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:

python

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

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.