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Model: unsloth/LFM2-350M-GGUF Source: Original Platform
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README.md
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---
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base_model:
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- LiquidAI/LFM2-350M
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library_name: transformers
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license: other
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license_name: lfm1.0
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license_link: LICENSE
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language:
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- en
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||||
- ar
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- zh
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- fr
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- de
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- ja
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- ko
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- es
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pipeline_tag: text-generation
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tags:
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- liquid
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- unsloth
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- lfm2
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- edge
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---
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> [!NOTE]
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> Includes our **chat template fixes**! <br> For `llama.cpp`, use `--jinja`
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>
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<div>
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<p style="margin-top: 0;margin-bottom: 0;">
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<em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
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</p>
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<div style="display: flex; gap: 5px; align-items: center; ">
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<a href="https://github.com/unslothai/unsloth/">
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<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
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</a>
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<a href="https://discord.gg/unsloth">
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<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
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||||
</a>
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||||
<a href="https://docs.unsloth.ai/">
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||||
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
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</a>
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<center>
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<div style="text-align: center;">
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<img
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src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/7_6D7rWrLxp2hb6OHSV1p.png"
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alt="Liquid AI"
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style="width: 100%; max-width: 66%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
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||||
/>
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</div>
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<a href="https://playground.liquid.ai/chat">
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<svg width="114.8" height="20" viewBox="0 0 1300 200" xmlns="http://www.w3.org/2000/svg" role="img" aria-label="Liquid Playground" style="margin-bottom: 1em;">
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|
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<text x="190" y="138" textLength="329" fill="#000">Liquid</text>
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<text x="655" y="148" textLength="619" fill="#000" opacity="0.1">Playground</text>
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</linearGradient>
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</svg>
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</a>
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||||
</center>
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||||
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# LFM2-350M
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LFM2 is a new generation of hybrid models developed by [Liquid AI](https://www.liquid.ai/), specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.
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We're releasing the weights of three post-trained checkpoints with 350M, 700M, and 1.2B parameters. They provide the following key features to create AI-powered edge applications:
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* **Fast training & inference** – LFM2 achieves 3x faster training compared to its previous generation. It also benefits from 2x faster decode and prefill speed on CPU compared to Qwen3.
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* **Best performance** – LFM2 outperforms similarly-sized models across multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities.
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* **New architecture** – LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions.
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* **Flexible deployment** – LFM2 runs efficiently on CPU, GPU, and NPU hardware for flexible deployment on smartphones, laptops, or vehicles.
|
||||
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Find more information about LFM2 in our [blog post](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models).
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## 📄 Model details
|
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|
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Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance.
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They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations.
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However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
|
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||||
| Property | Value |
|
||||
| ------------------- | ----------------------------- |
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| **Parameters** | 354,483,968 |
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| **Layers** | 16 (10 conv + 6 attn) |
|
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| **Context length** | 32,768 tokens |
|
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| **Vocabulary size** | 65,536 |
|
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| **Precision** | bfloat16 |
|
||||
| **Training budget** | 10 trillion tokens |
|
||||
| **License** | LFM Open License v1.0 |
|
||||
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**Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
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**Generation parameters**: We recommend the following parameters:
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||||
* `temperature=0.3`
|
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* `min_p=0.15`
|
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* `repetition_penalty=1.05`
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**Chat template**: LFM2 uses a ChatML-like chat template as follows:
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```
|
||||
<|startoftext|><|im_start|>system
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||||
You are a helpful assistant trained by Liquid AI.<|im_end|>
|
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<|im_start|>user
|
||||
What is C. elegans?<|im_end|>
|
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<|im_start|>assistant
|
||||
It's a tiny nematode that lives in temperate soil environments.<|im_end|>
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```
|
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|
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You can apply it using the dedicated [`.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#applychattemplate) function from Hugging Face transformers.
|
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**Tool use**: It consists of four main steps:
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1. **Function definition**: LFM2 takes JSON function definitions as input (JSON objects between `<|tool_list_start|>` and `<|tool_list_end|>` special tokens), usually in the system prompt
|
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2. **Function call**: LFM2 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer.
|
||||
3. **Function execution**: The function call is executed and the result is returned (string between `<|tool_response_start|>` and `<|tool_response_end|>` special tokens), as a "tool" role.
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4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.
|
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Here is a simple example of a conversation using tool use:
|
||||
|
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```
|
||||
<|startoftext|><|im_start|>system
|
||||
List of tools: <|tool_list_start|>[{"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"]}}]<|tool_list_end|><|im_end|>
|
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<|im_start|>user
|
||||
What is the current status of candidate ID 12345?<|im_end|>
|
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<|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|>
|
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<|im_start|>tool
|
||||
<|tool_response_start|>{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}<|tool_response_end|><|im_end|>
|
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<|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|>
|
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```
|
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|
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**Architecture**: Hybrid model with multiplicative gates and short convolutions: 10 double-gated short-range LIV convolution blocks and 6 grouped query attention (GQA) blocks.
|
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|
||||
**Pre-training mixture**: Approximately 75% English, 20% multilingual, and 5% code data sourced from the web and licensed materials.
|
||||
|
||||
**Training approach**:
|
||||
* Knowledge distillation using [LFM1-7B](https://www.liquid.ai/blog/introducing-lfm-7b-setting-new-standards-for-efficient-language-models) as teacher model
|
||||
* Very large-scale SFT on 50% downstream tasks, 50% general domains
|
||||
* Custom DPO with length normalization and semi-online datasets
|
||||
* Iterative model merging
|
||||
|
||||
## 🏃 How to run LFM2
|
||||
|
||||
To run LFM2, you need to install Hugging Face [`transformers`](https://github.com/huggingface/transformers) from source (v4.54.0.dev0).
|
||||
You can update or install it with the following command: `pip install "transformers @ git+https://github.com/huggingface/transformers.git@main"`.
|
||||
|
||||
Here is an example of how to generate an answer with transformers in Python:
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
# Load model and tokenizer
|
||||
model_id = "LiquidAI/LFM2-350M"
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
device_map="auto",
|
||||
torch_dtype="bfloat16",
|
||||
trust_remote_code=True,
|
||||
# attn_implementation="flash_attention_2" <- uncomment on compatible GPU
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
|
||||
# Generate answer
|
||||
prompt = "What is C. elegans?"
|
||||
input_ids = tokenizer.apply_chat_template(
|
||||
[{"role": "user", "content": prompt}],
|
||||
add_generation_prompt=True,
|
||||
return_tensors="pt",
|
||||
tokenize=True,
|
||||
).to(model.device)
|
||||
|
||||
output = model.generate(
|
||||
input_ids,
|
||||
do_sample=True,
|
||||
temperature=0.3,
|
||||
min_p=0.15,
|
||||
repetition_penalty=1.05,
|
||||
max_new_tokens=512,
|
||||
)
|
||||
|
||||
print(tokenizer.decode(output[0], skip_special_tokens=False))
|
||||
|
||||
# <|startoftext|><|im_start|>user
|
||||
# What is C. elegans?<|im_end|>
|
||||
# <|im_start|>assistant
|
||||
# C. elegans, also known as Caenorhabditis elegans, is a small, free-living
|
||||
# nematode worm (roundworm) that belongs to the phylum Nematoda.
|
||||
```
|
||||
|
||||
You can directly run and test the model with this [Colab notebook](https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing).
|
||||
|
||||
## 🔧 How to fine-tune LFM2
|
||||
|
||||
We recommend fine-tuning LFM2 models on your use cases to maximize performance.
|
||||
|
||||
| Notebook | Description | Link |
|
||||
|-------|------|------|
|
||||
| SFT + LoRA | Supervised Fine-Tuning (SFT) notebook with a LoRA adapter in TRL. | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="120" alt="Colab link"></a> |
|
||||
| DPO | Preference alignment with Direct Preference Optimization (DPO) in TRL. | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="120" alt="Colab link"></a> |
|
||||
|
||||
## 📈 Performance
|
||||
|
||||
LFM2 outperforms similar-sized models across different evaluation categories.
|
||||
|
||||
### 1. Automated benchmarks
|
||||
|
||||
|
||||
|
||||
| Model | MMLU | GPQA | IFEval | IFBench | GSM8K | MGSM | MMMLU |
|
||||
|-------|------|------|--------|---------|-------|------|-------|
|
||||
| LFM2-350M | 43.43 | 27.46 | 65.12 | 16.41 | 30.1 | 29.52 | 37.99 |
|
||||
| LFM2-700M | 49.9 | 28.48 | 72.23 | 20.56 | 46.4 | 45.36 | 43.28 |
|
||||
| LFM2-1.2B | *55.23* | **31.47** | **74.89** | *20.7* | *58.3* | *55.04* | **46.73** |
|
||||
| Qwen3-0.6B | 44.93 | 22.14 | 64.24 | 19.75 | 36.47 | 41.28 | 30.84 |
|
||||
| Qwen3-1.7B | **59.11** | 27.72 | *73.98* | **21.27** | 51.4 | **66.56** | *46.51* |
|
||||
| Llama-3.2-1B-Instruct | 46.6 | *28.84* | 52.39 | 16.86 | 35.71 | 29.12 | 38.15 |
|
||||
| gemma-3-1b-it | 40.08 | 21.07 | 62.9 | 17.72 | **59.59** | 43.6 | 34.43 |
|
||||
|
||||
### 2. LLM-as-a-Judge
|
||||
|
||||
|
||||
|
||||
|
||||
### 3. Inference
|
||||
|
||||
#### Throughput comparison on CPU in ExecuTorch
|
||||
|
||||
|
||||
|
||||
#### Throughput comparison on CPU in Llama.cpp
|
||||
|
||||
|
||||
|
||||
## 📬 Contact
|
||||
|
||||
If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
|
||||
Reference in New Issue
Block a user