Liquid AI 发布 LFM2.5-8B-A1B：一款支持设备端部署的混合专家架构模型，总参数 8.3B，活跃参数 1.5B
2026-05-29 07:29·35天前·Asif Razzaq
AI 摘要
Liquid AI 发布了 LFM2.5-8B-A1B 模型。这是一款混合专家架构模型，总参数量为 8.3B，每次推理仅激活其中的 1.5B 参数。该模型支持 128K 上下文窗口，具备推理和工具调用能力，可在消费级硬件上本地运行。
原文 · 未翻译
Liquid AI just shipped LFM2.5-8B-A1B. It is an on-device Mixture-of-Experts (MoE) model built for tool calling. The model holds 8.3B total parameters but activates only 1.5B per token. That sparsity is what lets it run on consumer hardware.
The release follows LFM2-8B-A1B, which Liquid AI team published earlier. LFM2.5 is a new family of hybrid models for on-device deployment. This version adds a 128K context window, reasoning, and scaled-up training.
What is LFM2.5-8B-A1B
The model uses a sparse MoE design. It activates 1.5B of 8.3B total parameters per forward pass. That keeps each generated token cheap to compute.
The architecture has 24 layers. Eighteen are double-gated LIV convolution blocks; six are GQA layers. It combines MoE, GQA, and gated short convolution blocks. The context length is 131,072 tokens. The model covers nine languages, including Arabic, Chinese, and Japanese.
Liquid AI team recommends a temperature of 0.2, top_k of 80, and repetition_penalty of 1.05.
Unlike its predecessor, LFM2.5-8B-A1B is a reasoning-only model. It produces an explicit chain of thought before its final answer. Liquid AI team chose this because MoE models run in compute-bound settings. A smaller active parameter count makes each reasoning token inexpensive.
What Changed Since LFM2-8B-A1B
Liquid expanded the context window from 32,768 to 128,000 tokens. Pretraining scaled from 12T to 38T tokens. The vocabulary doubled from 65,536 to 128,000 tokens.
The larger vocabulary tokenizes non-Latin scripts more efficiently. Liquid AI team reports the strongest compression gains in Hindi, Thai, Vietnamese, Indonesian, and Arabic. The rest of the architecture stays the same as LFM2-8B-A1B.
How Liquid AI Trained It
Liquid AI team extended the tokenizer in place rather than retraining from scratch. It continued BPE merge training from the original merges on a multilingual corpus. New embedding rows initialize as the mean of their sub-token decompositions. A brief two-stage adaptation then recovers quality.
Liquid AI 发布了 LFM2.5-8B-A1B 模型。这是一款混合专家架构模型，总参数量为 8.3B，每次推理仅激活其中的 1.5B 参数。该模型支持 128K 上下文窗口，具备推理和工具调用能力，可在消费级硬件上本地运行。
原文 · 保持原样，未翻译
Liquid AI just shipped LFM2.5-8B-A1B. It is an on-device Mixture-of-Experts (MoE) model built for tool calling. The model holds 8.3B total parameters but activates only 1.5B per token. That sparsity is what lets it run on consumer hardware.
The release follows LFM2-8B-A1B, which Liquid AI team published earlier. LFM2.5 is a new family of hybrid models for on-device deployment. This version adds a 128K context window, reasoning, and scaled-up training.
What is LFM2.5-8B-A1B
The model uses a sparse MoE design. It activates 1.5B of 8.3B total parameters per forward pass. That keeps each generated token cheap to compute.
The architecture has 24 layers. Eighteen are double-gated LIV convolution blocks; six are GQA layers. It combines MoE, GQA, and gated short convolution blocks. The context length is 131,072 tokens. The model covers nine languages, including Arabic, Chinese, and Japanese.
Context extension came in two phases. A 2T token midtraining phase reached 32K, focused on reasoning, math, and tool use. Raising the RoPE base θ, plus a 400B token stage, reached 128K.
Two reinforcement learning stages target known failure modes. A preference optimization stage reduces ‘doom loops’ in long reasoning traces. It redistributes probability mass toward plausible alternatives. A separate RL shaping reward discourages loop-inducing restart words like ‘Wait…’. Another RL stage uses an avg@k-based reward to cut hallucinations. The goal is abstention on queries beyond reliable knowledge.
https://www.liquid.ai/blog/lfm2-5-8b-a1b
The Benchmark Case
LFM2.5-8B-A1B improves over its predecessor across the board. The AA-Omniscience Non-Hallucination Rate jumped from 7.46 to 63.47. IFEval rose from 79.44 to 91.84. MATH500 climbed from 74.80 to 88.76. Tau² Telecom rose from 13.60 to 88.07.
Liquid AI team compared the model against dense and MoE alternatives. On instruction following, it matches Gemma-4-26B-A4B-IT on IFEval. It does so at a fraction of the active parameter count. On Tau² Telecom, it scores 88.07, ahead of much larger models.
The avg@k reward drives a much lower hallucination rate. Accuracy stays reasonable for the model’s size. On agentic benchmarks, it remains competitive with bigger models.
The model ships with day-one support across the inference ecosystem. Frameworks include llama.cpp, MLX, vLLM, and SGLang. ONNX and Liquid’s LEAP edge platform are also supported.
On CPU, it decodes 253 tokens/s on an M5 Max. It reaches 146 tokens/s on a Ryzen AI Max+ 395. It stays under 6 GB of memory throughout. On a phone, it holds about 30 tokens/s.
On a single NVIDIA H100 SXM5, output throughput hits 18.5K tokens per second. That is over 1.6B tokens per day at high concurrency.
For tool use, LFM2.5 writes Pythonic function calls by default. They appear between the and special tokens. You can override this to JSON in the system prompt.
Strengths and What to Watch
Strengths:
Activates only 1.5B parameters, keeping inference cheap on edge hardware
Competitive instruction-following and agentic scores for its size class
128K context window and nine-language coverage
Open-weight under the LFM1.0 license, with base and post-trained checkpoints
What to Watch:
Limited knowledge capacity from the small active parameter count
Not a fit for heavy programming or knowledge-intensive QA without retrieval
Reasoning-only output adds chain-of-thought tokens to every turn
Text-only; this variant has no vision or audio input
Marktechpost’s Visual Explainer
On-Device Model Guide
LFM2.5-8B-A1B
Liquid AI’s on-device Mixture-of-Experts model, built for tool calling and complex instruction following on consumer hardware.
8.3B total params 1.5B active 128K context reasoning‑only open‑weight
Released May 28, 2026 · Liquid AI · LFM1.0 license
What It Is
A sparse MoE that activates 1.5B of 8.3B parameters per token
24 layers — 18 double-gated LIV convolution blocks plus 6 GQA layers.
Combines MoE, GQA, and gated short convolution blocks.
Context length of 131,072 tokens; covers 9 languages.
Reasoning-only: produces an explicit chain of thought before answering.
Recommended params: temperature 0.2, top_k 80, repetition_penalty 1.05.
What Changed Since LFM2-8B-A1B
Bigger context, more training, a wider vocabulary
Context window
32,768 → 128,000
Processes longer documents and reasons for longer.
Or load directly with Transformers from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "LiquidAI/LFM2.5-8B-A1B" model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", dtype="bfloat16") tokenizer = AutoTokenizer.from_pretrained(model_id)
Recommended for
Agentic workflows Tool use Structured outputs Multilingual assistants On-device assistants
Less suited to
Heavy programming Knowledge-intensive QA without retrieval
← →
01 / 08
Key Takeaways
Liquid AI's LFM2.5-8B-A1B holds 8.3B total parameters but activates only 1.5B per token.
It is reasoning-only, with a 128K context window and nine-language coverage.
Non-Hallucination Rate jumped from 7.46 to 63.47 over LFM2-8B-A1B; IFEval reached 91.84.
It decodes 253 tok/s on an M5 Max under 6 GB, and ~30 tok/s on a phone.
Day-one support spans llama.cpp, MLX, vLLM, and SGLang, with open base and post-trained weights.
Check out the Model Weights and Technical details. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us
The post Liquid AI Releases LFM2.5-8B-A1B: An On-Device MoE Model With 8.3B Total and 1.5B Active Parameters appeared first on MarkTechPost.
Liquid AI team recommends a temperature of 0.2, top_k of 80, and repetition_penalty of 1.05.
Unlike its predecessor, LFM2.5-8B-A1B is a reasoning-only model. It produces an explicit chain of thought before its final answer. Liquid AI team chose this because MoE models run in compute-bound settings. A smaller active parameter count makes each reasoning token inexpensive.
What Changed Since LFM2-8B-A1B
Liquid expanded the context window from 32,768 to 128,000 tokens. Pretraining scaled from 12T to 38T tokens. The vocabulary doubled from 65,536 to 128,000 tokens.
The larger vocabulary tokenizes non-Latin scripts more efficiently. Liquid AI team reports the strongest compression gains in Hindi, Thai, Vietnamese, Indonesian, and Arabic. The rest of the architecture stays the same as LFM2-8B-A1B.
How Liquid AI Trained It
Liquid AI team extended the tokenizer in place rather than retraining from scratch. It continued BPE merge training from the original merges on a multilingual corpus. New embedding rows initialize as the mean of their sub-token decompositions. A brief two-stage adaptation then recovers quality.
Context extension came in two phases. A 2T token midtraining phase reached 32K, focused on reasoning, math, and tool use. Raising the RoPE base θ, plus a 400B token stage, reached 128K.
Two reinforcement learning stages target known failure modes. A preference optimization stage reduces ‘doom loops’ in long reasoning traces. It redistributes probability mass toward plausible alternatives. A separate RL shaping reward discourages loop-inducing restart words like ‘Wait…’. Another RL stage uses an avg@k-based reward to cut hallucinations. The goal is abstention on queries beyond reliable knowledge.
https://www.liquid.ai/blog/lfm2-5-8b-a1b
The Benchmark Case
LFM2.5-8B-A1B improves over its predecessor across the board. The AA-Omniscience Non-Hallucination Rate jumped from 7.46 to 63.47. IFEval rose from 79.44 to 91.84. MATH500 climbed from 74.80 to 88.76. Tau² Telecom rose from 13.60 to 88.07.
Liquid AI team compared the model against dense and MoE alternatives. On instruction following, it matches Gemma-4-26B-A4B-IT on IFEval. It does so at a fraction of the active parameter count. On Tau² Telecom, it scores 88.07, ahead of much larger models.
The avg@k reward drives a much lower hallucination rate. Accuracy stays reasonable for the model’s size. On agentic benchmarks, it remains competitive with bigger models.
The model ships with day-one support across the inference ecosystem. Frameworks include llama.cpp, MLX, vLLM, and SGLang. ONNX and Liquid’s LEAP edge platform are also supported.
On CPU, it decodes 253 tokens/s on an M5 Max. It reaches 146 tokens/s on a Ryzen AI Max+ 395. It stays under 6 GB of memory throughout. On a phone, it holds about 30 tokens/s.
On a single NVIDIA H100 SXM5, output throughput hits 18.5K tokens per second. That is over 1.6B tokens per day at high concurrency.
For tool use, LFM2.5 writes Pythonic function calls by default. They appear between the and special tokens. You can override this to JSON in the system prompt.
Strengths and What to Watch
Strengths:
Activates only 1.5B parameters, keeping inference cheap on edge hardware
Competitive instruction-following and agentic scores for its size class
128K context window and nine-language coverage
Open-weight under the LFM1.0 license, with base and post-trained checkpoints
What to Watch:
Limited knowledge capacity from the small active parameter count
Not a fit for heavy programming or knowledge-intensive QA without retrieval
Reasoning-only output adds chain-of-thought tokens to every turn
Text-only; this variant has no vision or audio input
Marktechpost’s Visual Explainer
On-Device Model Guide
LFM2.5-8B-A1B
Liquid AI’s on-device Mixture-of-Experts model, built for tool calling and complex instruction following on consumer hardware.
8.3B total params 1.5B active 128K context reasoning‑only open‑weight
Released May 28, 2026 · Liquid AI · LFM1.0 license
What It Is
A sparse MoE that activates 1.5B of 8.3B parameters per token
24 layers — 18 double-gated LIV convolution blocks plus 6 GQA layers.
Combines MoE, GQA, and gated short convolution blocks.
Context length of 131,072 tokens; covers 9 languages.
Reasoning-only: produces an explicit chain of thought before answering.
Recommended params: temperature 0.2, top_k 80, repetition_penalty 1.05.
What Changed Since LFM2-8B-A1B
Bigger context, more training, a wider vocabulary
Context window
32,768 → 128,000
Processes longer documents and reasons for longer.
Or load directly with Transformers from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "LiquidAI/LFM2.5-8B-A1B" model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", dtype="bfloat16") tokenizer = AutoTokenizer.from_pretrained(model_id)
Recommended for
Agentic workflows Tool use Structured outputs Multilingual assistants On-device assistants
Less suited to
Heavy programming Knowledge-intensive QA without retrieval
← →
01 / 08
Key Takeaways
Liquid AI's LFM2.5-8B-A1B holds 8.3B total parameters but activates only 1.5B per token.
It is reasoning-only, with a 128K context window and nine-language coverage.
Non-Hallucination Rate jumped from 7.46 to 63.47 over LFM2-8B-A1B; IFEval reached 91.84.
It decodes 253 tok/s on an M5 Max under 6 GB, and ~30 tok/s on a phone.
Day-one support spans llama.cpp, MLX, vLLM, and SGLang, with open base and post-trained weights.
Check out the Model Weights and Technical details. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us
The post Liquid AI Releases LFM2.5-8B-A1B: An On-Device MoE Model With 8.3B Total and 1.5B Active Parameters appeared first on MarkTechPost.