Update README.md

README.md

@@ -1,29 +1,77 @@
 ---
+tags:
+- liquid
+- lfm2
+- lfm2.5
+- longevity
+library_name: transformers
 base_model: LiquidAI/LFM2-2.6B
 license: other
 license_name: lfm1.0
 license_link: LICENSE
 ---
 
-## Longevity-LLM · LFM2-2.6B
-**Longevity-LLM (L-LLM)** is a family of compact, domain-adapted language models for interpreting heterogeneous aging biology data. This checkpoint, **L-LFM2-2.6B**, was produced by full-parameter supervised fine-tuning of [LiquidAI/LFM2-2.6B](https://huggingface.co/LiquidAI/LFM2-2.6B) on aging-related multi-omics and clinical data.
+<div align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" 
+    alt="Liquid AI" 
+    style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
+  />
+  <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
+    <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • 
+    <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
+  </div>
+</div>
+
+## Longevity-LLM - LFM2-2.6B
+**Longevity-LLM (L-LLM)** is a family of compact, domain-adapted language models for interpreting heterogeneous aging biology data. Longevity LFMs are available in two sizes:
+
+- [**LFM2-1.2B-Longevity**](https://huggingface.co/LiquidAI/LFM2-1.2B-Longevity/)
+- **LFM2-2.6B-Longevity** *(this model)*
+
+This checkpoint, **L-LFM2-2.6B**, was produced by full-parameter supervised fine-tuning of [LiquidAI/LFM2-2.6B](https://huggingface.co/LiquidAI/LFM2-2.6B) on aging-related multi-omics and clinical data.
 
 The family was developed jointly by [**Insilico Medicine**](https://insilico.com) and [**Liquid AI**](https://www.liquid.ai) and accompanies the study "An Open Benchmark and Language Models for AI in Aging Biology" (Zhavoronkov et al., 2026).
 
-## Model description
+## Model details
 - **Base model**: [LiquidAI/LFM2-2.6B](https://huggingface.co/LiquidAI/LFM2-2.6B)
 - **Architecture**: Hybrid Liquid model with multiplicative gates and short convolutions.
 - **Context length**: 32,768 tokens
+- **Language**: English
+
+**Training data**. The model was trained on the shared L-LLM corpus spanning aging biology. See [LongevityBench](https://huggingface.co/datasets/insilicomedicine/longebench) for more details.
+
+**Training procedure.** L-LFM2-2.6B was trained with full-parameter supervised fine-tuning. Prompts were formatted in ChatML with a dynamic-thinking template (user turns suffixed with `/think` or `/no_think` to select response mode at inference).
+
+### Chat Template
+
+LFM2 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template) for details. Example:
+
+```
+<|startoftext|><|im_start|>system
+You are a helpful assistant trained by Liquid AI.<|im_end|>
+<|im_start|>user
+What is C. elegans?<|im_end|>
+<|im_start|>assistant
+```
+
+You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically.
 
 
-## Training data
-The model was trained on the shared L-LLM corpus spanning aging biology. See [LongevityBench](https://huggingface.co/datasets/insilicomedicine/longebench) for more details.
+## Inference
 
+LFM2 is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.
 
-## Training procedure
-L-LFM2-2.6B was trained with full-parameter supervised fine-tuning. Prompts were formatted in ChatML with a dynamic-thinking template (user turns suffixed with `/think` or `/no_think` to select response mode at inference).
+| Name | Description | Docs | Notebook |
+|------|-------------|------|:--------:|
+| [Transformers](https://github.com/huggingface/transformers) | Simple inference with direct access to model internals. | <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> | <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> | <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| [SGLang](https://github.com/sgl-project/sglang) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/deployment/gpu-inference/sglang">Link</a> | — |
+| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> | <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| [MLX](https://github.com/ml-explore/mlx) | Apple's machine learning framework optimized for Apple Silicon. | <a href="https://docs.liquid.ai/lfm/inference/mlx">Link</a> | — |
+| [LM Studio](https://lmstudio.ai/) | Desktop application for running LLMs locally. | <a href="https://docs.liquid.ai/deployment/on-device/lm-studio">Link</a> | — |
 
-## Example usage with Transformers (compatible with `transformers>=5.1.0`)
+Quick start with Transformers (compatible with `transformers>=5.1.0`)
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -62,13 +110,13 @@ output = model.generate(
 
 print(tokenizer.decode(output[0], skip_special_tokens=False))
 ```
-
 ## Intended use and limitations
-
 Intended for research on aging biology and omics interpretation. Outputs are model predictions, not clinical advice, and should be validated experimentally. Performance is strongest on the modalities represented in the training corpus.
 
-## License
-The fine-tuned model is released under LFM Open License v1.0. The underlying base model remains under its original license.
+## Contact
+
+- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
+- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
 
 ## Citation
 
@@ -81,4 +129,13 @@ The fine-tuned model is released under LFM Open License v1.0. The underlying bas
 	doi          = { 10.57967/hf/9888 },
 	publisher    = { Hugging Face }
 }
+```
+
+```bibtex
+@article{liquidai2025lfm2,
+ title={LFM2 Technical Report},
+ author={Liquid AI},
+ journal={arXiv preprint arXiv:2511.23404},
+ year={2025}
+}
 ```