diff --git a/backends/llamacpp/csrc/ffi.hpp b/backends/llamacpp/csrc/ffi.hpp
index 36455263..99679fdb 100644
--- a/backends/llamacpp/csrc/ffi.hpp
+++ b/backends/llamacpp/csrc/ffi.hpp
@@ -43,6 +43,15 @@ namespace huggingface::tgi::backends::llamacpp {
         return std::shared_ptr<llama_model>(model, llama_model_deleter);
     };
 
+    auto get_llama_context_params = [](size_t num_threads) {
+        auto params = llama_context_default_params();
+        params.n_threads = num_threads;
+        params.n_threads_batch = num_threads;
+        params.flash_attn = true;
+        params.no_perf = false;
+        return params;
+    };
+
     /**
      * llama.cpp backend specific exception mapped from `backend_exception_t` to throw at the FFI level and
      * allow automatic implementation of Result<_, Exception> from C++ to Rust
@@ -64,7 +73,7 @@ namespace huggingface::tgi::backends::llamacpp {
          * @param num_threads The number of threads the worker is allowed to spawn accross for its threadpool
          */
         explicit llama_cpp_worker_frontend_t(llama_model *model, int32_t num_threads):
-            model_{ make_shared_llama_model(model) }, worker_(model_, {.n_ubatch = 1, .n_threads = num_threads, .no_perf = true}) {}
+            model_{ make_shared_llama_model(model) }, worker_(model_, get_llama_context_params(num_threads)) {}
 
         /**
          * Generate a new set of tokens from the provided `input_tokens`, streaming each individual token generated
diff --git a/backends/llamacpp/offline/main.cpp b/backends/llamacpp/offline/main.cpp
index e5c70e77..fad97b3a 100644
--- a/backends/llamacpp/offline/main.cpp
+++ b/backends/llamacpp/offline/main.cpp
@@ -27,24 +27,37 @@ int main(int argc, char **argv) {
             llama_model_deleter
     );
 
-    auto prompt = "My name is Morgan";
-    auto tokens = std::vector<llama_token>(16);
-    const auto nb_tokens = llama_tokenize(model.get(), prompt, sizeof(prompt), tokens.data(), tokens.size(), true,
+    auto prompt = std::string("My name is Morgan");
+    auto tokens = std::vector<llama_token>(128);
+    const auto nb_tokens = llama_tokenize(model.get(), prompt.c_str(), prompt.size(), tokens.data(), tokens.size(),
+                                          true,
                                           false);
     tokens.resize(nb_tokens);
-    auto backend = worker_t(std::move(model), {.n_batch = 1, .n_threads = 4});
+    llama_numa_init(ggml_numa_strategy::GGML_NUMA_STRATEGY_DISTRIBUTE);
+    auto backend = worker_t(model, llama_context_default_params());
 
     fmt::println("Tokenized: {}", tokens);
 
     // generate
     auto generated_tokens = std::vector<llama_token>(32);
     const auto n_generated_tokens = backend.generate(
-            {{.max_new_tokens = 32}, {.top_k = 40}, tokens},
+            {{.max_new_tokens = 32}, {.top_k = 40, .top_p = 0.95, .temperature = 0.8},
+             tokens},
             [&generated_tokens](llama_token new_token_id, float_t logit, bool is_eos, size_t step) -> bool {
                 generated_tokens.emplace(generated_tokens.begin() + (step - 1), new_token_id);
                 return false;
             }
     );
     generated_tokens.resize(n_generated_tokens.value());
-    fmt::println("Generated {} tokens", generated_tokens);
+
+    std::string decoded = std::string(256, 'a');
+    const size_t length = llama_detokenize(model.get(),
+                                           generated_tokens.data(),
+                                           generated_tokens.size(),
+                                           decoded.data(),
+                                           decoded.size(),
+                                           false, false);
+    decoded.resize(std::min(length, decoded.size()));
+    fmt::println("Generated tokens: {}", generated_tokens);
+    fmt::println("Generated text:   {}", decoded);
 }