---
language:
- en
library_name: coreml
pipeline_tag: text-generation
tags:
- lfm
- liquid
- coreml
- apple-neural-engine
- ane
- on-device
---

# LFM 2.5 1.2B Instruct - Core ML (ANE)

This is an experimental Core ML export of [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct), specifically optimized and structured for the Apple Neural Engine (ANE) using Core ML 7's Stateful API.

## Model Details
- **Architecture**: Liquid Foundation Model (LFM) - LIV Convolution + Full Attention Hybrid
- **Size**: 1.2B Parameters
- **Quantization**: 4-bit Linear Symmetric (INT4 weights)
- **Target Runtime**: Core ML / Apple Neural Engine (iOS 18+ / macOS 15+)
- **Cache Handling**: Native `MLState` (Stateful Core ML) with fixed sequence length bounds.

## Integration & Export Details
This model has been adapted from its original PyTorch format because the native `LIV Convolution` state management dynamically concats cache tensors over time, an operation that is incompatible with the ANE's static memory requirements.

To solve this, the export pipeline applied the following transformations:
1. **Static Buffer Allocation**: The rolling `conv_cache` and standard attention `key_value` caches are allocated to fixed bounds (e.g. `MAX_SEQ_LEN = 512`) at initialization.
2. **In-Place Updates**: Dynamic slice concatenation was monkey-patched to use in-place slice assignment (`tensor[:] = ...` and `tensor[:, :, cache_position, :] = ...`).
3. **Core ML 7 State Mapping**: These buffers are registered as `ct.StateType` inputs/outputs during `coremltools` conversion so the Swift runtime can handle them efficiently as `MLState` opaque handles.
4. **INT4 Quantization**: The linear layers have been quantized to 4-bit to fit within strict iOS Jetsam limits on 8GB devices.

## Usage in Swift
This model must be invoked using `MLState` instead of passing the caches explicitly:

```swift
import CoreML

let config = MLModelConfiguration()
config.computeUnits = .cpuAndGPU // or .all, though ANE compile success may vary by iOS patch
let model = try await LFM2_5_1_2B_Stateful(configuration: config)

let state = model.makeState()

// Token generation loop
let input = LFM2_5_1_2B_StatefulInput(
    input_ids: currentTokenArray,
    cache_position: cachePositionArray,
    attention_mask: attentionMaskArray
)

let output = try await model.prediction(input: input, using: state)
```

## Intended Use
This repository was compiled for use inside [iMLX](https://github.com/alan13367/iMLX) (an experimental local inference chat app for iOS). It includes the original Hugging Face `tokenizer.json` and a specific `model_config.json` designed for the app's `ModelDownloadService`.