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---
tags:
- liquid
- lfm2
- lfm2.5
- longevity
library_name: transformers
base_model: LiquidAI/LFM2-2.6B
license: other
license_name: lfm1.0
license_link: LICENSE
---
<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 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 biomedical AI specialized in aging biology, trained on genomic, proteomic, and clinical data.<|im_end|>
<|im_start|>user
Which of the following two Melanoma cases has had a longer progression-free interval after the initial RNAseq screening?
Options:
Patient-A: A 77-year-old male diagnosed with Stage I, TT2a, NNX, MM0 disease non-ulcerated, Clark level III;
Patient-B: A 85-year-old male diagnosed with Stage III, TTX, NN2, MM0 disease
GSEA results:
No significant pathway differences detected.<|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.


## Inference

LFM2 is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.

| 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> | — |

Quick start with Transformers (compatible with `transformers>=5.1.0`)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model_id = "LiquidAI/LFM2-2.6B-Longevity" 
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Generate answer
messages = [
    {"role": "system", "content": "You are a biomedical AI specialized in aging biology, trained on genomic, proteomic, and clinical data."},
    {"role": "user", "content": "What are the hallmarks of aging?"},
]
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
    return_dict=False,
).to(model.device)
output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_new_tokens=1500
)
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.

## 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
```bibtex
@article{zhavoronkov2026longevitybench,
  title   = {An Open Benchmark and Language Models for AI in Aging Biology},
  author  = {Zhavoronkov, Alex and Naumov, Vladimir and Sidorenko, Denis and Aliper, Alex and Aladinskiy, Vladimir and Hasani, Ramin and Amini, Alexander and Nasto, Katerina and Reymond, Mathieu and Shayakhmetov, Rim and Miftakhutdinov, Zulfat and Gladyshev, Vadim N. and Galkin, Fedor},
  journal = {Cell},
  volume  = {189},
  pages   = {5980--5994},
  year    = {2026},
  doi     = {10.1016/j.cell.2026.08.026},
  url     = {https://www.cell.com/cell/fulltext/S0092-8674(26)00999-2},
}
```

```bibtex
@article{liquidai2025lfm2,
 title={LFM2 Technical Report},
 author={Liquid AI},
 journal={arXiv preprint arXiv:2511.23404},
 year={2025}
}
```

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	---
	tags:
	- liquid
	- lfm2
	- lfm2.5
	- longevity
	library_name: transformers
	base_model: LiquidAI/LFM2-2.6B
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	---

	<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 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 biomedical AI specialized in aging biology, trained on genomic, proteomic, and clinical data.<\|im_end\|>
	<\|im_start\|>user
	Which of the following two Melanoma cases has had a longer progression-free interval after the initial RNAseq screening?

	Options:
	Patient-A: A 77-year-old male diagnosed with Stage I, TT2a, NNX, MM0 disease non-ulcerated, Clark level III;
	Patient-B: A 85-year-old male diagnosed with Stage III, TTX, NN2, MM0 disease

	GSEA results:
	No significant pathway differences detected.<\|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.


	## Inference

	LFM2 is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.

	\| 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> \| — \|

	Quick start with Transformers (compatible with `transformers>=5.1.0`)

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	# Load model and tokenizer
	model_id = "LiquidAI/LFM2-2.6B-Longevity"
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="auto",
	dtype="bfloat16",
	)
	tokenizer = AutoTokenizer.from_pretrained(model_id)

	# Generate answer
	messages = [
	{"role": "system", "content": "You are a biomedical AI specialized in aging biology, trained on genomic, proteomic, and clinical data."},
	{"role": "user", "content": "What are the hallmarks of aging?"},
	]

	input_ids = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	return_tensors="pt",
	tokenize=True,
	return_dict=False,
	).to(model.device)

	output = model.generate(
	input_ids,
	do_sample=True,
	temperature=0.3,
	min_p=0.15,
	repetition_penalty=1.05,
	max_new_tokens=1500
	)

	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.

	## 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
	```bibtex
	@article{zhavoronkov2026longevitybench,
	title = {An Open Benchmark and Language Models for AI in Aging Biology},
	author = {Zhavoronkov, Alex and Naumov, Vladimir and Sidorenko, Denis and Aliper, Alex and Aladinskiy, Vladimir and Hasani, Ramin and Amini, Alexander and Nasto, Katerina and Reymond, Mathieu and Shayakhmetov, Rim and Miftakhutdinov, Zulfat and Gladyshev, Vadim N. and Galkin, Fedor},
	journal = {Cell},
	volume = {189},
	pages = {5980--5994},
	year = {2026},
	doi = {10.1016/j.cell.2026.08.026},
	url = {https://www.cell.com/cell/fulltext/S0092-8674(26)00999-2},
	}
	```

	```bibtex
	@article{liquidai2025lfm2,
	title={LFM2 Technical Report},
	author={Liquid AI},
	journal={arXiv preprint arXiv:2511.23404},
	year={2025}
	}
	```