---

license: other

license_name: lfm-open-license-v1.0

license_link: https://www.liquid.ai/lfm-open-license

base_model: LiquidAI/LFM2.5-8B-A1B

tags:

- gguf

- nobodywho

- tool-calling

- lfm2

- moe

pipeline_tag: text-generation

library_name: gguf

---

NobodyWho/LFM2.5-8B-A1B-GGUF

Overview

GGUF quantization of LiquidAI's LFM2.5-8B-A1B model, prepared for

NobodyWho: it works with NobodyWho

out of the box, with LiquidAI's recommended sampling metadata embedded in every quant.

LFM2.5-8B-A1B is a sparse Mixture-of-Experts model (8B total / ~1B active per token) built on the

hybrid LFM2 architecture — the fastest model in its size class on both CPU and GPU.

Note: tool calling is unreliable on this model — see the Tool calling note below.

Model Capabilities

• Text generation — instruction-following chat
• Tool calling — supported but unreliable: the model often answers in prose instead of

invoking a tool. NobodyWho suite: 8/14 (F16, Q8_0), 4/14 (Q4_K_M)

• Long context — 128k tokens
• Efficient MoE — 8B total / ~1B active per token

NobodyWho preparation

The upstream GGUF (built from LiquidAI commit feb5e04) already renders tool calls correctly in

the model's native markup — <|tool_call_start|>[get_weather(city="Paris")]<|tool_call_end|> —

so nothing needs patching; NobodyWho just verifies it with the test suite. The

-vendor-sampling quants additionally embed LiquidAI's recommended sampling settings as

general.sampling.* metadata, which NobodyWho reads and applies by default (see

core/src/sampler.rs).

Available Quantizations

| File | Approach | Tool-calling tests |

|------|----------|--------------------|

| LFM2.5-8B-A1B-F16-vendor-sampling.gguf | Vendor sampling injected | 8/14 |

| LFM2.5-8B-A1B-Q8_0-vendor-sampling.gguf | Vendor sampling injected | 8/14 |

| LFM2.5-8B-A1B-Q4_K_M-vendor-sampling.gguf | Vendor sampling injected | 4/14 |

> Tool-calling results from NobodyWho's suite (June 2026). Failures are the model **declining to

> emit a tool call** on complex parameter schemas (sets / tuples / nested lists / dicts), not a

> format error. Vendor sampling does not change the result (verified with and without).

> The -vendor-sampling suffix marks files that embed general.sampling.* metadata.

Quick Start

Using the NobodyWho library:

from nobodywho import Chat

chat = Chat("huggingface:NobodyWho/LFM2.5-8B-A1B-GGUF/LFM2.5-8B-A1B-Q8_0-vendor-sampling.gguf")
response = chat.ask("What is the capital of Denmark?").completed()
print(response)  # The capital of Denmark is Copenhagen.

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="NobodyWho/LFM2.5-8B-A1B-GGUF",
    filename="LFM2.5-8B-A1B-Q8_0-vendor-sampling.gguf",
)

Model Specifications

• Parameters: 8B total / ~1B active (MoE)
• Context length: 128,000 tokens
• License: LFM Open License v1.0
• Base model: LiquidAI/LFM2.5-8B-A1B
• Architecture: lfm2moe

Licensing / Credits

Licensed under LFM Open License v1.0 (unchanged from upstream). All model credit belongs to

Liquid AI.