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 # Transforms
 
-🚧 Coming Soon 🚧
+Transforms allow you to post-process model outputs after ONNX inference and before returning results. They run inside the model binary, operating directly on tensors for high performance.
+
+Transforms run on Lua 5.4 in a sandboxed environment. As of right now, the transforms feature does not support LuaJIT.
+
+## Why Use Transforms?
+
+Common use cases:
+- **Normalize embeddings** for cosine similarity
+- **Apply softmax** to convert logits to probabilities
+- **Pool embeddings** to create sentence representations
+- **Scale outputs** for specific downstream tasks
+
+## Getting Started
+
+A transform is a Lua script that defines a `Postprocess` function:
+
+```lua
+---@param arr Tensor
+---@return Tensor
+function Postprocess(arr, ...)
+    -- your postprocessing logic
+    return tensor
+end
+```
+
+With a handful of exceptions, the `Postprocess` function must return a `Tensor` with the exact same shape as the input `Tensor` provided for that model type. The exceptions are as follows:
+
+- Embedding and sentence embedding models can modify the length of `hidden` (useful for matryoshka embeddings)
+- Sentence embeddings are given a `Tensor` of shape `[batch_size, seq_len, hidden]` and attention mask of `[batch_size, seq_len]`, and must return a `Tensor` of shape `[batch_size, hidden]`. In other words, it expects a pooling operation along dimension `seq_len`.
+
+!!! note "Note on indexing"
+    Lua is 1-indexed, meaning that it starts counting at 1 instead of 0. The `Tensor` API reflects this, meaning that you must count your axes and indices starting at 1 instead of 0.
+
+We provide a built-in API for standard tensor operations. To learn more, check out our [Tensor API reference page](reference). You can find the stub file [here](https://github.com/mozilla-ai/encoderfile/blob/main/encoderfile-core/stubs/lua/tensor.lua).
+
+If you don't see an op that you need, please don't hesitate to [create an issue](https://github.com/mozilla-ai/encoderfile/issues) on Github.
+
+## Input Signatures
+
+The input signature of `Postprocess` depends on the type of model being used.
+
+### Embedding
+
+```lua
+--- input: 3d tensor of shape [batch_size, seq_len, hidden]
+---@param arr Tensor
+---output: 3d tensor of shape [batch_size, seq_len, hidden]
+---@return Tensor
+function Postprocess(arr)
+    -- your postprocessing logic
+    return tensor
+end
+```
+
+### Sequence Classification
+
+```lua
+--- input: 2d tensor of shape [batch_size, n_labels]
+---@param arr Tensor
+---output: 2d tensor of shape [batch_size, n_labels]
+---@return Tensor
+function Postprocess(arr)
+    -- your postprocessing logic
+    return tensor
+end
+```
+
+### Token Classification
+
+```lua
+--- input: 3d tensor of shape [batch_size, seq_len, n_labels]
+---@param arr Tensor
+---output: 3d tensor of shape [batch_size, seq_len, n_labels]
+---@return Tensor
+function Postprocess(arr)
+    -- your postprocessing logic
+    return tensor
+end
+```
+
+### Sentence Embedding
+
+
+!!! note "Mean Pooling"
+    To mean-pool embeddings, you can use the `Tensor:mean_pool` function like this: `tensor:mean_pool(mask)`.
+
+```lua
+--- input: 3d tensor of shape [batch_size, seq_len, hidden]
+---@param arr Tensor
+-- input: 2d tensor of shape [batch_size, seq_len]
+-- This is automatically provided to the function and is equivalent to 🤗 transformer's attention_mask.
+---@param mask Tensor
+---output: 2d tensor of shape [batch_size, hidden]
+---@return Tensor
+function Postprocess(arr, mask)
+    -- your postprocessing logic
+    return tensor
+end
+```
+
+## Typical Transform Patterns
+
+Most transforms fall into one of 3 patterns:
+
+### 1. Elementwise Transforms
+
+Safe: they preserve shape automatically.
+
+Examples:
+
+- scaling (`tensor * 1.5`)
+- activation functions (`tensor:exp()`)
+
+### 2. Normalization Across Axis
+
+These also preserve shape.
+
+Examples:
+
+- Lp normalization: (`tensor:lp_normalize(p, axis)`)
+- subtracting mean per batch or per token
+- applying softmax across a specific dimension (`tensor:softmax(2)`)
+
+### 3. Mask-aware adjustments
+
+When working with sentence embedding models:
+
+```lua
+function Postprocess(arr, mask)
+    -- embeddings: [batch, seq, hidden]
+    -- mask: [batch, seq]
+
+    -- operations here must output [batch, hidden]
+    return ...
+end
+```
+
+## Best Practices
+
+!!! warning "Performance Implications"
+    Transforms run synchronously during inference, so expensive Lua-side loops will increase latency. If you don't see an op that you need, please don't hesitate to [create an issue](https://github.com/mozilla-ai/encoderfile/issues) on Github.
+
+A typical transform follows this structure:
+
+```lua
+function Postprocess(arr, ...)
+    -- Step 1: apply elementwise or axis-based operations
+    local modified = arr:exp()  -- example
+
+    -- Step 2: ensure the output shape matches the input shape
+    -- (all built-in ops described in the Tensor API preserve shape)
+
+    return modified
+end
+```
+
+## Debugging Transforms
+
+You can inspect shape and values using:
+
+```lua
+print("ndim:", t:ndim())
+print("len:", #t)
+print(tostring(t))
+```
+
+Errors typically fall into:
+
+- axis out of range
+    → axis must be 1-indexed and ≤ tensor rank
+
+- broadcasting errors
+    → the two shapes are incompatible
+
+- returned value is not a tensor
+    → must return a Tensor userdata object
+
+- shape mismatch
+    → you modified rank or dimensions
+
+## Configuration
+
+Transforms are embedded at build time. You can specify them in your config.yml either as a file path or inline.
+
+```yml
+transform:
+    path: path/to/your/transform/here
+```
+
+Or, they can be passed inline:
+```yml
+transform: |
+    function Postprocess(arr)
+        ...
+    return arr
+```
+