Update README.md

README.md

@@ -1,4 +1,15 @@
-# Qwen3 4B Q4
+# Good and Small models for Mobile Devices
+
+Try them out in 
+
+ ![PrivacyAIIcon](https://www.acmeup.com/icons/32.png)  [Privacy AI](https://apps.apple.com/app/apple-store/id6738392421?pt=127450194&ct=huggingface&mt=8) 
+
+on the App Store.
+
+[Privacy AI](https://apps.apple.com/app/apple-store/id6738392421?pt=127450194&ct=huggingface&mt=8) is a lightweight, serverless application. All tools - including web search, stock quotes, and Health analysis - run on-device, keeping data and actions fully private. It supports both local AI models and connections to your own OpenAI-compatible servers.
+
+
+## Qwen3 4B Q4
 
 Qwen3 is the latest generation of Qwen series. It is a large language model with 1.7B parameters, optimized for mobile devices. It is capable of running functions on mobile devices and has been trained to follow instructions and generate long texts (32K tokens). It is more resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. It has up to 128K tokens and can generate up to 32K tokens. It supports for over 100 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.  
 
@@ -35,7 +46,7 @@ Qwen3 is the latest generation of Qwen series. It is a large language model with
 
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-# GLM Edge 4B Chat
+## GLM Edge 4B Chat
 
 GLM-4 is the latest generation of pre-trained models in the GLM-4 series launched by Zhipu AI. In the evaluation of data sets in semantics, mathematics, reasoning, code, and knowledge, GLM-4 has shown superior performance beyond Llama-3. In addition to multi-round conversations, GLM-4-Chat also has advanced features such as web browsing, code execution, custom tool calls (Function Call), and long text reasoning (supporting up to 128K context). This generation of models has added multi-language support, supporting 26 languages including Japanese, Korean, and German.  
 
@@ -76,7 +87,7 @@ GLM-4 is the latest generation of pre-trained models in the GLM-4 series launche
 
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-# Gemma 3n E2B it
+## Gemma 3n E2B it
 
 Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for pre-trained and instruction-tuned variants. These models were trained with data in over 140 spoken languages.  
 
@@ -115,7 +126,7 @@ Gemma 3n models are designed for efficient execution on low-resource devices. Th
 
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-# SmolLM2 1.7B
+## SmolLM2 1.7B
 
 SmolLM2 was trained on 11 trillion tokens and demonstrates significant advances over other small models, particularly in instruction following, knowledge, reasoning, and mathematics.  
 
@@ -159,7 +170,7 @@ SmolLM2 was trained on 11 trillion tokens and demonstrates significant advances
 
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-# Phi4 mini 4B
+## Phi4 mini 4B
 
 Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model is intended for broad multilingual commercial and research use. The model provides uses for general purpose AI systems and applications which require: 1). Memory/compute constrained environments; 2). Latency bound scenarios; 3) Strong reasoning (especially math and logic).  The model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.  
 
@@ -196,7 +207,7 @@ Phi-4-mini-instruct is a lightweight open model built upon synthetic data and fi
 
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-# Qwen3 1.7B
+## Qwen3 1.7B
 
 Qwen3 1.7B is one of the small models in the Qwen series, designed for efficiency and speed. It can run seamlessly on edge devices, enabling rapid inference and real-time applications. This compact model is ideal for testing scenarios, prototyping, or deployment in resource-constrained environments.  
 
@@ -233,7 +244,7 @@ Qwen3 1.7B is one of the small models in the Qwen series, designed for efficienc
 
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-# ERNIE-4.5 0.3B
+## ERNIE-4.5 0.3B
 
 ERNIE 4.5 is a series of open source models created by Baidu. The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations: 1. Multimodal Heterogeneous MoE Pre-Training; 2. Scaling-Efficient Infrastructure; 3. Modality-Specific Post-Training  
 
@@ -270,7 +281,7 @@ ERNIE 4.5 is a series of open source models created by Baidu. The advanced capab
 
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-# SmolLM3 3B
+## SmolLM3 3B
 
 SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale. The model is a decoder-only transformer using GQA and NoPE (with 3:1 ratio), it was pretrained on 11.2T tokens with a staged curriculum of web, code, math and reasoning data. Post-training included midtraining on 140B reasoning tokens.  
 
@@ -305,4 +316,41 @@ SmolLM3 is a fully open model that offers strong performance at the 3B–4B scal
 **Parse Special Tokens:** Yes  
 
 
+---
+
+## LFM2 1.2B
+
+LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions. It supported languages: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.  
+
+**Model Intention:** LFM2 1.2B is particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations  
+
+**Model URL:** [https://huggingface.co/flyingfishinwater/good_and_small_models/resolve/main/LFM2-1.2B-Q4_0.gguf?download=true](https://huggingface.co/flyingfishinwater/good_and_small_models/resolve/main/LFM2-1.2B-Q4_0.gguf?download=true)  
+
+**Model Info URL:** [https://huggingface.co/LiquidAI/LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B)  
+
+**Model License:** [License Info](https://huggingface.co/LiquidAI/LFM2-1.2B/raw/main/LICENSE)  
+
+**Model Description:** LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions. It supported languages: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.  
+
+**Developer:** [https://huggingface.co/LiquidAI](https://huggingface.co/LiquidAI)  
+
+**File Size:** 696 MB  
+
+**Context Length:** 1024 tokens  
+
+**Prompt Format:** 
+
+```
+
+```
+
+**Template Name:** qwen  
+
+**Add BOS Token:** Yes  
+
+**Add EOS Token:** No  
+
+**Parse Special Tokens:** Yes  
+
+
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