diff --git a/candle-core/src/quantized/k_quants.rs b/candle-core/src/quantized/k_quants.rs
index e6db08d6..36efe2f2 100644
--- a/candle-core/src/quantized/k_quants.rs
+++ b/candle-core/src/quantized/k_quants.rs
@@ -421,8 +421,24 @@ impl GgmlType for BlockQ8_0 {
         Ok(())
     }
 
-    fn vec_dot(_: usize, _: &[Self], _: &[Self::VecDotType]) -> Result<f32> {
-        todo!()
+    fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
+        let qk = QK8_0;
+        if n % QK8_0 != 0 {
+            crate::bail!("vec_dot_q8_0_q8_0: {n} is not divisible by {qk}")
+        }
+
+        // Generic implementation.
+        let mut sumf = 0f32;
+        for (xs, ys) in xs.iter().zip(ys.iter()) {
+            let sum_i = xs
+                .qs
+                .iter()
+                .zip(ys.qs.iter())
+                .map(|(&x, &y)| x as i32 * y as i32)
+                .sum::<i32>();
+            sumf += sum_i as f32 * f16::to_f32(xs.d) * f16::to_f32(ys.d)
+        }
+        Ok(sumf)
     }
 }
 
diff --git a/candle-examples/examples/quantized/main.rs b/candle-examples/examples/quantized/main.rs
index f91f3194..209a0f55 100644
--- a/candle-examples/examples/quantized/main.rs
+++ b/candle-examples/examples/quantized/main.rs
@@ -195,10 +195,10 @@ impl WeightMap {
     }
 }
 
-fn precomput_freqs_cis(head_dim: usize) -> Result<(Tensor, Tensor)> {
+fn precomput_freqs_cis(head_dim: usize, freq_base: f32) -> Result<(Tensor, Tensor)> {
     let theta: Vec<_> = (0..head_dim)
         .step_by(2)
-        .map(|i| 1f32 / 10000f32.powf(i as f32 / head_dim as f32))
+        .map(|i| 1f32 / freq_base.powf(i as f32 / head_dim as f32))
         .collect();
     let theta = Tensor::new(theta.as_slice(), &Device::Cpu)?;
     let idx_theta = Tensor::arange(0, MAX_SEQ_LEN as u32, &Device::Cpu)?
@@ -214,7 +214,7 @@ impl ModelWeights {
     fn from_ggml(mut ct: ggml_file::Content, gqa: usize) -> Result<Self> {
         let cpu = &Device::Cpu;
         let head_dim = (ct.hparams.n_embd / ct.hparams.n_head) as usize;
-        let (cos, sin) = precomput_freqs_cis(head_dim)?;
+        let (cos, sin) = precomput_freqs_cis(head_dim, 10000.)?;
         let tok_embeddings = ct.remove("tok_embeddings.weight")?;
         let tok_embeddings = tok_embeddings.dequantize(cpu)?;
         let norm = RmsNorm::new(ct.remove("norm.weight")?, 1e-5)?;
@@ -287,7 +287,10 @@ impl ModelWeights {
         // Strangely this value is generally 1e-6 in GGUF file but used to be 1e-5 by default.
         let rms_norm_eps = md_get("llama.attention.layer_norm_rms_epsilon")?.to_f32()?;
 
-        let (cos, sin) = precomput_freqs_cis(rope_dim)?;
+        let rope_freq_base = md_get("llama.rope.freq_base")
+            .and_then(|m| m.to_f32())
+            .unwrap_or(10000f32);
+        let (cos, sin) = precomput_freqs_cis(rope_dim, rope_freq_base)?;
 
         let tok_embeddings = ct.tensor(reader, "token_embd.weight")?;
         let tok_embeddings = tok_embeddings.dequantize(cpu)?;
@@ -399,6 +402,12 @@ enum Which {
     L13bChat,
     #[value(name = "70b-chat")]
     L70bChat,
+    #[value(name = "7b-code")]
+    L7bCode,
+    #[value(name = "13b-code")]
+    L13bCode,
+    #[value(name = "32b-code")]
+    L34bCode,
 }
 
 #[derive(Parser, Debug)]
@@ -486,6 +495,9 @@ impl Args {
                         "TheBloke/Llama-2-70B-Chat-GGML",
                         "llama-2-70b-chat.ggmlv3.q4_0.bin",
                     ),
+                    Which::L7bCode => ("TheBloke/CodeLlama-7B-GGUF", "codellama-7b.Q8_0.gguf"),
+                    Which::L13bCode => ("TheBloke/CodeLlama-13B-GGUF", "codellama-13b.Q8_0.gguf"),
+                    Which::L34bCode => ("TheBloke/CodeLlama-34B-GGUF", "codellama-34b.Q8_0.gguf"),
                 };
                 let api = hf_hub::api::sync::Api::new()?;
                 let api = api.model(repo.to_string());
@@ -607,7 +619,13 @@ fn main() -> anyhow::Result<()> {
             );
             println!("params: {:?}", model.hparams);
             let default_gqa = match args.which {
-                Which::L7b | Which::L13b | Which::L7bChat | Which::L13bChat => 1,
+                Which::L7b
+                | Which::L13b
+                | Which::L7bChat
+                | Which::L13bChat
+                | Which::L7bCode
+                | Which::L13bCode
+                | Which::L34bCode => 1,
                 Which::L70b | Which::L70bChat => 8,
             };
             ModelWeights::from_ggml(model, args.gqa.unwrap_or(default_gqa))?