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lfm2.5-1.2b-instruct
Try LFM • Docs • LEAP • Discord
# LFM2.5-1.2B-Instruct
LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
- **Best-in-class performance**: A 1.2B model rivaling much larger models, bringing high-quality AI to your pocket.
- **Fast edge inference**: 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM.
- **Scaled training**: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning.
Find more information about LFM2.5 in our blog post.
## 🗒️ Model Details
LFM2.5-1.2B-Instruct is a general-purpose text-only model with the following features:
...
Repository: localaiLicense: other
lfm2.5-230m
LFM2.5-230M is LiquidAI's compact text model for edge and on-device use.
It has 230M parameters, a 128K-token context window, and support for ten
languages. This entry uses the recommended Q4_K_M GGUF quantization from
LiquidAI's official repository.
Repository: localaiLicense: other
lfm2.5-230m-q8
LFM2.5-230M is LiquidAI's compact text model for edge and on-device use.
It has 230M parameters, a 128K-token context window, and support for ten
languages. This entry uses the higher-quality Q8_0 GGUF quantization from
LiquidAI's official repository.
Repository: localaiLicense: other
lfm2.5-2.6b
LFM2.5-2.6B is LiquidAI's compact, text-only reasoning model for on-device
agentic workloads. It has 2.69B parameters, a 128K-token context window,
multilingual support, and post-training for tool use, instruction following,
data extraction, RAG, and multi-step agents. This entry uses the recommended
Q4_K_M GGUF quantization from LiquidAI's official repository.
Repository: localaiLicense: other
lfm2.5-2.6b-q8
LFM2.5-2.6B is LiquidAI's compact, text-only reasoning model for on-device
agentic workloads. It has 2.69B parameters, a 128K-token context window,
multilingual support, and post-training for tool use, instruction following,
data extraction, RAG, and multi-step agents. This entry uses the higher-quality
Q8_0 GGUF quantization from LiquidAI's official repository.
Repository: localaiLicense: other
lfm2.5-8b-a1b
Try LFM •
Docs •
LEAP •
Discord
# LFM2.5-8B-A1B
LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
- **On-device personal assistant**: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
- **Compressed performance**: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
- **Unmatched throughput**: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.
Find more information about LFM2.5-8B-A1B in our blog post.
**AA-Omniscience Index (higher is better) rewards correct answers and penalizes hallucinations. Scores range from -100 to 100. See more results on Artificial Analysis.*
## 🗒️ Model Details
LFM2.5-8B-A1B is a general-purpose text-only model with the following features:
...
Repository: localaiLicense: other
lfm2.5-1.2b-nova-function-calling
The **LFM2.5-1.2B-Nova-Function-Calling-GGUF** is a quantized version of the original model, optimized for efficiency with **Unsloth**. It supports text and multimodal tasks, using different quantization levels (e.g., Q2_K, Q3_K, Q4_K, etc.) to balance performance and memory usage. The model is designed for function calling and is faster than the original version, making it suitable for tasks like code generation, reasoning, and multi-modal input processing.
Repository: localaiLicense: apache-2.0
lfm2.5-audio-1.5b-realtime
LFM2.5-Audio-1.5B is LiquidAI's any-to-any audio foundation model. The
1.2B LFM2.5 backbone plus a FastConformer audio encoder and an LFM2-based
audio detokenizer give real-time speech-to-speech with text + audio output
interleaved at 12.5 Hz / 24 kHz. This entry runs in S2S (speech-to-speech)
mode and is the model the LocalAI realtime API any-to-any path consumes.
Switch to ASR, TTS, or chat by picking the sibling gallery entries.
Repository: localaiLicense: LFM-Open-License-v1.0
lfm2.5-audio-1.5b-chat
LFM2.5-Audio-1.5B in text-only chat mode. The model runs `generate_sequential`
with no audio modality, behaving like a small LFM2 chat model. Pick this
entry for tool-calling experiments without the audio overhead.
Repository: localaiLicense: LFM-Open-License-v1.0
lfm2.5-audio-1.5b-asr
LFM2.5-Audio-1.5B in ASR mode. System prompt `Perform ASR.` is prepended;
output is capitalised and punctuated. Wire this entry as a transcription
model on the /v1/audio/transcriptions endpoint.
Repository: localaiLicense: LFM-Open-License-v1.0
lfm2.5-audio-1.5b-tts
LFM2.5-Audio-1.5B in TTS mode. Four baked voices: us_male, us_female,
uk_male, uk_female — pick the default at load time via `voice:` option,
or override per-request via the OpenAI `/v1/audio/speech` `voice` field.
Repository: localaiLicense: LFM-Open-License-v1.0
lfm2.5-vl-1.6b
LFM2.5-VL-1.6B is Liquid AI's compact vision-language model for edge deployment. It improves instruction following, multilingual vision understanding, OCR, high-resolution images, and multi-image input over LFM2-VL-1.6B. This default entry uses the official Q4_K_M GGUF with the F16 vision projector.
License: LFM Open License 1.0.
Repository: localaiLicense: lfm1.0
lfm2.5-vl-1.6b-q8
LFM2.5-VL-1.6B in the higher-fidelity Q8_0 GGUF format with the F16 vision projector.
License: LFM Open License 1.0.
Repository: localaiLicense: lfm1.0
lfm2.5-8b-a1b-apex-i-quality
Repository: localai
lfm2.5-8b-a1b-apex-i-balanced
Repository: localai
lfm2.5-8b-a1b-apex-i-compact
Repository: localai
lfm2.5-8b-a1b-apex-i-mini
Repository: localai
lfm2.5-8b-a1b-ud-q4-k-m
Repository: localai
lfm2.5-8b-a1b-ud-q5-k-m
Repository: localai
lfm2.5-8b-a1b-ud-q6-k
Repository: localai
lfm2.5-8b-a1b-q8-0
Repository: localai