diff --git a/README.md b/README.md
index 90344b34..9bfa30d8 100644
--- a/README.md
+++ b/README.md
@@ -67,7 +67,7 @@ We also provide a some command line based examples using state of the art models
 - [StableLM-3B-4E1T](./candle-examples/examples/stable-lm/): a 3b general LLM
   pre-trained on 1T tokens of English and code datasets. Also supports
   StableLM-2, a 1.6b LLM trained on 2T tokens, as well as the code variants.
-- [Minimal Mamba](./candle-examples/examples/mamba-minimal/): a minimal
+- [Mamba](./candle-examples/examples/mamba/): an inference only
   implementation of the Mamba state space model.
 - [Mistral7b-v0.1](./candle-examples/examples/mistral/): a 7b general LLM with
   better performance than all publicly available 13b models as of 2023-09-28.
@@ -186,7 +186,7 @@ If you have an addition to this list, please submit a pull request.
         - Falcon.
         - StarCoder.
         - Phi 1, 1.5, and 2.
-        - Minimal Mamba
+        - Mamba, Minimal Mamba
         - Mistral 7b v0.1.
         - Mixtral 8x7b v0.1.
         - StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.
diff --git a/candle-examples/examples/mamba-minimal/README.md b/candle-examples/examples/mamba-minimal/README.md
index 0ce42123..46479828 100644
--- a/candle-examples/examples/mamba-minimal/README.md
+++ b/candle-examples/examples/mamba-minimal/README.md
@@ -2,6 +2,9 @@
 
 This is based on [mamba-minimal](https://github.com/johnma2006/mamba-minimal).
 
+Compared to the mamba example, this version can handle training but is much
+slower.
+
 ## Running the example
 
 ```bash
diff --git a/candle-examples/examples/mamba/README.md b/candle-examples/examples/mamba/README.md
new file mode 100644
index 00000000..507434a1
--- /dev/null
+++ b/candle-examples/examples/mamba/README.md
@@ -0,0 +1,17 @@
+# candle-mamba: Mamba implementation
+
+Candle implementation of *Mamba* [1] inference only. Mamba is an alternative to
+the transformer architecture. It leverages State Space Models (SSMs) with the
+goal of being computationally efficient on long sequences. The implementation is
+based on [mamba.rs](https://github.com/LaurentMazare/mamba.rs).
+
+- [1]. [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752).
+
+Compared to the mamba-minimal example, this version is far more efficient but
+would only work for inference.
+## Running the example
+
+```bash
+$ cargo run --example mamba-minimal --release -- --prompt "Mamba is the"
+```
+
diff --git a/candle-examples/examples/mamba/main.rs b/candle-examples/examples/mamba/main.rs
new file mode 100644
index 00000000..4802f960
--- /dev/null
+++ b/candle-examples/examples/mamba/main.rs
@@ -0,0 +1,299 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::{Error as E, Result};
+use clap::{Parser, ValueEnum};
+
+use candle_transformers::models::mamba::{Config, Model, State};
+
+use candle::{DType, Device, Tensor};
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_nn::VarBuilder;
+use candle_transformers::generation::LogitsProcessor;
+use hf_hub::{api::sync::Api, Repo, RepoType};
+use tokenizers::Tokenizer;
+
+struct TextGeneration {
+    model: Model,
+    config: Config,
+    device: Device,
+    tokenizer: TokenOutputStream,
+    logits_processor: LogitsProcessor,
+    repeat_penalty: f32,
+    repeat_last_n: usize,
+}
+
+impl TextGeneration {
+    #[allow(clippy::too_many_arguments)]
+    fn new(
+        model: Model,
+        config: Config,
+        tokenizer: Tokenizer,
+        seed: u64,
+        temp: Option<f64>,
+        top_p: Option<f64>,
+        repeat_penalty: f32,
+        repeat_last_n: usize,
+        device: &Device,
+    ) -> Self {
+        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
+        Self {
+            model,
+            config,
+            tokenizer: TokenOutputStream::new(tokenizer),
+            logits_processor,
+            repeat_penalty,
+            repeat_last_n,
+            device: device.clone(),
+        }
+    }
+
+    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
+        use std::io::Write;
+        self.tokenizer.clear();
+        let mut tokens = self
+            .tokenizer
+            .tokenizer()
+            .encode(prompt, true)
+            .map_err(E::msg)?
+            .get_ids()
+            .to_vec();
+        let mut generated_tokens = 0usize;
+        let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
+            Some(token) => token,
+            None => anyhow::bail!("cannot find the </s> token"),
+        };
+        let mut state = State::new(1, &self.config, &self.device)?;
+        let mut next_logits = None;
+        for &t in tokens.iter() {
+            let input = Tensor::new(&[t], &self.device)?;
+            let logits = self.model.forward(&input, &mut state)?;
+            next_logits = Some(logits);
+            if let Some(t) = self.tokenizer.next_token(t)? {
+                print!("{t}")
+            }
+        }
+        std::io::stdout().flush()?;
+
+        let start_gen = std::time::Instant::now();
+        for _ in 0..sample_len {
+            let logits = match next_logits.as_ref() {
+                Some(logits) => logits,
+                None => anyhow::bail!("cannot work on an empty prompt"),
+            };
+            let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
+            let logits = if self.repeat_penalty == 1. {
+                logits
+            } else {
+                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
+                candle_transformers::utils::apply_repeat_penalty(
+                    &logits,
+                    self.repeat_penalty,
+                    &tokens[start_at..],
+                )?
+            };
+            let next_token = self.logits_processor.sample(&logits)?;
+            tokens.push(next_token);
+            generated_tokens += 1;
+            if next_token == eos_token {
+                break;
+            }
+            if let Some(t) = self.tokenizer.next_token(next_token)? {
+                print!("{t}");
+                std::io::stdout().flush()?;
+            }
+
+            let input = Tensor::new(&[next_token], &self.device)?;
+            next_logits = Some(self.model.forward(&input, &mut state)?)
+        }
+        let dt = start_gen.elapsed();
+        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
+            print!("{rest}");
+        }
+        std::io::stdout().flush()?;
+        println!(
+            "\n{generated_tokens} tokens generated ({:.2} token/s)",
+            generated_tokens as f64 / dt.as_secs_f64(),
+        );
+        Ok(())
+    }
+}
+
+#[derive(Parser, ValueEnum, Clone, Copy, PartialEq, Eq, Debug)]
+enum Which {
+    Mamba130m,
+    Mamba370m,
+    Mamba790m,
+    Mamba1_4b,
+    Mamba2_8b,
+    Mamba2_8bSlimPj,
+}
+
+impl std::fmt::Display for Which {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "{:?}", self)
+    }
+}
+
+impl Which {
+    fn model_id(&self) -> &'static str {
+        match self {
+            Self::Mamba130m => "state-spaces/mamba-130m",
+            Self::Mamba370m => "state-spaces/mamba-370m",
+            Self::Mamba790m => "state-spaces/mamba-790m",
+            Self::Mamba1_4b => "state-spaces/mamba-1.4b",
+            Self::Mamba2_8b => "state-spaces/mamba-2.8b",
+            Self::Mamba2_8bSlimPj => "state-spaces/mamba-2.8b-slimpj'",
+        }
+    }
+
+    fn revision(&self) -> &'static str {
+        match self {
+            Self::Mamba130m
+            | Self::Mamba370m
+            | Self::Mamba790m
+            | Self::Mamba1_4b
+            | Self::Mamba2_8bSlimPj => "refs/pr/1",
+            Self::Mamba2_8b => "refs/pr/4",
+        }
+    }
+}
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+
+    #[arg(long)]
+    prompt: String,
+
+    /// The temperature used to generate samples.
+    #[arg(long)]
+    temperature: Option<f64>,
+
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
+    /// The seed to use when generating random samples.
+    #[arg(long, default_value_t = 299792458)]
+    seed: u64,
+
+    /// The length of the sample to generate (in tokens).
+    #[arg(long, short = 'n', default_value_t = 5000)]
+    sample_len: usize,
+
+    #[arg(long, default_value = "mamba130m")]
+    which: Which,
+
+    #[arg(long)]
+    model_id: Option<String>,
+
+    #[arg(long)]
+    revision: Option<String>,
+
+    #[arg(long)]
+    tokenizer_file: Option<String>,
+
+    #[arg(long)]
+    weight_files: Option<String>,
+
+    #[arg(long)]
+    config_file: Option<String>,
+
+    /// Penalty to be applied for repeating tokens, 1. means no penalty.
+    #[arg(long, default_value_t = 1.1)]
+    repeat_penalty: f32,
+
+    /// The context size to consider for the repeat penalty.
+    #[arg(long, default_value_t = 64)]
+    repeat_last_n: usize,
+}
+
+fn main() -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+    let _guard = if args.tracing {
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+    println!(
+        "avx: {}, neon: {}, simd128: {}, f16c: {}",
+        candle::utils::with_avx(),
+        candle::utils::with_neon(),
+        candle::utils::with_simd128(),
+        candle::utils::with_f16c()
+    );
+    println!(
+        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
+        args.temperature.unwrap_or(0.),
+        args.repeat_penalty,
+        args.repeat_last_n
+    );
+
+    let start = std::time::Instant::now();
+    let api = Api::new()?;
+    let repo = api.repo(Repo::with_revision(
+        args.model_id
+            .unwrap_or_else(|| args.which.model_id().to_string()),
+        RepoType::Model,
+        args.revision
+            .unwrap_or_else(|| args.which.revision().to_string()),
+    ));
+    let tokenizer_filename = match args.tokenizer_file {
+        Some(file) => std::path::PathBuf::from(file),
+        None => api
+            .model("EleutherAI/gpt-neox-20b".to_string())
+            .get("tokenizer.json")?,
+    };
+    let config_filename = match args.config_file {
+        Some(file) => std::path::PathBuf::from(file),
+        None => repo.get("config.json")?,
+    };
+    let filenames = match args.weight_files {
+        Some(files) => files
+            .split(',')
+            .map(std::path::PathBuf::from)
+            .collect::<Vec<_>>(),
+        None => {
+            vec![repo.get("model.safetensors")?]
+        }
+    };
+    println!("retrieved the files in {:?}", start.elapsed());
+    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+
+    let start = std::time::Instant::now();
+    let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
+    let device = candle_examples::device(args.cpu)?;
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
+    let model = Model::new(&config, vb.pp("backbone"))?;
+    println!("loaded the model in {:?}", start.elapsed());
+
+    let mut pipeline = TextGeneration::new(
+        model,
+        config,
+        tokenizer,
+        args.seed,
+        args.temperature,
+        args.top_p,
+        args.repeat_penalty,
+        args.repeat_last_n,
+        &device,
+    );
+    pipeline.run(&args.prompt, args.sample_len)?;
+    Ok(())
+}
diff --git a/candle-transformers/src/models/mamba.rs b/candle-transformers/src/models/mamba.rs
new file mode 100644
index 00000000..da254bd1
--- /dev/null
+++ b/candle-transformers/src/models/mamba.rs
@@ -0,0 +1,211 @@
+#![allow(unused)]
+/// A fast implementation of mamba for inference only.
+/// This is based on: https://github.com/LaurentMazare/mamba.rs
+use crate::models::with_tracing::{linear, linear_no_bias, Linear};
+use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
+use candle_nn::{RmsNorm, VarBuilder};
+
+const D_CONV: usize = 4;
+const D_STATE: usize = 16;
+
+#[derive(Debug, Clone, serde::Deserialize)]
+pub struct Config {
+    d_model: usize,
+    n_layer: usize,
+    vocab_size: usize,
+    pad_vocab_size_multiple: usize,
+}
+
+impl Config {
+    fn vocab_size(&self) -> usize {
+        let pad = self.pad_vocab_size_multiple;
+        (self.vocab_size + pad - 1) / pad * pad
+    }
+
+    fn dt_rank(&self) -> usize {
+        (self.d_model + 15) / 16
+    }
+
+    fn d_inner(&self) -> usize {
+        self.d_model * 2
+    }
+}
+
+pub struct State {
+    hs: Vec<Tensor>,
+    prev_xs: Vec<[Tensor; D_CONV]>,
+    pos: usize,
+}
+
+impl State {
+    pub fn new(batch_size: usize, cfg: &Config, device: &Device) -> Result<Self> {
+        let mut hs = Vec::with_capacity(cfg.n_layer);
+        let mut prev_xs = Vec::with_capacity(cfg.n_layer);
+        for _i in 0..cfg.n_layer {
+            let h = Tensor::zeros((batch_size, cfg.d_inner(), D_STATE), DType::F32, device)?;
+            let x = Tensor::zeros((batch_size, cfg.d_inner()), DType::F32, device)?;
+            hs.push(h);
+            prev_xs.push([x.clone(), x.clone(), x.clone(), x.clone()]);
+        }
+        Ok(Self {
+            hs,
+            prev_xs,
+            pos: 0,
+        })
+    }
+}
+
+#[derive(Clone, Debug)]
+pub struct MambaBlock {
+    in_proj: Linear,
+    conv1d_bias: Tensor,
+    conv1d_weights: [Tensor; D_CONV],
+    x_proj: Linear,
+    dt_proj: Linear,
+    a_log: Tensor,
+    d: Tensor,
+    out_proj: Linear,
+    dt_rank: usize,
+    layer_index: usize,
+    d_inner: usize,
+}
+
+impl MambaBlock {
+    pub fn new(layer_index: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let d_inner = cfg.d_inner();
+        let dt_rank = cfg.dt_rank();
+        let in_proj = linear_no_bias(cfg.d_model, d_inner * 2, vb.pp("in_proj"))?;
+        let x_proj = linear_no_bias(d_inner, dt_rank + D_STATE * 2, vb.pp("x_proj"))?;
+        let dt_proj = linear(dt_rank, d_inner, vb.pp("dt_proj"))?;
+        let a_log = vb.get((d_inner, D_STATE), "A_log")?;
+        let d = vb.get(d_inner, "D")?;
+        let out_proj = linear_no_bias(d_inner, cfg.d_model, vb.pp("out_proj"))?;
+        let conv1d_bias = vb.get(d_inner, "conv1d.bias")?;
+        let conv1d_weight = vb.get((d_inner, 1, D_CONV), "conv1d.weight")?;
+        let conv1d_weights = [
+            conv1d_weight.i((.., 0, 0))?,
+            conv1d_weight.i((.., 0, 1))?,
+            conv1d_weight.i((.., 0, 2))?,
+            conv1d_weight.i((.., 0, 3))?,
+        ];
+        Ok(Self {
+            in_proj,
+            conv1d_bias,
+            conv1d_weights,
+            x_proj,
+            dt_proj,
+            a_log,
+            d,
+            out_proj,
+            dt_rank,
+            layer_index,
+            d_inner,
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
+        let (b_sz, _dim) = xs.dims2()?;
+        let li = self.layer_index;
+        let mut xs = xs.apply(&self.in_proj)?.chunk(2, D::Minus1)?;
+        let proj_for_silu = xs.remove(1);
+        state.prev_xs[li][state.pos % D_CONV] = xs.remove(0);
+        let mut proj_for_conv = self.conv1d_bias.broadcast_as((b_sz, self.d_inner))?;
+        for d_c in 0..D_CONV {
+            proj_for_conv = (proj_for_conv
+                + self.conv1d_weights[d_c]
+                    .broadcast_mul(&state.prev_xs[li][(d_c + 1 + state.pos) % D_CONV])?)?;
+        }
+        let proj_for_conv = candle_nn::ops::silu(&proj_for_conv)?;
+        // SSM + Selection, we're doing inference here so only need the last step of
+        // the sequence.
+        // Algorithm 3.2 on page 6, https://arxiv.org/pdf/2312.00752.pdf
+
+        let x_proj = self.x_proj.forward(&proj_for_conv)?;
+        let delta = x_proj.narrow(D::Minus1, 0, self.dt_rank)?;
+        let b = x_proj.narrow(D::Minus1, self.dt_rank, D_STATE)?;
+        let c = x_proj.narrow(D::Minus1, self.dt_rank + D_STATE, D_STATE)?;
+
+        let delta = delta.apply(&self.dt_proj)?;
+        // softplus
+        let delta = (delta.exp()? + 1.)?.log()?;
+        let a = self.a_log.to_dtype(candle::DType::F32)?.exp()?.neg()?;
+        let d = self.d.to_dtype(candle::DType::F32)?;
+
+        // Selective scan part
+        // Eqn (2a), page 3, h_t = Ab h_{t-1} + Bb x_t
+        let delta = delta
+            .unsqueeze(D::Minus1)?
+            .broadcast_as((b_sz, self.d_inner, D_STATE))?;
+        let a = a.broadcast_as((b_sz, self.d_inner, D_STATE))?;
+        let b = b.broadcast_as((b_sz, self.d_inner, D_STATE))?;
+        let proj_for_conv_b =
+            proj_for_conv
+                .unsqueeze(D::Minus1)?
+                .broadcast_as((b_sz, self.d_inner, D_STATE))?;
+        state.hs[li] = ((&state.hs[li] * (&delta * &a)?.exp()?)? + &delta * &b * &proj_for_conv_b)?;
+        let ss = (state.hs[li]
+            .matmul(&c.unsqueeze(D::Minus1)?)?
+            .squeeze(D::Minus1)?
+            + proj_for_conv.broadcast_mul(&d)?)?;
+
+        let ys = (ss * candle_nn::ops::silu(&proj_for_silu))?;
+        ys.apply(&self.out_proj)
+    }
+}
+
+#[derive(Clone, Debug)]
+pub struct ResidualBlock {
+    mixer: MambaBlock,
+    norm: RmsNorm,
+}
+
+impl ResidualBlock {
+    pub fn new(layer_index: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let norm = candle_nn::rms_norm(cfg.d_model, 1e-5, vb.pp("norm"))?;
+        let mixer = MambaBlock::new(layer_index, cfg, vb.pp("mixer"))?;
+        Ok(Self { mixer, norm })
+    }
+
+    fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
+        self.mixer.forward(&xs.apply(&self.norm)?, state)? + xs
+    }
+}
+
+// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L56
+#[derive(Clone, Debug)]
+pub struct Model {
+    embedding: candle_nn::Embedding,
+    layers: Vec<ResidualBlock>,
+    norm_f: RmsNorm,
+    lm_head: Linear,
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let embedding = candle_nn::embedding(cfg.vocab_size(), cfg.d_model, vb.pp("embedding"))?;
+        let mut layers = Vec::with_capacity(cfg.n_layer);
+        let vb_l = vb.pp("layers");
+        for layer_idx in 0..cfg.n_layer {
+            let layer = ResidualBlock::new(layer_idx, cfg, vb_l.pp(layer_idx))?;
+            layers.push(layer)
+        }
+        let norm_f = candle_nn::rms_norm(cfg.d_model, 1e-5, vb.pp("norm_f"))?;
+        let lm_head = Linear::from_weights(embedding.embeddings().clone(), None);
+        Ok(Self {
+            embedding,
+            layers,
+            norm_f,
+            lm_head,
+        })
+    }
+
+    pub fn forward(&self, input_ids: &Tensor, state: &mut State) -> Result<Tensor> {
+        let _b_size = input_ids.dims1()?;
+        let mut xs = self.embedding.forward(input_ids)?;
+        for layer in self.layers.iter() {
+            xs = layer.forward(&xs, state)?
+        }
+        state.pos += 1;
+        xs.apply(&self.norm_f)?.apply(&self.lm_head)
+    }
+}
diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index f3782fff..769fd650 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -13,6 +13,7 @@ pub mod jina_bert;
 pub mod llama;
 pub mod llama2_c;
 pub mod llama2_c_weights;
+pub mod mamba;
 pub mod marian;
 pub mod mistral;
 pub mod mixformer;