### Run Mistral Example in Release Mode

Source: https://github.com/oxiglade/mlx-rs/blob/main/examples/mistral/README.md

Execute the Mistral text generation example in release mode. This command assumes you have the mlx-rs project set up.

```bash
cargo run --release
```

--------------------------------

### Run Mistral Example with Custom Prompt and Seed

Source: https://github.com/oxiglade/mlx-rs/blob/main/examples/mistral/README.md

Run the Mistral text generation example with a custom prompt and seed. This command is executed in release mode.

```bash
cargo run --release -- --prompt "Hello, world!" --seed 1
```

--------------------------------

### Updating Arrays in MLX-RS Training Loop

Source: https://github.com/oxiglade/mlx-rs/blob/main/mlx-rs/README.md

This example shows how to update a weight array within a training loop by calculating the gradient and applying it to the corresponding input array. Ensure `eval()` is called after updates.

```rust
let mut inputs = vec![w, x, y];

for _ in 0..num_iterations {
    let grad = transforms::grad(loss_fn, argnums)(&inputs)?;
    inputs[0] = &inputs[0] - Array::from_f32(learning_rate) * grad;
    inputs[0].eval()?;
}
```

--------------------------------

### Correct Closure for Automatic Differentiation

Source: https://github.com/oxiglade/mlx-rs/blob/main/README.md

This Rust code shows the recommended approach for automatic differentiation by explicitly passing all required arrays as inputs to the closure, ensuring proper tracing of the compute graph.

```rust
let loss_fn = |inputs: &[Array]| -> Result<Array, Exception> {
    let w = &inputs[0];
    let x = &inputs[1];
    let y = &inputs[2];

    let y_pred = x.matmul(w)?;
    let loss = Array::from_f32(0.5) * ops::mean(&ops::square(y_pred - y)?, None, None)?;
    Ok(loss)
};
let argnums = &[0];

let mut inputs = vec![w, x, y];
let grad = transforms::grad(loss_fn, argnums)(&inputs)?;
```

--------------------------------

### Add MLX-RS Dependency to Cargo.toml

Source: https://github.com/oxiglade/mlx-rs/blob/main/mlx-rs/README.md

Include the mlx-rs crate in your Rust project by adding this entry to your Cargo.toml file.

```toml
[dependencies]
mlx-rs = "0.21.0"

```

--------------------------------

### Correct Automatic Differentiation Closure in Rust

Source: https://github.com/oxiglade/mlx-rs/blob/main/mlx-rs/README.md

This closure correctly passes all required arrays as explicit inputs, ensuring proper tracing for automatic differentiation in mlx-rs. The `argnums` parameter specifies which input array to differentiate with respect to.

```rust
let loss_fn = |inputs: &[Array]| -> Result<Array, Exception> {
    let w = &inputs[0];
    let x = &inputs[1];
    let y = &inputs[2];

    let y_pred = x.matmul(w)?;
    let loss = Array::from_f32(0.5) * ops::mean(&ops::square(y_pred - y)?, None, None)?;
    Ok(loss)
};
let argnums = &[0];

let mut inputs = vec![w, x, y];
grad = transforms::grad(loss_fn, argnums)(&inputs)?;
```

--------------------------------

### Incorrect Automatic Differentiation Closure in Rust

Source: https://github.com/oxiglade/mlx-rs/blob/main/mlx-rs/README.md

This closure attempts to capture arrays from the outer scope, which can lead to segfaults in mlx-rs. Avoid this pattern.

```rust
let x = random::normal::<f32>(&[num_examples, num_features], None, None, None)?;
let y = x.matmul(&w_star)? + eps;

let loss_fn = |w: &Array| -> Result<Array, Exception> {
    let y_pred = x.matmul(w)?;
    let loss = Array::from_f32(0.5) * ops::mean(&ops::square(&(y_pred - &y))?, None, None)?;
    Ok(loss)
};

grad_fn = transforms::grad(loss_fn, &[0]);
```

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