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README.md

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----
-license: mit
----
+---
+license: mit
+library_name: transformers
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+pipeline_tag: text-generation
+tags:
+- llama
+- conversational
+---
+# DeepSeek-R1-Distill-Llama-8B-Stateful-CoreML
+
+This repository contains a CoreML conversion of the DeepSeek-R1-Distill-Llama-8B model optimized for Apple Silicon devices. This conversion features stateful key-value caching for efficient text generation.
+
+## Model Description
+
+[DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) is a distilled 8 billion parameter language model from the DeepSeek-AI team. The model is built on the Llama architecture and has been distilled to maintain performance while reducing the parameter count.
+
+This CoreML conversion provides:
+- Full compatibility with Apple Silicon devices (M1, M2, M3 series)
+- Stateful inference with KV-caching for efficient text generation
+- Optimized performance for on-device deployment
+
+## Technical Specifications
+
+- **Base Model**: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+- **Parameters**: 8 billion
+- **Context Length**: Configurable (default: 64, expandable based on memory constraints)
+- **Quantization**: FP16 
+- **File Format**: .mlpackage
+- **Deployment Target**: macOS 15+
+- **Architecture**: Stateful LLM with key-value caching
+- **Input Features**: Flexible input size with dynamic shape handling
+
+## Key Features
+
+- **Stateful Inference**: The model implements a custom SliceUpdateKeyValueCache to maintain conversation state between inference calls, significantly improving generation speed.
+- **Dynamic Input Shapes**: Supports variable input lengths through RangeDim specification.
+- **Optimized Memory Usage**: Efficiently manages the key-value cache to minimize memory footprint.
+
+## Implementation Details
+
+This conversion utilizes:
+- A custom KvCacheStateLlamaForCausalLM wrapper around the Hugging Face Transformers implementation
+- CoreML's state management capabilities for maintaining KV caches between inference calls
+- Proper buffer registration to ensure state persistence
+- Dynamic tensor shapes to accommodate various input and context lengths
+
+## Usage
+
+The model can be loaded and used with CoreML in your Swift or Python projects:
+
+```python
+import coremltools as ct
+
+# Load the model
+model = ct.models.MLModel("DeepSeek-R1-Distill-Llama-8B.mlpackage")
+
+# Prepare inputs for inference
+# ...
+
+# Run inference
+output = model.predict({
+    "inputIds": input_ids,
+    "causalMask": causal_mask
+})
+```
+
+## Conversion Process
+
+The model was converted using CoreML Tools with the following steps:
+1. Loading the original model from Hugging Face
+2. Wrapping it with custom state management
+3. Tracing with PyTorch's JIT
+4. Converting to CoreML format with state specifications
+5. Saving in the .mlpackage format
+
+## Requirements
+
+To use this model:
+- Apple Silicon Mac (M1/M2/M3 series)
+- macOS 15 or later
+- Minimum 16GB RAM recommended
+
+## Limitations
+
+- The model requires significant memory for inference, especially with longer contexts
+- Performance is highly dependent on the device's Neural Engine capabilities
+- The default configuration supports a context length of 64 tokens, but this can be adjusted
+
+## License
+
+This model conversion inherits the license of the original DeepSeek-R1-Distill-Llama-8B model.
+
+## Acknowledgments
+
+- [DeepSeek-AI](https://github.com/deepseek-ai) for creating and releasing the original model
+- [Hugging Face](https://huggingface.co/) for hosting the model and providing the Transformers library
+- Apple for developing the CoreML framework
+
+## Citation
+
+If you use this model in your research, please cite both the original DeepSeek model and this conversion.