---

license: other

license_name: lfm1.0

license_link: LICENSE

base_model: LiquidAI/LFM2.5-2.6B

library_name: gguf

pipeline_tag: text-generation

language:

- ar

- zh

- en

- fr

- de

- hi

- id

- it

- ja

- ko

- pl

- pt

- ru

- es

- th

- vi

tags:

- gguf

- nobodywho

- liquid

- lfm2.5

- reasoning

- tool-calling

---

NobodyWho/LFM2.5-2.6B-GGUF

Overview

GGUF build of LiquidAI's LFM2.5-2.6B, prepared for

NobodyWho. The upstream GGUF cannot render a

single prompt in any llama.cpp-family runtime because of an invalid chat template; this repo

ships the same file with that template repaired. The weight tensors are bit-identical to

upstream — only three metadata keys differ.

LFM2.5 is a hybrid architecture built for on-device deployment, extending LFM2 with additional

pre-training and reinforcement learning. This checkpoint is a reasoning model: it emits a

<think> block before its answer.

Model Capabilities

• Text generation — instruction-following chat
• Reasoning — the template always opens a <think> block on the generation prompt
• Tool calling — native LFM2 function-calling format
• Long context — 128k tokens
• Multilingual — 16 languages

Getting Started

Install NobodyWho:

pip install nobodywho

Run — the model is downloaded and cached automatically on first use:

from nobodywho import Chat

chat = Chat("huggingface:NobodyWho/LFM2.5-2.6B-GGUF/LFM2.5-2.6B-Q4_K_M.gguf")
print(chat.ask("What is the capital of Denmark?").completed())

Because this is a reasoning model, the completion contains the model's <think> trace

followed by </think> and then the answer.

Tool calling

from nobodywho import Chat, tool

@tool(description="Gets the current weather for a city")
def get_weather(city: str) -> str:
    return f"It is sunny and 22°C in {city}."

chat = Chat(
    "huggingface:NobodyWho/LFM2.5-2.6B-GGUF/LFM2.5-2.6B-Q4_K_M.gguf",
    tools=[get_weather],
)
print(chat.ask("What is the weather in Paris?").completed())

The model reliably emits its native tool-call markup —

<|tool_call_start|>[get_weather(city='Paris')]<|tool_call_end|> — for both single and

follow-up turns.

> [!NOTE]

> Parsing that markup into executed tool calls ships in the upcoming nobodywho release

> (PR #564). On nobodywho 1.1.0 the

> markup is returned as text.

Why

The upstream template wraps the assistant branch of its message loop in

{% generation %} … {% endgeneration %}. Those tags are a Hugging Face transformers

extension used to mark assistant spans for return_assistant_tokens_mask during training.

They are not part of Jinja2, and no llama.cpp-family runtime implements them — so the template

fails to compile and every prompt errors out:

Could not render messages Template failed to render:
syntax error: unknown statement generation (in <string>:86)

The tags emit no output, so removing them is a no-op for the rendered prompt. The template in

this repo is byte-identical to upstream apart from those two lines.

Two related metadata problems were fixed at the same time:

• tokenizer.ggml.add_bos_token was absent, so runtimes fell back to their own default

(NobodyWho logs defaulting to true) while the template already emits {{- bos_token -}}

itself — putting two leading <|startoftext|> tokens on every prompt. Upstream's

tokenizer.json uses a plain ByteLevel post-processor that adds no BOS, so false is the

correct value.

• general.name held Ab00687315Bc1298E9D54E9C4B611Dde9867Ccc2, a build hash that leaked into

the metadata during the vendor's GGUF conversion.

Full metadata diff versus upstream

| Key | Upstream | Here |

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

| tokenizer.chat_template | {% generation %} tags present — fails to compile | tags removed, otherwise byte-identical |

| tokenizer.ggml.add_bos_token | absent | false |

| general.name | Ab00687315Bc1298E9D54E9C4B611Dde9867Ccc2 | LFM2.5 2.6B |

Nothing else changed: 266 tensors, identical names, shapes and dtypes, and an identical

SHA-256 over the tensor payload (9230ca69…4b978d6).

Files

| File | Fix recipe | Size |

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

| LFM2.5-2.6B-Q4_K_M.gguf | chat template + BOS + name | 1.6 GB |

Verification

The patched template was checked against every branch it can take — plain user turn, system

prompt, multi-turn with thinking stripped from pre-last-user turns, preserve_thinking=true,

tools in the system prompt, assistant tool_calls, multipart (list) content,

add_generation_prompt=false, and the CONTINUE_FINAL_MESSAGE_TAG path — then exercised in

NobodyWho for multi-turn chat with a system prompt and for single and follow-up tool calls.

Sampling notes

The file carries LiquidAI's recommended general.sampling.* metadata as upstream shipped it:

temperature 0.1, top_k 50. NobodyWho reads these and applies them by default.

The vendor additionally recommends repetition_penalty 1.1 — both the upstream model card and

leap/Q4_K_M.json agree on it — but it is not embedded in the GGUF. Pass it yourself if

you want the vendor's full recipe:

llama-cli -m LFM2.5-2.6B-Q4_K_M.gguf --conversation \
    --temp 0.1 --top-k 50 --repeat-penalty 1.1

Model Details

| Property | Value |

|---|---|

| Parameters | 2.6B |

| Quantization | Q4_K_M |

| Context length | 131,072 tokens |

| Architecture | lfm2 (hybrid) |

| Vocabulary | 128,000 tokens |

| License | LFM Open License v1.0 |

| Base model | LiquidAI/LFM2.5-2.6B |

| Upstream GGUF | LiquidAI/LFM2.5-2.6B-GGUF |

These files also work in any other llama.cpp-based runtime; the original unmodified GGUFs live

in the upstream LiquidAI/LFM2.5-2.6B-GGUF

repo.

License

LFM Open License v1.0, unchanged from upstream — see LICENSE.

All credit for the model goes to Liquid AI.