diff --git a/candle-examples/examples/quantized-phi/main.rs b/candle-examples/examples/quantized-phi/main.rs
new file mode 100644
index 000000000..301c2e06e
--- /dev/null
+++ b/candle-examples/examples/quantized-phi/main.rs
@@ -0,0 +1,273 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use clap::{Parser, ValueEnum};
+use std::io::Write;
+use tokenizers::Tokenizer;
+
+use candle::quantized::gguf_file;
+use candle::Tensor;
+use candle_transformers::generation::{LogitsProcessor, Sampling};
+
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_transformers::models::quantized_phi as model;
+use model::ModelWeights;
+
+const DEFAULT_PROMPT: &str = "Write a function to count prime numbers up to N. ";
+
+#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
+enum Which {
+    #[value(name = "phi-2")]
+    Phi2,
+}
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// GGUF file to load, typically a .gguf file generated by the quantize command from llama.cpp
+    #[arg(long)]
+    model: Option<String>,
+
+    /// The initial prompt, use 'interactive' for entering multiple prompts in an interactive way
+    /// and 'chat' for an interactive model where history of previous prompts and generated tokens
+    /// is preserved.
+    #[arg(long)]
+    prompt: Option<String>,
+
+    /// The length of the sample to generate (in tokens).
+    #[arg(short = 'n', long, default_value_t = 1000)]
+    sample_len: usize,
+
+    /// The tokenizer config in json format.
+    #[arg(long)]
+    tokenizer: Option<String>,
+
+    /// The temperature used to generate samples, use 0 for greedy sampling.
+    #[arg(long, default_value_t = 0.8)]
+    temperature: f64,
+
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
+    /// Only sample among the top K samples.
+    #[arg(long)]
+    top_k: Option<usize>,
+
+    /// The seed to use when generating random samples.
+    #[arg(long, default_value_t = 299792458)]
+    seed: u64,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+
+    /// Process prompt elements separately.
+    #[arg(long)]
+    split_prompt: bool,
+
+    /// Run on CPU rather than GPU even if a GPU is available.
+    #[arg(long)]
+    cpu: bool,
+
+    /// 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,
+
+    /// The model size to use.
+    #[arg(long, default_value = "phi-2")]
+    which: Which,
+}
+
+impl Args {
+    fn tokenizer(&self) -> anyhow::Result<Tokenizer> {
+        let tokenizer_path = match &self.tokenizer {
+            Some(config) => std::path::PathBuf::from(config),
+            None => {
+                let api = hf_hub::api::sync::Api::new()?;
+                let api = api.model("microsoft/phi-2".to_string());
+                api.get("tokenizer.json")?
+            }
+        };
+        Tokenizer::from_file(tokenizer_path).map_err(anyhow::Error::msg)
+    }
+
+    fn model(&self) -> anyhow::Result<std::path::PathBuf> {
+        let model_path = match &self.model {
+            Some(config) => std::path::PathBuf::from(config),
+            None => {
+                let (repo, filename) = match self.which {
+                    Which::Phi2 => ("TheBloke/phi-2-GGUF", "phi-2.Q4_K_M.gguf"),
+                };
+                let api = hf_hub::api::sync::Api::new()?;
+                let api = api.model(repo.to_string());
+                api.get(filename)?
+            }
+        };
+        Ok(model_path)
+    }
+}
+
+fn format_size(size_in_bytes: usize) -> String {
+    if size_in_bytes < 1_000 {
+        format!("{}B", size_in_bytes)
+    } else if size_in_bytes < 1_000_000 {
+        format!("{:.2}KB", size_in_bytes as f64 / 1e3)
+    } else if size_in_bytes < 1_000_000_000 {
+        format!("{:.2}MB", size_in_bytes as f64 / 1e6)
+    } else {
+        format!("{:.2}GB", size_in_bytes as f64 / 1e9)
+    }
+}
+
+fn main() -> anyhow::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, args.repeat_penalty, args.repeat_last_n
+    );
+
+    let model_path = args.model()?;
+    let mut file = std::fs::File::open(&model_path)?;
+    let start = std::time::Instant::now();
+    let device = candle_examples::device(args.cpu)?;
+
+    let mut model = {
+        let model = gguf_file::Content::read(&mut file).map_err(|e| e.with_path(model_path))?;
+        let mut total_size_in_bytes = 0;
+        for (_, tensor) in model.tensor_infos.iter() {
+            let elem_count = tensor.shape.elem_count();
+            total_size_in_bytes +=
+                elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.block_size();
+        }
+        println!(
+            "loaded {:?} tensors ({}) in {:.2}s",
+            model.tensor_infos.len(),
+            &format_size(total_size_in_bytes),
+            start.elapsed().as_secs_f32(),
+        );
+        ModelWeights::from_gguf(model, &mut file, &device)?
+    };
+    println!("model built");
+
+    let tokenizer = args.tokenizer()?;
+    let mut tos = TokenOutputStream::new(tokenizer);
+    let prompt_str = args.prompt.unwrap_or_else(|| DEFAULT_PROMPT.to_string());
+    print!("{}", &prompt_str);
+    let tokens = tos
+        .tokenizer()
+        .encode(prompt_str, true)
+        .map_err(anyhow::Error::msg)?;
+    let tokens = tokens.get_ids();
+    let to_sample = args.sample_len.saturating_sub(1);
+    let mut all_tokens = vec![];
+    let mut logits_processor = {
+        let temperature = args.temperature;
+        let sampling = if temperature <= 0. {
+            Sampling::ArgMax
+        } else {
+            match (args.top_k, args.top_p) {
+                (None, None) => Sampling::All { temperature },
+                (Some(k), None) => Sampling::TopK { k, temperature },
+                (None, Some(p)) => Sampling::TopP { p, temperature },
+                (Some(k), Some(p)) => Sampling::TopKThenTopP { k, p, temperature },
+            }
+        };
+        LogitsProcessor::from_sampling(args.seed, sampling)
+    };
+
+    let start_prompt_processing = std::time::Instant::now();
+    let mut next_token = if !args.split_prompt {
+        let input = Tensor::new(tokens, &device)?.unsqueeze(0)?;
+        let logits = model.forward(&input, 0)?;
+        let logits = logits.squeeze(0)?;
+        logits_processor.sample(&logits)?
+    } else {
+        let mut next_token = 0;
+        for (pos, token) in tokens.iter().enumerate() {
+            let input = Tensor::new(&[*token], &device)?.unsqueeze(0)?;
+            let logits = model.forward(&input, pos)?;
+            let logits = logits.squeeze(0)?;
+            next_token = logits_processor.sample(&logits)?
+        }
+        next_token
+    };
+    let prompt_dt = start_prompt_processing.elapsed();
+    all_tokens.push(next_token);
+    if let Some(t) = tos.next_token(next_token)? {
+        print!("{t}");
+        std::io::stdout().flush()?;
+    }
+    let eos_token = *tos
+        .tokenizer()
+        .get_vocab(true)
+        .get("<|endoftext|>")
+        .unwrap();
+    let start_post_prompt = std::time::Instant::now();
+    let mut sampled = 0;
+    for index in 0..to_sample {
+        let input = Tensor::new(&[next_token], &device)?.unsqueeze(0)?;
+        let logits = model.forward(&input, tokens.len() + index)?;
+        let logits = logits.squeeze(0)?;
+        let logits = if args.repeat_penalty == 1. {
+            logits
+        } else {
+            let start_at = all_tokens.len().saturating_sub(args.repeat_last_n);
+            candle_transformers::utils::apply_repeat_penalty(
+                &logits,
+                args.repeat_penalty,
+                &all_tokens[start_at..],
+            )?
+        };
+        next_token = logits_processor.sample(&logits)?;
+        all_tokens.push(next_token);
+        if let Some(t) = tos.next_token(next_token)? {
+            print!("{t}");
+            std::io::stdout().flush()?;
+        }
+        sampled += 1;
+        if next_token == eos_token {
+            break;
+        };
+    }
+    if let Some(rest) = tos.decode_rest().map_err(candle::Error::msg)? {
+        print!("{rest}");
+    }
+    std::io::stdout().flush()?;
+    let dt = start_post_prompt.elapsed();
+    println!(
+        "\n\n{:4} prompt tokens processed: {:.2} token/s",
+        tokens.len(),
+        tokens.len() as f64 / prompt_dt.as_secs_f64(),
+    );
+    println!(
+        "{sampled:4} tokens generated: {:.2} token/s",
+        sampled as f64 / dt.as_secs_f64(),
+    );
+    Ok(())
+}
diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index f4a719319..5f1a40ad0 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -37,6 +37,7 @@ pub mod quantized_mistral;
 pub mod quantized_mixformer;
 pub mod quantized_moondream;
 pub mod quantized_mpt;
+pub mod quantized_phi;
 pub mod quantized_recurrent_gemma;
 pub mod quantized_rwkv_v5;
 pub mod quantized_rwkv_v6;
diff --git a/candle-transformers/src/models/quantized_phi.rs b/candle-transformers/src/models/quantized_phi.rs
new file mode 100644
index 000000000..0ebf7f4d4
--- /dev/null
+++ b/candle-transformers/src/models/quantized_phi.rs
@@ -0,0 +1,288 @@
+use std::collections::HashMap;
+
+use candle::quantized::gguf_file;
+use candle::quantized::QTensor;
+use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
+use candle_nn::{Embedding, LayerNorm};
+
+pub const MAX_SEQ_LEN: usize = 4096;
+
+#[derive(Debug, Clone)]
+struct QLinear {
+    inner: candle::quantized::QMatMul,
+    bias: Tensor,
+    span: tracing::Span,
+}
+
+impl QLinear {
+    fn new<R: std::io::Read + std::io::Seek>(
+        ct: &gguf_file::Content,
+        r: &mut R,
+        name: &str,
+        device: &Device,
+    ) -> Result<Self> {
+        let span = tracing::span!(tracing::Level::TRACE, "qmatmul");
+        let w = ct.tensor(r, &format!("{name}.weight"), device)?;
+        let b = ct.tensor(r, &format!("{name}.bias"), device)?;
+        let inner = candle::quantized::QMatMul::from_qtensor(w)?;
+        let bias = b.dequantize(device)?;
+        Ok(Self { inner, bias, span })
+    }
+}
+
+impl Module for QLinear {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(xs)?.broadcast_add(&self.bias)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Mlp {
+    ffn_up: QLinear,
+    ffn_down: QLinear,
+}
+
+impl Module for Mlp {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.ffn_up)?.gelu()?.apply(&self.ffn_down)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct LayerWeights {
+    attn_qkv: QLinear,
+    attn_output: QLinear,
+    attn_norm: LayerNorm,
+    mlp: Mlp,
+    n_head: usize,
+    n_kv_head: usize,
+    head_dim: usize,
+    cos: Tensor,
+    sin: Tensor,
+    rope_dim: usize,
+    neg_inf: Tensor,
+    kv_cache: Option<(Tensor, Tensor)>,
+    span_attn: tracing::Span,
+    span_rot: tracing::Span,
+}
+
+fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: &Tensor) -> Result<Tensor> {
+    let shape = mask.shape();
+    let m = mask.where_cond(&on_true.broadcast_as(shape.dims())?, on_false)?;
+    Ok(m)
+}
+
+impl LayerWeights {
+    fn apply_rotary_emb(&self, xs: &Tensor, index_pos: usize) -> Result<Tensor> {
+        let _enter = self.span_rot.enter();
+        let (_b_sz, _n_head, seq_len, _n_embd) = xs.dims4()?;
+        let xs_rot = xs.i((.., .., .., ..self.rope_dim))?;
+        let xs_pass = xs.i((.., .., .., self.rope_dim..))?;
+        let cos = self.cos.narrow(0, index_pos, seq_len)?;
+        let sin = self.sin.narrow(0, index_pos, seq_len)?;
+        let xs_rot = candle_nn::rotary_emb::rope(&xs_rot.contiguous()?, &cos, &sin)?;
+        Tensor::cat(&[&xs_rot, &xs_pass], D::Minus1)
+    }
+
+    fn forward_attn(
+        &mut self,
+        x: &Tensor,
+        mask: Option<&Tensor>,
+        index_pos: usize,
+    ) -> Result<Tensor> {
+        let _enter = self.span_attn.enter();
+        let (b_sz, seq_len, n_embd) = x.dims3()?;
+        let qkv =
+            self.attn_qkv
+                .forward(x)?
+                .reshape((b_sz, seq_len, 3, self.n_head, self.head_dim))?;
+
+        let q = qkv.i((.., .., 0))?.transpose(1, 2)?;
+        let k = qkv.i((.., .., 1))?.transpose(1, 2)?;
+        let v = qkv.i((.., .., 2))?.transpose(1, 2)?;
+        // This call to contiguous ensures that the fast kernel can be called below. It's
+        // actually a no-op except when processing the initial prompt so has no significant
+        // impact on performance.
+        let v = v.contiguous()?;
+
+        let q = self.apply_rotary_emb(&q, index_pos)?.contiguous()?;
+        let k = self.apply_rotary_emb(&k, index_pos)?;
+
+        let (k, v) = match &self.kv_cache {
+            None => (k.contiguous()?, v.contiguous()?),
+            Some((k_cache, v_cache)) => {
+                if index_pos == 0 {
+                    (k.contiguous()?, v.contiguous()?)
+                } else {
+                    let k = Tensor::cat(&[k_cache, &k], 2)?;
+                    let v = Tensor::cat(&[v_cache, &v], 2)?;
+                    (k.contiguous()?, v.contiguous()?)
+                }
+            }
+        };
+        self.kv_cache = Some((k.clone(), v.clone()));
+
+        let k = crate::utils::repeat_kv(k, self.n_head / self.n_kv_head)?;
+        let v = crate::utils::repeat_kv(v, self.n_head / self.n_kv_head)?;
+
+        let att = (q.matmul(&k.t()?)? / (self.head_dim as f64).sqrt())?;
+        let att = match mask {
+            None => att,
+            Some(mask) => {
+                let mask = mask.broadcast_as(att.shape())?;
+                masked_fill(&att, &mask, &self.neg_inf)?
+            }
+        };
+        let att = candle_nn::ops::softmax_last_dim(&att)?;
+        // Convert to contiguous as matmul doesn't support strided vs for now.
+        let y = att.matmul(&v.contiguous()?)?;
+        let y = y.transpose(1, 2)?.reshape(&[b_sz, seq_len, n_embd])?;
+        let y = self.attn_output.forward(&y)?;
+        Ok(y)
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct ModelWeights {
+    tok_embeddings: Embedding,
+    layers: Vec<LayerWeights>,
+    output_norm: LayerNorm,
+    output: QLinear,
+    masks: HashMap<usize, Tensor>,
+    span: tracing::Span,
+    span_output: tracing::Span,
+}
+
+fn precomput_freqs_cis(
+    head_dim: usize,
+    freq_base: f32,
+    device: &Device,
+) -> Result<(Tensor, Tensor)> {
+    let theta: Vec<_> = (0..head_dim)
+        .step_by(2)
+        .map(|i| 1f32 / freq_base.powf(i as f32 / head_dim as f32))
+        .collect();
+    let theta = Tensor::new(theta.as_slice(), device)?;
+    let idx_theta = Tensor::arange(0, MAX_SEQ_LEN as u32, device)?
+        .to_dtype(DType::F32)?
+        .reshape((MAX_SEQ_LEN, 1))?
+        .matmul(&theta.reshape((1, theta.elem_count()))?)?;
+    let cos = idx_theta.cos()?;
+    let sin = idx_theta.sin()?;
+    Ok((cos, sin))
+}
+
+fn layer_norm(w: QTensor, b: QTensor, eps: f64) -> Result<LayerNorm> {
+    let w = w.dequantize(&w.device())?;
+    let b = b.dequantize(&b.device())?;
+    let ln = LayerNorm::new(w, b, eps);
+    Ok(ln)
+}
+
+impl ModelWeights {
+    pub fn from_gguf<R: std::io::Seek + std::io::Read>(
+        ct: gguf_file::Content,
+        reader: &mut R,
+        device: &Device,
+    ) -> Result<Self> {
+        let md_get = |s: &str| match ct.metadata.get(s) {
+            None => candle::bail!("cannot find {s} in metadata"),
+            Some(v) => Ok(v),
+        };
+
+        // Parameter extraction from metadata.
+        let head_count = md_get("phi2.attention.head_count")?.to_u32()? as usize;
+        let head_count_kv = md_get("phi2.attention.head_count_kv")?.to_u32()? as usize;
+        let block_count = md_get("phi2.block_count")?.to_u32()? as usize;
+        let embedding_length = md_get("phi2.embedding_length")?.to_u32()? as usize;
+        let rope_dim = md_get("phi2.rope.dimension_count")?.to_u32()? as usize;
+        let ln_eps = md_get("phi2.attention.layer_norm_epsilon")?.to_f32()? as f64;
+        let (cos, sin) = precomput_freqs_cis(rope_dim, 10_000., device)?;
+        let neg_inf = Tensor::new(f32::NEG_INFINITY, device)?;
+
+        let tok_embeddings = ct.tensor(reader, "token_embd.weight", device)?;
+        let tok_embeddings = tok_embeddings.dequantize(device)?;
+        let output_norm = layer_norm(
+            ct.tensor(reader, "output_norm.weight", device)?,
+            ct.tensor(reader, "output_norm.bias", device)?,
+            ln_eps,
+        )?;
+        let output = QLinear::new(&ct, reader, "output", device)?;
+        let mut layers = Vec::with_capacity(block_count);
+        for layer_idx in 0..block_count {
+            let prefix = format!("blk.{layer_idx}");
+            let ffn_up = QLinear::new(&ct, reader, &format!("{prefix}.ffn_up"), device)?;
+            let ffn_down = QLinear::new(&ct, reader, &format!("{prefix}.ffn_down"), device)?;
+            let mlp = Mlp { ffn_up, ffn_down };
+            let attn_norm = layer_norm(
+                ct.tensor(reader, &format!("{prefix}.attn_norm.weight"), device)?,
+                ct.tensor(reader, &format!("{prefix}.attn_norm.bias"), device)?,
+                ln_eps,
+            )?;
+            let span_attn = tracing::span!(tracing::Level::TRACE, "attn");
+            let span_rot = tracing::span!(tracing::Level::TRACE, "attn-rot");
+            layers.push(LayerWeights {
+                attn_qkv: QLinear::new(&ct, reader, &format!("{prefix}.attn_qkv"), device)?,
+                attn_output: QLinear::new(&ct, reader, &format!("{prefix}.attn_output"), device)?,
+                attn_norm,
+                mlp,
+                n_head: head_count,
+                n_kv_head: head_count_kv,
+                head_dim: embedding_length / head_count,
+                cos: cos.clone(),
+                sin: sin.clone(),
+                rope_dim,
+                neg_inf: neg_inf.clone(),
+                kv_cache: None,
+                span_attn,
+                span_rot,
+            })
+        }
+        let span = tracing::span!(tracing::Level::TRACE, "model");
+        let span_output = tracing::span!(tracing::Level::TRACE, "output");
+        Ok(Self {
+            tok_embeddings: Embedding::new(tok_embeddings, embedding_length),
+            layers,
+            output_norm,
+            output,
+            masks: HashMap::new(),
+            span,
+            span_output,
+        })
+    }
+
+    fn mask(&mut self, t: usize, device: &Device) -> Result<Tensor> {
+        if let Some(mask) = self.masks.get(&t) {
+            Ok(mask.clone())
+        } else {
+            let mask: Vec<_> = (0..t)
+                .flat_map(|i| (0..t).map(move |j| u8::from(j > i)))
+                .collect();
+            let mask = Tensor::from_slice(&mask, (t, t), device)?;
+            self.masks.insert(t, mask.clone());
+            Ok(mask)
+        }
+    }
+
+    pub fn forward(&mut self, xs: &Tensor, index_pos: usize) -> Result<Tensor> {
+        let (_b_sz, seq_len) = xs.dims2()?;
+        let mask = if seq_len == 1 {
+            None
+        } else {
+            Some(self.mask(seq_len, xs.device())?)
+        };
+        let _enter = self.span.enter();
+        let mut xs = self.tok_embeddings.forward(xs)?;
+        for layer in self.layers.iter_mut() {
+            let residual = &xs;
+            let xs_norm = xs.apply(&layer.attn_norm)?;
+            let attn_outputs = layer.forward_attn(&xs_norm, mask.as_ref(), index_pos)?;
+            let feed_forward_hidden_states = layer.mlp.forward(&xs_norm)?;
+            xs = (attn_outputs + feed_forward_hidden_states + residual)?
+        }
+        let xs = xs.apply(&self.output_norm)?.i((.., seq_len - 1, ..))?;
+        let _enter = self.span_output.enter();
+        self.output.forward(&xs)
+    }
+}