---

pipeline_tag: text-generation

base_model: LiquidAI/LFM2.5-2.6B-Base

license: other

license_name: lfm1.0

library_name: llama-cpp

tags:

• liquid
• lfm2.5
• gguf
• quantized

languages:

• en
• ar
• zh
• fr
• de
• hi
• id
• it
• ja
• ko
• pl
• pt
• ru
• es
• th
• vi

---

LFM2.5-2.6B GGUF

GGUF quantized versions of LiquidAI/LFM2.5-2.6B, a high-performance hybrid model designed for on-device deployment, featuring a 128K context window and advanced agentic capabilities.

Model Overview

LFM2.5-2.6B is part of the LFM2.5 family, building on the LFM2 architecture to provide best-in-class performance for its size. It is specifically optimized for agentic workloads, tool use, and long-context workflows, offering competitive performance against models 4x its size.

Key features include:

• Agentic Post-Training: Trained using agentic reinforcement learning for improved tool use and instruction following.
• Efficient Inference: Designed for high-speed execution on both CPU and GPU.
• Reasoning Capabilities: A pure reasoning model that utilizes a <think> tag to reason before answering.
• Massive Context: Supports up to 131,072 tokens.

Model Architecture

| Property | Value |

| :----------------------- | :--------- |

| Architecture | LFM2 |

| Parameters | 2.69B |

| Layers | 30 (22 conv + 8 GQA) |

| Context Length | 131,072 |

| Vocabulary Size | 128,000 |

| Training Budget | 34 Trillion Tokens |

| Supported Languages | English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish |

Available GGUF Files

| File | Quantization | Use Case |

| :------------------------- | :----------- | :----------------------------------------- |

| lfm2.5-2.6b.gguf | FP16/BF16 | Max precision, reference model |

| lfm2.5-2.6b-Q8_0.gguf | Q8_0 | Near-lossless, high fidelity |

| lfm2.5-2.6b-Q6_K.gguf | Q6_K | Very high quality, recommended for quality |

| lfm2.5-2.6b-Q5_K_M.gguf | Q5_K_M | High quality, balanced |

| lfm2.5-2.6b-Q5_K_S.gguf | Q5_K_S | High quality, slightly smaller |

| lfm2.5-2.6b-Q4_K_M.gguf | Q4_K_M | Good quality, recommended default |

| lfm2.5-2.6b-Q4_K_S.gguf | Q4_K_S | Smaller, acceptable quality |

| lfm2.5-2.6b-Q4_0.gguf | Q4_0 | Legacy quant, fastest inference |

> Recommended: Q4_K_M or Q5_K_M offer the best quality-to-size trade-off for most use cases.

Usage

llama.cpp CLI

./llama-cli \
  -m lfm2.5-2.6b-Q4_K_M.gguf \
  -p "What is the capital of France?" \
  --temp 0.1 --top-k 50 --repeat-penalty 1.1

llama-server (OpenAI-compatible API)

./llama-server \
  -m lfm2.5-2.6b-Q4_K_M.gguf \
  --host 0.0.0.0 --port 8080

Chat Template & Reasoning

LFM2.5 uses a ChatML-like format. It is a reasoning model that automatically adds a <think> tag when starting an assistant answer to process its logic before providing the final response.

Example format:

<|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
<think>
... reasoning process ...
</think>
C. elegans is a species of small roundworm...<|im_end|>

Tool Calling

LFM2.5 supports Pythonic function calling. It outputs function calls between <|tool_call_start|> and <|tool_call_end|> tokens.

Generation Parameters

Recommended parameters for optimal performance:

| Parameter | Value |

| :------------------ | :---- |

| Temperature | 0.1 |

| Top-K | 50 |

| Repetition Penalty | 1.1 |

Quantization

These GGUF files were created using llama.cpp tools to enable efficient local deployment on CPUs and GPUs with reduced memory footprints.

Acknowledgements

• Original model: LiquidAI/LFM2.5-2.6B
• Quantization tool: llama.cpp

License

LFM 1.0 License