Update README.md

README.md

@@ -51,6 +51,14 @@ Alternatively, try the API model on the [Playground](https://playground.liquid.a
 
 ## 📄 Model details
 
+| Model | Parameters | Description |
+|-------|------------|-------------|
+| [LFM2.5-1.2B-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) | 1.2B | Pre-trained base model for fine-tuning |
+| [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) | 1.2B | General-purpose instruction-tuned model |
+| [LFM2.5-1.2B-JP](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP) | 1.2B | Japanese-optimized chat model |
+| [**LFM2.5-VL-1.6B**](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B) | 1.6B | Vision-language model with fast inference |
+| [LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) | 1.5B | Audio-language model for speech and text I/O |
+
 LFM2.5-VL-1.6B is a general-purpose vision-language model with the following features:
 
 - **LM Backbone**: LFM2.5-1.2B-Base
@@ -65,6 +73,13 @@ LFM2.5-VL-1.6B is a general-purpose vision-language model with the following fea
   - text: `temperature=0.1`, `min_p=0.15`, `repetition_penalty=1.05`
   - vision: `min_image_tokens=64` `max_image_tokens=256`, `do_image_splitting=True`
 
+| Model | Description |
+|-------|-------------|
+| [**LFM2.5-VL-1.6B**](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. |
+| [LFM2.5-VL-1.6B-GGUF](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. |
+| [LFM2.5-VL-1.6B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-ONNX) | ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). |
+| [LFM2.5-VL-1.6B-MLX](https://huggingface.co/mlx-community/LFM2.5-VL-1.6B-8bit) | MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework. |
+
 We recommend using it for general vision-language workloads, OCR or document comprehension. It’s not well-suited for knowledge-intensive tasks.
 
 ### Chat Template
@@ -174,7 +189,7 @@ We recommend fine-tuning LFM2.5-VL-1.6B model on your use cases to maximize perf
 
 | Notebook  | Description                                                          | Link |
 |-----------|----------------------------------------------------------------------|------|
-| SFT (TRL) | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL. | <a href="https://colab.research.google.com/drive/10530_jt_Joa5zH2wgYlyXosypq1R7PIz?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT (TRL) | Supervised Fine-Tuning with LoRA using TRL. | <a href="https://colab.research.google.com/drive/10530_jt_Joa5zH2wgYlyXosypq1R7PIz?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
 
 
 ## 📊 Performance