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.
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*AA-Omniscience Index (higher is better) rewards correct answers and penalizes hallucinations. Scores range from -100 to 100. See more results on Artificial Analysis.
MLX format for Apple Silicon. Optimized for fast inference on Mac devices.
We recommend using LFM2.5-8B-A1B for agentic workflows, tool use, structured outputs, multilingual assistants, and on-device personal-assistant applications. It is not the best fit for heavy programming or knowledge-intensive question answering without retrieval.
<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
What is C. elegans?<|im_end|>
<|im_start|>assistant
Because LFM2.5-8B-A1B is a reasoning model, assistant turns contain an explicit chain of thought before the final answer. You can use tokenizer.apply_chat_template() to format your messages automatically.
Tool Use
LFM2.5 supports function calling in four steps:
Function definition: Provide the list of tools as a JSON object in the system prompt, or use tokenizer.apply_chat_template() with tools=....
Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between <|tool_call_start|> and <|tool_call_end|> special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
Function execution: Execute the call and return the result with the tool role.
Final answer: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt.
<|startoftext|><|im_start|>system
List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|im_end|>
<|im_start|>user
What is the current status of candidate ID 12345?<|im_end|>
<|im_start|>assistant
<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
<|im_start|>tool
[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|>
<|im_start|>assistant
The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>
🏃 Inference
LFM2.5-8B-A1B is supported by many inference frameworks. See the Inference documentation for the full list.
Thanks to reasoning, scaled-up pre-training, and large-scale RL, LFM2.5-8B-A1B improves over its predecessor across the board:
Benchmark
LFM2-8B-A1B
LFM2.5-8B-A1B
Δ
AA-Omniscience Index
-78.42
-24.70
+53.62
AA-Omniscience Accuracy
7.33
8.67
+1.34
AA-Omniscience Non-Hallucination Rate
7.46
63.47
+56.01
IFEval
79.44
91.84
+12.40
IFBench
26.00
56.47
+30.47
Multi-IF
58.54
79.93
+21.39
MATH500
74.80
88.76
+13.96
AIME25
20.00
42.53
+22.53
BFCLv3
45.07
64.36
+19.29
BFCLv4
25.52
48.50
+22.98
Tau² Telecom
13.60
88.07
+74.47
Tau² Retail
7.02
39.82
+32.80
Knowledge and instruction following
Model
Parameters
AA-Omni. Index
AA-Omni. Accuracy
AA-Omni. Non-Halluc.
IFEval
IFBench
Multi-IF
LFM2.5-8B-A1B
8B/A1B
-24.70
8.67
63.47
91.84
56.47
79.93
Granite-4.0-H-Tiny
7B/A1B
-75.50
9.37
6.38
82.23
21.28
59.00
Qwen3.5-4B
4B
-51.53
17.20
16.99
87.80
50.38
67.43
Qwen3-30B-A3B-Thinking-2507
30.5B/3.3B
-51.31
18.80
13.87
90.82
51.11
79.04
Gemma-4-E2B-IT
5.1B
-72
7.00
15.05
82.93
33.53
69.70
Gemma-4-E4B-IT
8B
-50.67
8.10
36.06
87.74
39.48
77.58
Gemma-4-26B-A4B-IT
26B/4B
-62.07
14.37
10.75
91.40
47.25
82.06
gpt-oss-20b
21B/3.6B
-49.17
14.57
24.50
86.73
58.65
76.64
Math and agentic workflows
Model
Parameters
MATH500
AIME25
AIME26
BFCLv3
BFCLv4
Tau² Telecom
Tau² Retail
LFM2.5-8B-A1B
8B/A1B
88.76
42.53
50.00
64.79
49.73
88.07
39.82
Granite-4.0-H-Tiny
7B/A1B
59.20
4.93
3.33
56.89
28.52
16.67
18.42
Qwen3.5-4B
4B
80.76
54.28
58.33
71.06
54.01
87.72
71.93
Qwen3-30B-A3B-Thinking-2507
30.5B/3.3B
86.48
71.67
66.67
73.39
50.53
21.93
56.14
Gemma-4-E2B-IT
5.1B
64.00
26
30
56.44
31.91
22.37
18.95
Gemma-4-E4B-IT
8B
65.00
34.33
40.67
57.31
33.92
26.75
42.11
CPU Inference
image
GPU Inference
LFM2.5-8B-A1B is the fastest model in its size class, reaching 18.5K output tokens per second at high concurrency, over 1.6B tokens per day on a single H100.
If you are interested in custom solutions with edge deployment, please contact our sales team.
Citation
bibtex
1@article{liquidAI20268BA1B,
2 author = {Liquid AI},
3 title = {LFM2.5-8B-A1B: Personal Assistant On Your Laptop},
4 journal = {Liquid AI Blog},
5 year = {2026},
6 note = {www.liquid.ai/blog/lfm2-5-8b-a1b},
7}
The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder
💬 How to test:
Choose an AI assistant type:
TurboLLM (GPT-4.1-mini)
HugLLM (Hugginface Open-source models)
TestLLM (Experimental CPU-only)
What I’m Testing
I’m pushing the limits of small open-source models for AI network monitoring, specifically:
Function calling against live network services
How small can a model go while still handling:
Automated Nmap security scans
Quantum-readiness checks
Network Monitoring tasks
🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):
✅ Zero-configuration setup
⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!
Other Assistants
🟢 TurboLLM – Uses gpt-4.1-mini :
**It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
Create custom cmd processors to run .net code on Quantum Network Monitor Agents
Real-time network diagnostics and monitoring
Security Audits
Penetration testing (Nmap/Metasploit)
🔵 HugLLM – Latest Open-source models:
🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.
💡 Example commands you could test:
"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!
Final Word
I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.
If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.
I'm also open to job opportunities or sponsorship.
