } else {
+            write!(
+                f,
+                "{v:width$.prec$}",
+                v = v,
+                width = max_w,
+                prec = self.precision
+            )
+        }
+    }
+
+    fn value(tensor: &Tensor) -> Self::Elem {
+        tensor.double_value(&[])
+    }
+
+    fn values(tensor: &Tensor) -> Vec<Self::Elem> {
+        Vec::<Self::Elem>::try_from(tensor.reshape(-1)).unwrap()
+    }
+}
+
+struct IntFormatter;
+
+impl TensorFormatter for IntFormatter {
+    type Elem = i64;
+
+    fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result {
+        write!(f, "{v:max_w$}")
+    }
+
+    fn value(tensor: &Tensor) -> Self::Elem {
+        tensor.int64_value(&[])
+    }
+
+    fn values(tensor: &Tensor) -> Vec<Self::Elem> {
+        Vec::<Self::Elem>::try_from(tensor.reshape(-1)).unwrap()
+    }
+}
+
+struct BoolFormatter;
+
+impl TensorFormatter for BoolFormatter {
+    type Elem = bool;
+
+    fn fmt<T: std::fmt::Write>(&self, v: Self::Elem, max_w: usize, f: &mut T) -> std::fmt::Result {
+        let v = if v { "true" } else { "false" };
+        write!(f, "{v:max_w$}")
+    }
+
+    fn value(tensor: &Tensor) -> Self::Elem {
+        tensor.int64_value(&[]) != 0
+    }
+
+    fn values(tensor: &Tensor) -> Vec<Self::Elem> {
+        Vec::<Self::Elem>::try_from(tensor.reshape(-1)).unwrap()
+    }
+}
+
+fn get_summarized_data(t: &Tensor, edge_items: i64) -> Tensor {
+    let size = t.size();
+    if size.is_empty() {
+        t.shallow_clone()
+    } else if size.len() == 1 {
+        if size[0] > 2 * edge_items {
+            Tensor::cat(
+                &[
+                    t.slice(0, None, Some(edge_items), 1),
+                    t.slice(0, Some(-edge_items), None, 1),
+                ],
+                0,
+            )
+        } else {
+            t.shallow_clone()
+        }
+    } else if size[0] > 2 * edge_items {
+        let mut vs: Vec<_> = (0..edge_items)
+            .map(|i| get_summarized_data(&t.get(i), edge_items))
+            .collect();
+        for i in (size[0] - edge_items)..size[0] {
+            vs.push(get_summarized_data(&t.get(i), edge_items))
+        }
+        Tensor::stack(&vs, 0)
+    } else {
+        let vs: Vec<_> = (0..size[0])
+            .map(|i| get_summarized_data(&t.get(i), edge_items))
+            .collect();
+        Tensor::stack(&vs, 0)
+    }
+}
+
+impl std::fmt::Display for Tensor {
+    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
+        if self.defined() {
+            let po = PRINT_OPTS.lock().unwrap();
+            let summarize = self.numel() > po.threshold;
+            let basic_kind = BasicKind::for_tensor(self);
+            let to_display = if summarize {
+                get_summarized_data(self, po.edge_items as i64)
+            } else {
+                self.shallow_clone()
+            };
+            match basic_kind {
+                BasicKind::Int => {
+                    let tf = IntFormatter;
+                    let max_w = tf.max_width(&to_display);
+                    tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
+                    writeln!(f)?;
+                }
+                BasicKind::Float => {
+                    let tf = FloatFormatter::new(&to_display, &po);
+                    let max_w = tf.max_width(&to_display);
+                    tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
+                    writeln!(f)?;
+                }
+                BasicKind::Bool => {
+                    let tf = BoolFormatter;
+                    let max_w = tf.max_width(&to_display);
+                    tf.fmt_tensor(self, 1, max_w, summarize, &po, f)?;
+                    writeln!(f)?;
+                }
+                BasicKind::Complex => {}
+            };
+            let kind = match self.f_kind() {
+                Ok(kind) => format!("{kind:?}"),
+                Err(err) => format!("{err:?}"),
+            };
+            write!(f, "Tensor[{:?}, {}]", self.size(), kind)
+        } else {
+            write!(f, "Tensor[Undefined]")
+        }
+    }
+}
+*/
diff --git a/candle-core/src/lib.rs b/candle-core/src/lib.rs
index b220dfb9..2084d7ca 100644
--- a/candle-core/src/lib.rs
+++ b/candle-core/src/lib.rs
@@ -3,6 +3,7 @@ mod cpu_backend;
 #[cfg(feature = "cuda")]
 mod cuda_backend;
 mod device;
+mod display;
 mod dtype;
 mod dummy_cuda_backend;
 mod error;
diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index cb968487..fc67ae94 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -44,12 +44,6 @@ impl std::ops::Deref for Tensor {
     }
 }
 
-impl std::fmt::Debug for Tensor {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "[{:?}, {:?}]", &self.shape().dims(), self.device())
-    }
-}
-
 macro_rules! unary_op {
     ($fn_name:ident, $op_name:ident) => {
         pub fn $fn_name(&self) -> Result<Self> {