license: other language:

• en
• zh library_name: llama.cpp pipeline_tag: text-generation tags:
• gguf
• lfm2
• lfm2.5
• llama.cpp
• atomic-chat base_model:
• LiquidAI/LFM2.5-2.6B

LFM2.5 2.6B Uncensored — GGUF

Unofficial GGUF conversions of SC117/LFM2.5-2.6B-Uncensored, prepared from the original BF16 weights for local inference with llama.cpp, Atomic Chat, LM Studio, and other GGUF-compatible applications.

This repository is not affiliated with Liquid AI. Please review the upstream license before redistribution or commercial use.

Available files

Sizes are the local GGUF file sizes in decimal GB. All variants retain the model's tokenizer, chat template, and metadata.

File SHA-256

Recommended choice for an M2 MacBook with 16 GB RAM

• Q6_K is the recommended everyday choice for short English/Chinese image and video prompts.
• Q8_0 is also practical and should preserve a little more quality, at the cost of a larger model and somewhat higher memory bandwidth use.
• Keep context moderate (for example, 4k–8k tokens) for the fastest response.

The model weights fit comfortably in 16 GB unified memory; the operating system, application, context, and KV cache still need headroom.

llama.cpp

llama-server \
  -m LFM2.5-2.6B-Uncensored-Q6_K.gguf \
  -c 8192 -ngl 99 -fa on --jinja

Use the Q8 file in the -m argument when maximum quality is preferred. The exact flags available depend on the llama.cpp build and hardware backend.

Atomic Chat

Load one of the GGUF files with the Llama.cpp engine. For structured prompt generation, Atomic Chat's Llama.cpp settings expose Grammar File and JSON Schema File output constraints. A simple flat JSON Schema is usually more reliable than a large schema with nested references.

Atomic Chat's local OpenAI-compatible server is normally available at http://127.0.0.1:1337/v1. With a llama.cpp backend, a request can use response_format with type: "json_schema" to constrain the generated prompt object. Do not combine a custom grammar/schema with native tool-call grammar in the same request.

Example structured output schema

{
  "type": "object",
  "properties": {
    "prompt_en": { "type": "string" },
    "prompt_zh": { "type": "string" },
    "negative_prompt": { "type": "string" },
    "parameters": { "type": "string" }
  },
  "required": ["prompt_en", "prompt_zh", "negative_prompt", "parameters"],
  "additionalProperties": false
}

The schema constrains the output shape; describe the intended fields in the prompt as well. Validate the returned JSON in the calling application.

Quantization provenance

The files were quantized directly from the BF16 GGUF conversion of the upstream model with llama-quantize (llama.cpp build 10278, commit d52ec04a6):

llama-quantize LFM2.5-2.6B-Uncensored-BF16.gguf LFM2.5-2.6B-Uncensored-Q4_K_M.gguf Q4_K_M 8
llama-quantize LFM2.5-2.6B-Uncensored-BF16.gguf LFM2.5-2.6B-Uncensored-Q5_K_M.gguf Q5_K_M 8
llama-quantize LFM2.5-2.6B-Uncensored-BF16.gguf LFM2.5-2.6B-Uncensored-Q6_K.gguf Q6_K 8
llama-quantize LFM2.5-2.6B-Uncensored-BF16.gguf LFM2.5-2.6B-Uncensored-Q8_0.gguf Q8_0 8

Original model: SC117/LFM2.5-2.6B-Uncensored