library_name, license, license_name, license_link, language, pipeline_tag, tags, base_model

library_name	license	license_name	license_link	language	pipeline_tag	tags	base_model
transformers	other	lfm1.0	LICENSE	en ar zh fr de ja ko es pt	text-generation	liquid lfm2.5 edge	LiquidAI/LFM2.5-8B-A1B-Base

LFM2.5-8B-A1B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 94a220cd6.

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Click here to get info on choosing the right GGUF model format

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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

Model	Parameters	Description
LFM2.5-8B-A1B-Base	8.3B total / 1.5B active	Pre-trained base model for fine-tuning
LFM2.5-8B-A1B	8.3B total / 1.5B active	Reasoning-tuned general-purpose model

LFM2.5-8B-A1B is a general-purpose text-only model with the following features:

• Total parameters: 8.3B
• Active parameters: 1.5B
• Number of layers: 24 (18 double-gated LIV conv + 6 GQA)
• Training budget: 38 trillion tokens
• Context length: 128,000
• Vocabulary size: 128,000
• Languages: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish
• Generation parameters: We recommend the following parameters:
  • temperature: 0.2
  • top_k: 80
  • repetition_penalty: 1.05

Model	Description
LFM2.5-8B-A1B	Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang.
LFM2.5-8B-A1B-GGUF	Quantized format for llama.cpp and compatible tools. Optimized for edge inference and local deployment.
LFM2.5-8B-A1B-ONNX	ONNX Runtime format for cross-platform deployment.
LFM2.5-8B-A1B-MLX	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.

Chat Template

LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details. Example:

<|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:

1. Function definition: Provide the list of tools as a JSON object in the system prompt, or use tokenizer.apply_chat_template() with tools=....
2. 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.
3. Function execution: Execute the call and return the result with the tool role.
4. Final answer: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt.

See the Tool Use documentation for the full guide. Example:

<|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.

Name	Description	Docs	Notebook
Transformers	Simple inference with direct access to model internals.	Link
vLLM	High-throughput production deployments with GPU.	Link
llama.cpp	Cross-platform inference with CPU offloading.	Link
MLX	Apple's machine learning framework optimized for Apple Silicon.	Link	—
LM Studio	Desktop application for running LLMs locally.	Link	—

Quick start with Transformers (compatible with transformers>=5.0.0):

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-8B-A1B"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
#   attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "What is C. elegans?"

input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
)["input_ids"].to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.2,
    top_k=80,
    repetition_penalty=1.05,
    max_new_tokens=8192,
    streamer=streamer,
)

🔧 Fine-Tuning

We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.

Name	Description	Docs	Notebook
CPT (Unsloth)	Continued Pre-Training using Unsloth for text completion.	Link
CPT (Unsloth)	Continued Pre-Training using Unsloth for translation.	Link
SFT (Unsloth)	Supervised Fine-Tuning with LoRA using Unsloth.	Link
SFT (TRL)	Supervised Fine-Tuning with LoRA using TRL.	Link
DPO (TRL)	Direct Preference Optimization with LoRA using TRL.	Link
GRPO (Unsloth)	GRPO with LoRA using Unsloth.	Link
GRPO (TRL)	GRPO with LoRA using TRL.	Link

📊 Performance

Improvements over LFM2-8B-A1B

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

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.

📬 Contact

• Got questions or want to connect? Join our Discord community.
• If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

@article{liquidAI20268BA1B,
  author  = {Liquid AI},
  title   = {LFM2.5-8B-A1B: Personal Assistant On Your Laptop},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-8b-a1b},
}

@article{liquidai2025lfm2,
  title   = {LFM2 Technical Report},
  author  = {Liquid AI},
  journal = {arXiv preprint arXiv:2511.23404},
  year    = {2025}
}

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

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:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"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.

Thank you! 😊