---
library_name: transformers
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
- ar
- zh
- fr
- de
- ja
- ko
- es
- pt
- it
pipeline_tag: text-generation
tags:
- liquid
- lfm2.5
- edge
base_model: LiquidAI/LFM2.5-8B-A1B-Base
---
# LFM2.5-8B-A1B
> [!IMPORTANT]
> **⚠️Important:**
> The tokenizer was updated after the original release to fix tool-calling issues in `llama.cpp`.
> If you downloaded LFM2.5-8B-A1B before [commit `feb5e04`](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B/commit/feb5e04da4910dd56d33f0cd03747ff298c1e801), please re-download the tokenizer files.
> The [GGUF files](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-GGUF) have also been re-converted with the updated tokenizer.
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](https://www.liquid.ai/blog/lfm2-5-8b-a1b).
**AA-Omniscience Index (higher is better) rewards correct answers and penalizes hallucinations. Scores range from -100 to 100. See more results on [Artificial Analysis](https://artificialanalysis.ai/evaluations/omniscience).*
## 🗒️ Model Details
| Model | Parameters | Description |
| --- | --- | --- |
| [LFM2.5-8B-A1B-Base](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base) | 8.3B total / 1.5B active | Pre-trained base model for fine-tuning |
| [**LFM2.5-8B-A1B**](https://huggingface.co/LiquidAI/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**](https://huggingface.co/LiquidAI/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](https://huggingface.co/LiquidAI/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](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-ONNX) | ONNX Runtime format for cross-platform deployment. |
| [LFM2.5-8B-A1B-MLX](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-MLX-8bit) | 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](https://docs.liquid.ai/lfm/key-concepts/chat-template) 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()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) 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()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) 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](https://docs.liquid.ai/lfm/key-concepts/tool-use) 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](https://docs.liquid.ai/lfm/inference/transformers) for the full list.
| Name | Description | Docs | Notebook |
|------|-------------|------|:--------:|
| [Transformers](https://github.com/huggingface/transformers) | Simple inference with direct access to model internals. | Link | 
|
| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | Link | |
| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | Link | |
| [MLX](https://github.com/ml-explore/mlx) | Apple's machine learning framework optimized for Apple Silicon. | Link | — |
| [LM Studio](https://lmstudio.ai/) | Desktop application for running LLMs locally. | Link | — |
Quick start with Transformers (compatible with `transformers>=5.0.0`):
```python
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](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for text completion. | Link | |
| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for translation. | Link | |
| SFT ([Unsloth](https://github.com/unslothai/unsloth)) | Supervised Fine-Tuning with LoRA using Unsloth. | Link | |
| SFT ([TRL](https://github.com/huggingface/trl)) | Supervised Fine-Tuning with LoRA using TRL. | Link | |
| DPO ([TRL](https://github.com/huggingface/trl)) | Direct Preference Optimization with LoRA using TRL. | Link | |
| GRPO ([Unsloth](https://github.com/unslothai/unsloth)) | GRPO with LoRA using Unsloth. | Link | |
| GRPO ([TRL](https://github.com/huggingface/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](https://discord.com/invite/liquid-ai).
- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
## Citation
```bibtex
@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},
}
```
```bibtex
@article{liquidai2025lfm2,
title = {LFM2 Technical Report},
author = {Liquid AI},
journal = {arXiv preprint arXiv:2511.23404},
year = {2025}
}
```