# LFM2.5 230M

URL: https://interfaze.ai/models/liquidailfm25-230m

[All models](https://interfaze.ai/models)

LFM2.5 230M by LiquidAI, a text-generation model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | LFM2.5 230M | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | 10 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 32.8K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | LFM2.5 230M | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/LiquidAI/LFM2.5-230M)

LFM2.5 is a family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.

-   **Our most compact model yet**: 230M parameters that punch above their weight, bringing real capability to the tightest memory and compute budgets.
-   **Fast edge inference**: Best throughput from low-cost CPUs to production GPUs, running at 213 tok/s decode speed on Galaxy S25 Ultra and 42 tok/s on a Raspberry Pi 5.
-   **Built for agentic tasks**: Distilled from LFM2.5-350M and refined with multi-stage reinforcement learning, making it well-suited for tool use and data extraction.

Find more information about LFM2.5-230M in our [blog post](https://www.liquid.ai/blog/lfm2-5-230m).

![lfm2\_5\_230m\_benchmarks](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/4UpNxlgfKjfgT5ByIVph0.png)

## 🗒️ Model Details

| Model | Parameters | Description |
| --- | --- | --- |
| LFM2.5-230M-Base | 230M | Pre-trained base model for fine-tuning |
| LFM2.5-230M | 230M | General-purpose instruction-tuned model |

LFM2.5-230M is a general-purpose text-only model with the following features:

-   **Number of parameters**: 230M
-   **Number of layers**: 14 (8 double-gated LIV convolution blocks + 6 GQA blocks)
-   **Training budget**: 19T tokens
-   **Context length**: 32,768 tokens
-   **Vocabulary size**: 65,536
-   **Knowledge cutoff**: Mid-2024
-   **Languages**: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish
-   **Generation parameters**:
    -   `temperature: 0.1`
    -   `top_k: 50`
    -   `repetition_penalty: 1.05`

| Model | Description |
| --- | --- |
| LFM2.5-230M | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang. |
| LFM2.5-230M-GGUF | Quantized format for llama.cpp and compatible tools. Optimized for edge inference and local deployment. |
| LFM2.5-230M-ONNX | ONNX Runtime format for cross-platform deployment. |
| LFM2.5-230M-MLX | MLX format for Apple Silicon. Optimized for fast inference on Mac devices. |

We recommend using it for data extraction and lightweight on-device agentic pipelines. It is not recommended for reasoning-heavy workloads such as advanced math, code generation, or creative writing.

### 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`

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 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 | 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 | — |
| SGLang | High-throughput production deployments with GPU. | Link | \- |

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

```
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "LiquidAI/LFM2.5-230M"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",

)
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=0.1,
    top_k=50,
    repetition_penalty=1.05,
    max_new_tokens=512,
    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

### Benchmarks

| Model | GPQA Diamond | MMLU-Pro | IFEval | IFBench | Multi-IF |
| --- | --- | --- | --- | --- | --- |
| LFM2.5-230M | 25.41 | 20.25 | 71.71 | 38.40 | 37.70 |
| LFM2.5-350M | 30.64 | 20.01 | 76.96 | 40.69 | 44.92 |
| LFM2-350M | 27.58 | 19.29 | 64.96 | 18.20 | 32.92 |
| Granite 4.0-H-350M | 22.32 | 13.14 | 61.27 | 17.22 | 28.70 |
| Granite 4.0-350M | 25.91 | 12.84 | 53.48 | 15.98 | 24.21 |
| Qwen3.5-0.8B (Instruct) | 27.41 | 37.42 | 59.94 | 22.87 | 41.68 |
| Gemma 3 1B IT | 23.89 | 14.04 | 63.49 | 20.33 | 44.25 |

| Model | CaseReportBench | BFCLv3 | BFCLv4 | τ²-Bench Telecom | τ²-Bench Retail |
| --- | --- | --- | --- | --- | --- |
| LFM2.5-230M | 22.51 | 43.26 | 21.03 | 5.26 | 13.68 |
| LFM2.5-350M | 32.45 | 44.11 | 21.86 | 18.86 | 17.84 |
| LFM2-350M | 11.67 | 22.95 | 12.29 | 10.82 | 5.56 |
| Granite 4.0-H-350M | 12.44 | 43.07 | 13.28 | 13.74 | 6.14 |
| Granite 4.0-350M | 0.84 | 39.58 | 13.73 | 2.92 | 6.14 |
| Qwen3.5-0.8B (Instruct) | 13.83 | 35.08 | 18.70 | 12.57 | 6.14 |
| Gemma 3 1B IT | 2.28 | 16.61 | 7.17 | 9.36 | 6.43 |

### CPU Inference

![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TCR-MfPtX3YTPvRzxWcG3.png)

### GPU Inference

![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/emlcz4gf2wendPhKQWEBN.png)

## 📬 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

```
@article{liquidAI2026230M,
  author = {Liquid AI},
  title = {LFM2.5-230M: Built to Run Anywhere},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2-5-230m},
}
```

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

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